Participants

Mr. kathiresan sivakumar

Phd Student | nazarbayev university

Abstract: Synchronization of time-delay dynamical networks via Secure Intermittent Control with Hybrid Impulses under Dual-Channel Attacks

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Synchronization of time-delay dynamical networks via Secure Intermittent Control with Hybrid Impulses under Dual-Channel Attacks

Kathiresan sivakumar, Nazarbayev university, kathiresan.sivakumar@nu.edu.kz

Rakkiyappan Rajan, Bharathiar university, rakkigru@gmail.com

Ardak kashkynbayev, Nazarbayev university, ardak.kashkynbayev@nu.edu.kz

Abstract

This paper investigates the synchronization of time-delay dynamical networks under a secure aperiodically intermittent control (APIC) framework with hybrid delayed impulses in the presence of dual-channel cyber attacks. Unlike conventional schemes, the proposed APIC strategy activates continuous control only during ON intervals, while impulses are triggered during OFF intervals to maintain synchronization. A dual-channel attack model is considered, where edge-based deception corrupts the sensor–controller channel and denial-of-service (DoS) attacks block impulsive actions on the controller–actuator channel. To enhance resilience, a secure impulsive control protocol is designed so that impulses are executed only in secure OFF intervals, thereby saving energy and reducing vulnerability to attacks. By employing inequality-based analysis and introducing the notions of average impulsive interval (AII), average impulsive delay (AID), and average impulsive effect (AIE), new linear matrix inequality (LMI) criteria are established to ensure synchronization. The proposed APIC-based protocol effectively counteracts the dual adverse effects introduced by dual-channel cyber attacks and hybrid impulsive delays, thereby ensuring secure synchronization of time-delay dynamical networks. Numerical simulations are conducted to illustrate the effectiveness and practical applicability of the proposed theoretical results.

Keywords: Time-delay neural networksSynchronizationAperiodically intermittent control (APIC)Hybrid delayed impulsesDual-channel cyber attacks

Mr. Arun Kumar M

PG Student | Vellore Institute of Technology, Chennai

Abstract: Classification of Martian Surface Features: CNN Architecture Comparison with GLCM-Class Weight Hybridization and Dual-Threshold

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Classification of Martian Surface Features: CNN Architecture Comparison with GLCM-Class Weight Hybridization and Dual-Threshold

Arun Kumar M, Vellore Institute of Technology, Chennai, arunkumar.m2024a@vitstudent.ac.in

Anandha Bhavatarini S, Vellore Institute of Technology, Chennai, anandha.2024@vitstudent.ac.in

Yuvaraj A, Vellore Institute of Technology, Chennai, yuvaraj.2024@vitstudent.ac.in

Dr Hanna Grace G, Vellore Institute of Technology, Chennai, hannahgrace.g@vitstudent.ac.in

Abstract

The automated interpretation of Mars orbital imagery is essential for advancing planetary surface studies. This research evaluates three Convolutional Neural Network (CNN) architectures VGG19, ResNet152V2, and InceptionV3 for the classification of Mars surface features from HiRISE images. The classification task encompassed seven categories, including craters, bright and dark dunes, slope streaks, impact ejecta, swiss cheese and spider formations. To improve model reliability, a hybrid preprocessing pipeline was designed by combining Gray Level Co-occurrence Matrix (GLCM) texture descriptors with class weight balancing, thereby addressing both textural differentiation and class imbalance. Furthermore, a dual-threshold uncertainty detection mechanism, incorporating prediction confidence and entropy, was implemented to identify ambiguous cases. Results indicate that ResNet152V2 achieved the strongest overall performance using standard preprocessing, while InceptionV3 exhibited consistent behaviour when class weighting was applied. The comparative assessment contributes to a better understanding of deep learning architectures for planetary surface analysis and establishes a methodological basis for future automated classification systems targeting Martian orbital data.

Keywords: Mars surface classificationConvolutional Neural NetworksHiRISE imageryGLCM texture analysisClass imbalanceHybrid preprocessingDual-threshold frameworkDeep learningMartian orbital images

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Ms. Haripriya S

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: A Multi-Host Compartmental Model of Scrub Typhus Dynamics with Comorbidity and Intervention Strategies

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A Multi-Host Compartmental Model of Scrub Typhus Dynamics with Comorbidity and Intervention Strategies

S. Haripriya, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Chennai-600 127, Tamil Nadu, India, haripriya.s2022@vitstudent.ac.in

Mini Ghosh, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Chennai-600 127, Tamil Nadu, India, minighosh@vit.ac.in

Abstract

Scrub typhus is a severe infectious disease caused by the rickettsial bacterium Orientia tsutsugamushi. The disease is transmitted to humans through the bite of infected larval mites (chiggers) with rodents serving as reservoir hosts and humans as accidental hosts. Most cases occur within the Tsutsugamushi Triangle which including parts of East and Southeast Asia with nearly one million people affected globally each year. Scrub typhus can cause severe complications particularly in patients with comorbidities who face a heightened risk of mortality remains an independent risk factor for the development of autoimmune diseases and also untreated cases may reach fatality rates of $30\%$ or higher although timely antibiotic treatment is generally effective. To address these complexities, we developed a compartmental multi-host model incorporating humans, mites and reservoir hosts with disease states reflecting clinical heterogeneity including mild infection, comorbidity and treatment effects. We establish positivity and boundedness, derive the basic reproduction number $R_0$ and analyze stability and sensitivity to identify key parameters. Using South Korea case data, we perform parameter estimation and data fitting to validate the model. We further explore climatic factors - temperature, rainfall and humidity as potential drivers of scrub typhus seasonality. Finally, we evaluate optimal control strategies including treatment and vector reduction to assess their effectiveness in reducing disease burden.

Keywords: Scrub typhusMulti-host modelComorbiditySeasonalityMathematical modelingStability analysisSensitivity analysisOptimal control

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. Sashmita Sahoo

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Time and space depended spectral approximation of the Hunter–Saxton equation

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. J. Merlin

Research scholar | Gandhigram Rural Institute, Dindigul, Tamilnadu, India

Abstract: Quantifying Artificial and Natural Fractals: A Python-Based Analysis of Fractal Dimension in L-Systems and Foliage

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Quantifying Artificial and Natural Fractals: A Python-Based Analysis of Fractal Dimension in L-Systems and Foliage

R. Uthayakumar, Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamilnadu, India, uthayagri@gmail.com

J. Merlin, Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamilnadu, India, merlin4math@gmail.com

Abstract

Fractal Dimension (FD) is a powerful metric widely used to quantify the structural complexity of biological forms, including leaves. However, a comprehensive comparison of the accuracy and applicability of various FD estimation techniques across both digitally generated and natural botanical forms remains an area requiring further investigation. This plan aims to address this gap by employing a dual approach: generating artificial leaf structures using computational methods and comparing their fractal dimensions to those of a variety of natural leaves. Multiple established FD estimation techniques will be applied to both datasets. The artificially generated forms provide a controlled benchmark with definable structural parameters, while the natural specimens offer authentic biological complexity. We anticipate that this comparative analysis will identify which fractal dimension approaches are most effective at capturing morphological complexity and evaluate how well computational models can emulate the fractal properties of real leaves. This work has significant implications for fields including biomathematics, computational botony, digital biomimicry, and ecological modeling, as it establish a rigorous methodological framework for the fractal-based comparison of artificial and natural leaves.

Keywords: L- systemsfoliagefractal dimensionbiological complexitymorphological complexitybiomathematicscomputational botonyartificial leavesnatural leaves

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. Ashika T

Research Associate | Vellore Institute of Technology Chennai

Abstract: Fault Tolerant Rough Set Theory

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Fault Tolerant Rough Set Theory

Ashika T, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai, India, ashika.t2023@vitstudent.ac.in

G. Hannah Grace, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai, India, hannahgrace.g@vit.ac.in

Abstract

Rough Set Theory (RST) has been widely employed in data analysis, feature selection and knowledge discovery owing to its capability to approximate vague and uncertain concepts. Despite its strengths, classical RST relies on strict indiscernibility and complete information, which limits its effectiveness in the presence of noise, inconsistencies or missing values. Extensions such as Tolerance Rough Sets (TRS) and Variable Precision Rough Sets (VPRS) have partially addressed these issues, yet they remain constrained to specific forms of tolerance. To overcome these limitations, this study proposes the Fault-Tolerant Rough Set (FTRS) model, a unified framework that integrates multiple tolerance mechanisms into RST approximations. The model introduces three parameters namely attribute-level tolerance $(\epsilon)$ for relaxed similarity, label-level tolerance $(\delta)$ for controlled misclassification and missingness tolerance $(\mu)$ for incomplete data. With these extensions, FTRS encompasses RST, TRS and VPRS as special cases, thereby offering robust uncertainty management. Formal definitions, theoretical properties and an example highlight its improved classification reliability. FTRS advances RST applications in noisy, incomplete datasets like medical and sensor data.

Keywords: Incomplete DataRough set theoryFault toleranceSimilarityUncertainty Handling

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Mr. FRIDOLIN IMMANUEL L

Research Scholar | SRM Institute of Science and Technology, Kattankulathur

Ms. NEHA ABHILASH

Research Scholar | SRM Institute of Science and Technology,Kattankulathur

Mr. Ayush Goyal

UG Student | Northumbria University ,Newcastle, UK

Abstract: Comparative Study of Graph Neural Networks for Predicting Developmental Toxicity Using Molecular Graphs

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Comparative Study of Graph Neural Networks for Predicting Developmental Toxicity Using Molecular Graphs

Ayush Goyal, School of Computer Science, Northumbria University ,Newcastle, UK, ayush2.goyal@northumbria.ac.uk

Monnishkaran Madheswaran, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu 600 127, India, monnishkaran.m2022@vitstudent.ac.in

Keerthana Jaganathan, School of Computer Science, Northumbria University ,Newcastle, UK, keerthana.jaganathan@northumbria.ac.uk

Lakshmanan Shanmugam, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu 600 127, India, lakshmanan.s@vit.ac.in

Abstract

Predicting developmental toxicity is crucial because exposure to harmful chemicals during pregnancy can interfere with foetal growth, long-term developmental disorders and serious risks to public health. Traditional animal testing is still widely used, but its role is gradually being reduced since it is time-consuming, expensive, and comes with strong ethical concerns. Researchers are now turning more towards computer-aided techniques, as they are easier to reproduce and less costly than traditional methods. This work employed a dataset of 234 experimentally corroborated chemical compounds and their toxicity conclusions. Each compound was acquired as a molecular graph from SMILES strings to safeguard atom- and bond-level details. The dataset was divided into training and testing sets, and its performance was further assessed through stratified 5-fold cross-validation to improve generalisation. To assess the generalisation capabilities by doing external validation, 3 state-of-the-art graph neural network models were taken: GINE with edge features, Message Passing Neural Network (MPNN), and Graph Attention Network (GAT). Experimental results indicated that MPNN exhibited the highest performance and reliable predictive results. GAT models performed well in precision, but recall was comparatively lower than in other models. This study demonstrates the potential of GNN-based models and their explainable components to ease predictive modelling for developmental toxicity, presenting notable benefits for drug development, regulatory chemical safety evaluation, toxicology studies on reducing animal testing, and the evaluation of developmental chemical hazards.

Keywords: Developmental ToxicityGraph Neural Networks (GNNs)Graph Isomorphism Network (GINE)Message Passing Neural Network (MPNN)Graph Attention Network (GAT)

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. LAKSHANA S

Research Associate | Vellore Institute of Technology, Chennai, India

Ms. SUPRAJA S

Research Associate | Vellore Institute of Technology, Chennai, India

Ms. AMITHA ACCA DANIEL

Research Scholar | SRM INSTITUTE OF SCIENCE AND TECHNOLOGY,KATTANKULATHUR

Ms. AMITHA ACCA DANIEL

Research Scholar | SRM INSTITUTE OF SCIENCE AND TECHNOLOGY,KATTANKULATHUR

Ms. Mayuri Santoshrao Bhokare

Research Associate | Vellore Institue of Technology

Mr. Abishek R

Research Scholar | VIT Chennai

Ms. PERIYANAYAGA CLARISHA S

Research Scholar | VIT Chennai

Ms. JAJEETHA T

Research Associate | Vellore Institute of Technology, Chennai

Mr. Francis A

Research scholar | VIT University

Ms. Suba S

Research Scholar | Vellore Institute of Technology ,Chennai.

Mr. Monnishkaran M

Research Scholar | Vellore Institute of Technology, Chennai

Ms. Aardra Mohan

Research Scholar | Vellore Institute of Technology, Chennai

Ms. Karpagavalli S

Research Scholar | Vellore Institute of Technology, Chennai

Ms. Jayabharathi J

Research Scholar | Vellore Institute of Technology, Chennai

Mr. Ayyappan N

Project Associate | Vellore Institute of Technology, Chennai

Ms. Priyadharshini P L

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Hexic Fuzzy Set Approach to handle uncertainty in Multi-Criteria Decision Making Problem

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Ms. Carlyn Dorcas S

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Pythagorean Fuzzy Approach for Transportation Problem Using Parametric Representation

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Mr. A. Karthik

project research assistant | Vellore Institute of Technology - Chennai

Abstract: A mathematical model of chikungunya transmission with media awareness and the application of optimal control theory

Abstract Preview

A mathematical model of chikungunya transmission with media awareness and the application of optimal control theory

A. Karthik, School of Advanced Sciences, Vellore Institute of Technology, Chennai-600 127, Tamil Nadu, India, karthik.2021a@vitstudent.ac.in

Mini Ghosh, School of Advanced Sciences, Vellore Institute of Technology, Chennai-600 127, Tamil Nadu, India, minighosh@vit.ac.in

Abstract

A compartmental mathematical model was developed to examine the transmission dynamics of chikungunya, integrating media awareness and optimal control strategies. Both asymptomatic and symptomatic infectious categories, along with a media-induced awareness component, were incorporated to more precisely represent epidemiological dynamics. The model's positivity, boundedness, and stability characteristics were confirmed, and the basic reproduction number was calculated using the next-generation matrix method. Model calibration was conducted utilizing cumulative case data from India and high-burden states (Gujarat, Karnataka, and Maharashtra) through maximum likelihood estimation. Sensitivity analysis identified transmission-related parameters, specifically the mosquito biting rate and infection probabilities, as the most significant determinants of epidemic persistence. The system's threshold behavior was further demonstrated through contour plots of $R_0$ with respect to different pairs of parameters and variations of parameters. A comprehensive control framework incorporating two time-dependent variables—minimization of human–mosquito interaction and augmentation of treatment—was formulated and examined utilizing Pontryagin’s Maximum Principle. The optimal control result indicates that the simultaneous application of both controls resulted in the most significant decrease in symptomatic infections, while individual interventions also produced quantifiable advantages. The results emphasize the necessity of incorporating preventive measures, awareness enhancement, and improved treatment into region-specific chikungunya management strategies.

Keywords: Chikungunya diseasenonlinear incidence ratebasic reproduction numberLyapunov functionstability analysisOptimal control

Panel Details:

Day: 1

Session No: 1

Panel No: 4

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 709

Dr. J. Jansi Rani

Assistant Professor | SRM Institute of Science and Technology, Ramapuram, Chennai

Abstract: Comparative Study of Ranking Techniques in Solving Pythagorean Fuzzy Transportation Models

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Ms. Lavanya S

Research Scholar | SRM Institute of Science and Technology, Ramapuram

Abstract: A Fractional-Order Tumor–Immune Model for Lung Cancer with Combined Therapeutic Strategies

Abstract Preview

A Fractional-Order Tumor–Immune Model for Lung Cancer with Combined Therapeutic Strategies

Lavanya S, Department of Mathematics, SRM Institute of Science and Technology, Ramapuram, ls6843@srmist.edu.in

Srinivasan R, Department of Mathematics, SRM Institute of Science and Technology, Ramapuram, srinivar1@srmist.edu.in

Abstract

This work presents a fractional-order mathematical model for lung cancer that integrates surgery, immunotherapy, and chemotherapy as a combined treatment strategy. The model describes tumor–immune dynamics under multiple interventions and incorporates a feedback control framework to optimize treatment scheduling. Using the Pontryagin Maximum Principle, necessary conditions for optimal control are derived to minimize tumor burden while reducing treatment intensity. Analytical results show that the basic reproduction number $R_0$ declines substantially with triple therapy, reflecting its effectiveness in limiting tumor proliferation. Sensitivity analysis identifies chemotherapy efficacy and immune activation as key parameters influencing long-term outcomes. Numerical simulations confirm that the integrated approach yields superior tumor suppression compared to single or dual therapies, with improved immune response and sustained stability. This study highlights the potential of combining fractional calculus, multi-modal therapies, and control theory to design personalized, effective treatment protocols for lung cancer patients.

Keywords: Lung cancer modellingFractional-order differential equationsPontryagin’s Maximum PrincipleImmunotherapyOptimal control

Panel Details:

Day: 1

Session No: 1

Panel No: 16

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Moumitha K

UG Student | PSG College of Technology

Abstract: EEG-Based Imagined Speech Classification Using Privacy-Preserving Distributed Learning Approaches

Abstract Preview

EEG-Based Imagined Speech Classification Using Privacy-Preserving Distributed Learning Approaches

Vaishnavi. K, PSG College of Technology, kvs.cse@psgtech.ac.in

G. Sudha Sadasivam, PSG College of Technology, hod.cse@psgtech.ac.in

Moumitha. K, PSG College of Technology, 22z241@psgtech.ac.in

Sruthi. S, PSG College of Technology, 22z264@psgtech.ac.in

Durga. G, PSG College of Technology, 22z321@psgtech.ac.in

Darshana. S. M, PSG College of Technology, 22z315@psgtech.ac.in

Abstract

The classification of imagined speech using electroencephalography (EEG), has significant applications in Brain-Computer Interfaces (BCIs), especially for people with motor disabilities, enabling communication through imagined speech. However, centralized training methods for these types of data raise serious concerns about data privacy and scalability. This paper presents two separate privacy-preserving solutions to address this problem: Federated Learning and Split Learning. Both approaches employ the Extra Trees Classifier, a robust ensemble model suitable for high-dimensional EEG data. In the federated learning setup, local clients train individually on their private data and only share important features with a central server, which aggregates to update the global model. In split learning, various clients compute partial feature sets and transmit only transformed embeddings to the server, which performs final classification. The EEG dataset used consists of EEG signals of imagined speech of 8 words from 11 participants recorded using eight electrodes. Results show that both methods yield competitive accuracy while preserving privacy—reaching up to $71\%$ in federated learning and $72\%$ with subject-specific split learning. These approaches facilitate learning in edge devices and also preserve data privacy. This study compares and documents the effectiveness of both methods in imagined speech classification and discusses their trade-offs in terms of accuracy, privacy, and computational load.

Keywords: EEGImagined SpeechExtra Trees ClassifierFederated LearningSplit LearningBrain-Computer Interface

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. JOSELIN INFANTA J

RESEARCH SCHOLAR | VIT CHENNAI

Ms. Saranya A

Research Scholar | Vellore Institute of Technology, Vellore, India.

Abstract: Explainable Myocardial Infarction Classification using Testing with Concept Activation Vectors: Temporal Concept- Based Interpretability for ECG Models

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. J. SUGANTHI

Research Scholar | VIT, Chennai

Abstract: On the Application of Haar Wavelet Method for Second-Order initial value Problems in Ordinary Differential Equations: A Comparison with Falkner-Type Method

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Mr. P.R.SAKTHIVEL

Research Scholar | Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu-603203, India.

Abstract: A analysis of non-Newtonian MHD fluid flow in the inclined channel along with permeability, pressure, and magnetic field by Python IDE

Panel Details:

Day: 1

Session No: 1

Panel No: 12

Time: 3:45 PM - 6:00 PM

Venue: N/A

Dr. J Sasikumar

Assistant Professor (S.G) | SRM Institute of Science and Technology

Ms. Raga R

Research Scholar | Vellore Institute of Technology, Chennai

Ms. Haripriya K

Research scholar | Vellore Institute of Technology, Chennai

Ms. Karthika M

Research Scholar | Bannari Amman Institute of Technology

Abstract: A Neutrosophic Hypersoft WASPAS-Entropy Model for Multi-Condition Crop Suitability in Tamilnadu

Abstract Preview

A Neutrosophic Hypersoft WASPAS-Entropy Model for Multi-Condition Crop Suitability in Tamilnadu

M. Parimala, Bannari Amman Institute of Technology, rishwanthpari@gmail.com

M. Karthika, Bannari Amman Institute of Technology, karthikamuthusamy1991@gmail.com

Abstract

Agriculture plays a crucial role in the GDP growth of every country, supporting a wide range of industries from small-scale sectors to large-scale operations such as food processing, handicrafts, and household goods manufacturing. One of the major challenges in Indian agriculture is identifying the most suitable crops for all seasons, particularly given the diverse soil types and fluctuating climatic conditions in Tamil Nadu, which range from low rainfall to severe droughts. To address this issue, we propose an extended WASPAS (Weighted Aggregated Sum Product Assessment) method based on the neutrosophic hypersoft set (NHSS) framework. Unlike the traditional neutrosophic soft set, which only considers main criteria (attributes), the NHSS takes into account subcriteria or attribute values, offering a more granular and accurate decision-making process. In this paper, we introduce a neutrosophic hypersoft WASPAS approach incorporating entropy-based weights to determine the most suitable crops for summer, winter, and monsoon seasons across three soil types, considering both heavy rainfall and drought tolerance. A sensitivity analysis is conducted to assess the robustness of the results, followed by a comparative analysis that highlights the strengths and limitations of the proposed method.

Keywords: Neutrosophic setWASPAS methodNeutrosophic hypersoft setOPtimization techniquesCrop selection

Panel Details:

Day: 1

Session No: 1

Panel No: 14

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. Bharani Dharan K

Assistant Professor Junior | Vellore Institute of Technology, Chennai

Mr. Ragavendirane M.S

Assistant Professor Junior Grade 2 | Vellore Institute of Technology, Chennai 600127.

Abstract: Multi-Exposure Image Fusion Utilizing HSV, YUV Color Models in a Quintic Fuzzy Environment

Abstract Preview

Multi-Exposure Image Fusion Utilizing HSV, YUV Color Models in a Quintic Fuzzy Environment

Ragavendirane M.S, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, ragavendirane.ms@vit.ac.in

Dhanasekar .S, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, dhanasekar.sundaram@vit.ac.in

Reegan Jebadass .J, Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai, reeganmail@gmail.com

Abstract

Multi-exposure image fusion (MEIF) is a process of merging images with different exposure levels of the same scenario into a single informative image for better visualization and analysis. Conventional MEIF methods often lack the ability to handle uncertainty between the pixels due to a wide range of illumination that varies under certain circumstances, which causes limited adaptability for human observations. To address these kinds of limitations, this study presents a Quintic fuzzy set (QFS)-based MEIF model that incorporates HSV and YUV color models. QFS effectively manages the inherent imprecision of pixel regions across various exposure levels by assigning membership degrees from a higher-order perspective. Whereas, HSV and YUV models are effectively employed to preserve the perceptual attributes for the fusion of dynamic image sets. The proposed method induces a smoother transition between the pixel regions of different images of the same scene by improving robustness against noise and enhancing visual quality. Experimental results demonstrate that QFS-based MEIF outperforms existing approaches in terms of detail preservation and reduced fusion artifacts via some standard image quality analysis.

Keywords: Multi-exposure image fusionQuintic Fuzzy SetsHSV and YUV color models

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Mr. Mahesh Kumar C V

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Adaptive Low Light Image Enhancement using Bipolar Fuzzy Set

Abstract Preview

Adaptive Low Light Image Enhancement using Bipolar Fuzzy Set

Mahesh Kumar C V, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai., maheshkumarcv961@gmail.com

David Raj M, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai., daviddgl2013@gmail.com

Saraswathi D, Centre for Human Movement Analytics, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai., saraswathi.d@vit.ac.in

Abstract

Digital images captured under a low-light environment often struggle to clearly assign the intensity due to uncertainty and insufficient illumination. To address such issues, fuzzy set theory plays a crucial role. Bipolar fuzzy set, an important extension of the conventional fuzzy set, provides an advanced framework to deal with uncertainty by focusing on positive and negative membership grades. However, handling negative membership grades and developing an image enhancement model that accesses bipolar fuzzy information pose significant challenges. To address this issue, the present study designs a bipolar fuzzy set based low-light image enhancement model by leveraging the one-to-one correspondence between bipolar fuzzy set and two-polar fuzzy set. Additionally, an image fusion approach is employed to combine the images of positive and negative membership grades. Finally, the experimental study revealed that the proposed model is superior to several state-of-the-art techniques in terms of both enhancement quality and computational efficiency.

Keywords: Bipolar fuzzy setFuzzy complementTwo polar fuzzy set

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Dr. Vo Thi Thanh Ha

Lecturer | Industrial University of Ho Chi Minh city

Abstract: Discrete-time event-triggered state estimation for nonlinear fractional-order systems with time delays

Panel Details:

Day: 1

Session No: 1

Panel No: 16

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. G.Caroline Vincy

Research scholar | VIT,Chennai

Abstract: Embedding and the Spanning Tree Wirelength of $(K_{p}-C_{p})^n$

Panel Details:

Day: 1

Session No: 1

Panel No: 14

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. SHEELA RANI M

Research Scholar | Vellore Institute of Technology, Chennai

Ms. Geethika P R

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: A Graph Contrastive Learning based Graph Neural Networks Model for Node Classification

Abstract Preview

A Graph Contrastive Learning based Graph Neural Networks Model for Node Classification

Geethika P R, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, geethika.pr2022@vitstudent.ac.in

S. Dhanasekar, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, dhanasekar.sundaram@vit.ac.in

Abstract

Graph Contrastive Learning (GCL) has emerged as a powerful paradigm for learning robust node representations in a self-supervised manner. Central to its success is the design of effective augmentation strategies to generate meaningful positive sample pairs. However, most existing methods rely on random perturbations which may overlook the intrinsic structural semantics of the graph. In this work, we propose a novel augmentation technique that leverages fundamental graph-theoretical properties to generate positive samples with stronger semantic alignment. Specifically, we design property-aware transformations guided by measures such as node centrality, clustering coefficients, and community structures, ensuring that the augmented views preserve critical topological and contextual information. Our method seamlessly integrates into standard GCL frameworks and is evaluated on multiple benchmark datasets for node classification tasks. Experimental results demonstrate that our augmentation strategy not only enhances representation quality but also improves classification performance. This work highlights the potential of theoretically grounded augmentations in advancing the robustness and effectiveness of graph contrastive learning.

Keywords: Graph Contrastive LearningGraph Neural Networks

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Mr. Nguyen Hoang Huy

Student | Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

Abstract: Reduced-order event-triggered state observers for a full-car active suspension system with delayed input and external disturbances

Abstract Preview

Reduced-order event-triggered state observers for a full-car active suspension system with delayed input and external disturbances

Dinh Cong Huong, Faculty of Automotive Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam, dinhconghuong@iuh.edu.vn

Dang Tien Phuc, Faculty of Automotive Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam, dangtienphuc@iuh.edu.vn

Nguyen Hoang Huy, Faculty of Automotive Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam, 22000561.huy@student.iuh.edu.vn

Nguyen Van Vi Khang, Faculty of Automotive Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam, 22000571.khang@student.iuh.edu.vn

Abstract

In this paper, we propose a novel method based on an event-triggered mechanism and a reduced-order second-order observer to estimate state vectors of a full-car active suspension system with delayed input and external disturbances. Mathematical equations of a full-car active suspension system with delayed input and external disturbances are first formulated by applying the D'Alembert's principle. Then, an event-triggered mechanism and a reduced-order second-order observer are designed to estimate unmeasured states of the full-car active suspension system. Finally, the existence conditions of the event-triggered reduced-order second-order observer and a design algorithm are derived. Different from the existing like-Luenberger reduced-order observers in the literature, which are suitable for first-order systems, the one in this paper can be used to estimate unmeasured states of dynamical systems described by second-order systems. Moreover, the proposed method in this paper offers advantages in saving communication resources and cost.

Keywords: Event-triggered reduced-order observersfull-car model of the vehicle suspensionsconvex optimization problemLinear matrix inequalities (LMIs)

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Dr. Dao Thi Hai Yen

lecturer | Phu Yen University

Abstract: Design of discrete-time event-triggered Takagi-Sugeno fuzzy observers for Takagi-Sugeno fuzzy time-delay models and their application to nonlinear dynamics time-delay systems

Panel Details:

Day: 1

Session No: 1

Panel No: 16

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. YAGAVI R

RESEARCH SCHOLAR | VELLORE INSTITUTE OF TECHNOLOGY, CHENNAI

Abstract: Graph Stress Metrics and their Computational Insights

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Ms. Palliyil Stefy Solomon

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Robust Picture Fuzzy Regression Function Approach for Stock Market Forecasting: A Systematic Estimator Comparison

Panel Details:

Day: 1

Session No: 1

Panel No: 14

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. CHITHRA S

Research Scholar | Vellore Institute of Technology - Chennai

Abstract: A low-light image enhancement approach using Weber’s intuitionistic fuzzy generator

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. Jayashree BJ

Master's student | Vellore Institute of technology, Chennai

Abstract: Optimizing Air Traffic Forecasting with GRU and Machine Learning Techniques

Abstract Preview

Optimizing Air Traffic Forecasting with GRU and Machine Learning Techniques

Divya Dharshini S, Vellore Institute of Technology, Chennai, divyadharshini.s2024@vitstudent.ac.in

Jayashree BJ, Vellore Institute of Technology, Chennai, jayashree.bj2024@vitstudent.ac.in

Vidhya V, Vellore Institute of Technology, Chennai, vidhya.v@vit.ac.in

Abstract

Air traffic control is increasingly dependent on data-driven systems to maximize airport use and passenger movement through improved forecasting. In this paper, we introduce a new hybrid model consisting of Ridge regression and Gated Recurrent Unit (GRU) neural networks to capture stability as well as temporal dynamics in air traffic forecasting. The GRU learns long-term temporal trends from 15 years (2010–2024) of historical data that include flights, weather, and seasonality, whereas Ridge regression introduces robustness, stability, and interpretability. For more effective decision support, dashboards and scenario-based analyses are combined, and stakeholders can assess alternative operational plans. This mixed-methodology addresses crucial gaps in current forecasting systems, including overdependence on one model, uninterpretability, and inadequate management of India-specific perturbations such as monsoons and festivals. The model facilitates real-time extension for forecasting and focuses on interpretability via Ridge feature attribution and GRU sequence analysis. Evaluation will comprise RMSE, MAE,$R^{2}$,and MAPE, in addition to per-scenario performance, showcasing the efficacy of this Ridge+GRU approach towards adaptive and interpretable air traffic decision-making.

Keywords: Air Traffic ForecastingTime-Series PredictionWeather and Seasonal DisruptionModeling Decision Support SystemsScenario-Based ForecastingHybrid GRU–Ridge ModelMachine Learning in AviationFlight Landing Counts Forecasting.

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Dr. ĐỖ VĂN NĂNG

Lecture | INDUSTRIAL UNIVERSITY OF HCM CITY

Abstract: Event-Triggered Finite Time Space Guarantee Cost Control for Uncertain Time-Delay Fractional-Order Systems

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Dr. P. Balasubramaniam

Senior Professor | The Gandhigram Rural Institute-Deemed to be University Ministry of Education (Shiksha Mantralaya) - Govt. of India Gandhigram - 624 302, Tamil Nadu, India.

Abstract: Quantum Neural Network-Driven Optimal Control of the Nonlinear Time–Fractional Schrödinger Equation

Abstract Preview

Quantum Neural Network-Driven Optimal Control of the Nonlinear Time–Fractional Schrödinger Equation

Vijayaram S, Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India, vijayaramemfmp@gmail.com

Balasubramaniam P, Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India, balugru@gmail.com

Abstract

The optimal control of quantum systems with fractional dynamics has emerged as a powerful approach to capture memory effects, anomalous transport, and non-Markovian behavior. In this work, we develop a hybrid computational framework for the nonlinear time–fractional Schrödinger equation that unifies fractional calculus, advanced numerical solvers, and quantum machine learning. Specifically, the method employs Caputo fractional derivatives via the L1 scheme to incorporate nonlocal temporal effects, an enhanced split-step Fourier method for stable and efficient wave function propagation, and quantum neural networks implemented on variational quantum circuits to generate adaptive control fields. The optimization is guided by a fidelity-based objective functional with regularization to suppress unphysical controls, while gradients of the variational parameters are estimated through finite differences to refine the control landscape. The proposed results demonstrate that combining fractional quantum dynamics with quantum neural networks enables scalable and robust control strategies in non-Markovian and strongly correlated regimes. This framework provides a flexible foundation for extending fractional quantum control theory to applications in quantum technologies, condensed matter systems, and anomalous transport phenomena.

Keywords: Fractional calculusSchrödinger equationsoptimal controlquantum neural networkssplit-step methodsquantum machine learning

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Ms. HARISHNA K H

Research Scholar | Vellore Institute of Technology Chennai

Abstract: Optimization of Wirelength for Embedding Crossed Cubes into Necklace Graphs

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Mr. Afizudeen S

Assistant Professor Junior | Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai

Abstract: A Domination-based Anchor Node Deployment algorithm for Obstacle-Aware Wireless Sensor Network

Abstract Preview

A Domination-based Anchor Node Deployment algorithm for Obstacle-Aware Wireless Sensor Network

Afizudeen S, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, afizudeen.s2022@vitstudent.ac.in

Pavithra R, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, r.pavithra@vit.ac.in

Abstract

Localization in Wireless Sensor Networks (WSNs) is a process of estimating the geographical coordinates of sensor nodes and it remains a vital challenge in WSN for effective and reliable monitoring over physical environments. In real-time applications, sensor localization often functions in obstacle-aware environments such as walls, irregular terrain, etc., which obstructs the wireless transmission and reduces network’s localization performance. To address this challenge in cost-effective approach, it is essential to deploy optimal number of anchor nodes along with optimal positions and for estimating location of unknown sensor nodes located in the network region, every unknown node must receive the information from neighbor anchor nodes. Hence, this study proposes a Domination-based Anchor Deployment in Obstacle-aware WSN (DAPA-O) which determines the minimum requirement of anchor nodes with optimal anchor coordinates for obstacle-based scenarios. The proposed anchor placement algorithm integrates with traditional DV-Hop, Weighted DV-Hop, and Weighted Hyperbolic DV-HOP algorithms to estimate the location unknown nodes position. Simulation results exhibit that the proposed DAPA-O method attains minimum localization error compared to random anchor deployment in terms of average localization error for three DV-Hop variants

Keywords: Anchor deploymentDominationObstacle-aware WSNLocalizationand Wireless Sensor Network

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. Bhavadharani A

Research Scholar | SRM Institute of Science and Technology

Abstract: Characterization of Temporal Behavior in Finite 2D Cellular Automata (Square, Triangular and Hexagonal lattices)

Abstract Preview

Characterization of Temporal Behavior in Finite 2D Cellular Automata (Square, Triangular and Hexagonal lattices)

A. Bhavadharani, Department of Mathematics, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India, ba7497@srmist.edu.in

D. K. Sheena Christy, Department of Mathematics, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India, sheenac@srmist.edu.in

Abstract

2D cellular automata (CA) offer a powerful framework for modelling complex dynamical systems, but their immense rule space and sensitivity to initial conditions make comprehensive classification intractable. Unlike the well-established Wolfram classification for one-dimensional CA, 2D CA lack a unified taxonomy due to increased neighborhood complexity and visualization challenges. In this study, we leverage a central feature, the ability to represent CA configurations as matrices, to systematically analyze rule behavior on finite lattices. By focusing on rule and initial configuration pairs, we introduce a novel framework based on entropy time-series analysis and state change ratio time series analysis to characterize the evolution of 2D CA. This matrix-based representation enables algorithmic detection of homogeneous and periodic patterns on triangular, square, and hexagonal lattices. Our methodology provides a scalable, geometry-independent approach that avoids exhaustive rule enumeration while revealing critical insights into the spatio-temporal dynamics of 2D CA. We present a flattened visualization of 2D CA evolution, enabling a unified temporal representation across generations. This allows both the overall Shannon entropy and compression ratio to be computed, analogous to methods used in elementary cellular automata.

Keywords: Cellular AutomataEntropyState-change ratioTemporal evolution

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. Delna Kuriyakose

Research Scholar | Vellore Institute of Technology

Abstract: Stochastic Modeling of EEG Microstate in Autism Spectrum Disorder Assessment

Abstract Preview

Stochastic Modeling of EEG Microstate in Autism Spectrum Disorder Assessment

Delna Kuriyakose, Vellore Institute of Technology, Vellore, delna.kuriyakose@vit.ac.in

Gowsalya M, Vellore Institute of Technology, Vellore, gowsalya.m@vit.ac.in

Abstract

Autism Spectrum Disorder (ASD) remains diagnostically challenging due to the absence of reliable neurophysiological biomarkers. EEG microstates, which represent transient and quasi-stable patterns of large scale brain activity, provide a promising avenue for characterizing altered neural dynamicsin ASD. This study introduces a computational framework that integrates PCA based microstate segmentation with stochastic temporal modeling via sticky Hidden Markov Models (HMM) and unsupervised learning for pattern discovery. EEG recordings from ASD and neurotypical (NT) cohorts were decomposed into principal microstate classes using PCA, followed by HMM-based estimationof temporal metrics including mean lifetime, fractional occupancy, self transition probabilities, and transition entropy. Dimensionality reduction using UMAP and clustering with K-Means revealed three distinct microstate profiles: Balanced Dynamics, Dysregulated High Entropy State, and Mildly Enhanced Stability. Non-parametric group comparison using the Kruskal Wallis test identified eightmicrostate derived features with statistically significant ASD–NT differences $(p < 0.05)$. Furthermore, a Random Forest classifier achieved $98.76\%$ accuracy in predicting cluster assignments, underscoring the discriminative power of the proposed features. These findings demonstrate that combining microstate-informed temporal dynamics with stochastic modeling and machine learning offers a robust approach for uncovering clinically relevant neural signatures in ASD.

Keywords: Autism Spectrum Disorder (ASD)Hidden Markov Models (HMM)Microstate analysisEEG

Panel Details:

Day: 1

Session No: 1

Panel No: 4

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 709

Mr. PRABU G

Research scholar | Pachaiyappa's College, chennai-30

Abstract: Oscillation of Second-order Differential Equation with Several Non-monotone Advanced Arguments

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Ms. PRIYADHARSHINI

Research Scholar | Vellore Institute of Technology

Abstract: Unsteady Response of Blood Flow Under Body Acceleration with Different Combinations of Arterial Stenoses

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Mr. NANTHA KUMAR G

Research Scholar | Madurai Kamaraj University

Abstract: Semi-Markov T-S Fuzzy time-varying Delay Systems under Uncertainties and Disturbances: Robust $H_\infty$ Integral Fuzzy Sliding Mode Control Approach

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Ms. Shalini Kannan

Research Associate | Vellore Institute of Technology - Chennai Campus

Abstract: Gradient-Based Minimization of Fuzzy Nonlinear Function Using Fletcher-Reeves Algorithm

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. Yuvarani C

Research Scholar | Vellore Institute of Technology

Abstract: Local Antimagic Chromatic Number of Subclasses of Chain Bipartite Graphs

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Dr. V P Ananthi

Assistant Professor | Gobi Arts & Science College

Abstract: Edge Detection of Images Based on the Extraction of Fuzzy Local Binary Pattern

Abstract Preview

Edge Detection of Images Based on the Extraction of Fuzzy Local Binary Pattern

V. P. Ananthi, Department of Mathematics, Gobi Arts & Science College, Karattadipalayam, Erode, Tamil Nadu, India, ananthi@gascgobi.ac.in

K Vetri, Department of Mathematics, Velalar College of Engineering and Technology, Thindal, Erode, Tamil Nadu, India, vetrikg@gmail.com

G. Santhiya, Department of Mathematics, Gobi Arts & Science College, Karattadipalayam, Erode,Tamil Nadu, India, thiyakrish145@gmail.com

Abstract

In the modern AI era images always play a indispensable role in medicine, defence, remote sensing etc.. numerous researches developed to finding the edges of an image. But still the thirsty of finding accurate edges of an image increase rapidly. In this present work a new algorithm is presented to find edges of an image based on local binary pattern(LBP) combined with intuitionistic fuzzy logic to reduce unwanted hairline and also a new contrast enhancement operator is used to improve the accuracy of finding tiny changes in the grey level. The hesitation h from $0$ to $1$ and threshold value $T$ from $0$ to $255$ are optimised for each $3*3$ neighbourhood by high entropy value. Using this optimised h and T values the intuitionistic fuzzy local binary pattern(IFLBP) code is generated. Same procedure is followed to each pixel and corresponding IFLBP code is calculated for entire image. Consolidate all pixels to get crisp edge image. The proposed results are compared with other existing edge detection methods and the performance and efficiency of the proposed method is high when compared with other existing methods

Keywords: Intuitionistic Fuzzy SetLocal Binary PatternContrast MeasureFeature ExtractionEdge detection

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Dr. LAAVANYA D

Assistant Professor | Chennai Institute of Technology

Abstract: Graceful coloring in claw-free split graphs

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Dr. Nivedha

Research Scholar (Assistant Professor Junior, Grade 2) | Vellore Institute of Technology, Chennai

Abstract: Generalized Edge Corona of Spider Graphs are Antimagic: An Algorithmic Approach

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Ms. D. KALA

Research Scholar | VIT CHENNAI

Abstract: Odd-Sum Coloring for Snake Graphs

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Ms. Selvapriya P

Research scholar | Vellore Institute of Technology - Chennai Campus

Abstract: Best proximity point using proximal generalized k-Kannan mapping, and it's application in boundary value problem

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Ms. Kaviya M

Research Scholar | Department of Mathematics and Statistics, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattangulathur, 603 203

Abstract: Combined Effects of Heat Source and Chemical Reaction on MHD Casson Fluid Flow through a Vertical Channel

Abstract Preview

Combined Effects of Heat Source and Chemical Reaction on MHD Casson Fluid Flow through a Vertical Channel

M. Kaviya, Department of Mathematics and Statistics, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, km7813@srmist.edu.in

R. Kavitha, Department of Mathematics and Statistics, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, kavithar10@srmist.edu.in

Abstract

The unstable flow of Casson fluid that passes through a vertical channel was examined and evaluated here. The main objective of this work is to give precise final solutions for unstable free convection flows in vertical channels of Casson fluid while accounting for the influence of magnetohydrodynamics (MHD). The findings of the equations governing them were obtained using a technique known as the Laplace transform. Heat source and chemical reaction have been added to the current study. The effects of several dimensionless terms have been computed and assessed, including the following: thermal source, chemical reaction, Casson parameter $\gamma$, magnetic field, Prandtl number, Grashof number $Gr$, Modified Grashof number $Gm$, skin friction, Sherwood number and Nusselt number. It has been addressed how the above stated parameters affect the profiles of temperature, concentration, and velocity. The behaviour of various profiles over different dimensionless parameters was demonstrated graphically in order to explain and understand deeply. All the graphical results were obtained using MATLAB software which were used to analyse the mathematical solutions of governing equation profiles.

Keywords: Heat and mass transferMHDheat sourcethermal radiation and Laplace technique

Panel Details:

Day: 1

Session No: 1

Panel No: 12

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. ANNAL R

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Graceful Coloring of Triangular Snake Graphs

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Ms. AKSHAYA. R

Research Associate | Vellore Institute of Technology, Chennai

Abstract: A Computational Study on Casson Hybrid Nanoliquid flow over a Rough Surfaced Spinning Sphere with Irregular Boundaries under the Influence of Variable Viscosity and Thermal Conductivity

Abstract Preview

A Computational Study on Casson Hybrid Nanoliquid flow over a Rough Surfaced Spinning Sphere with Irregular Boundaries under the Influence of Variable Viscosity and Thermal Conductivity

Akshaya R, Department of Mathematics, Vellore Institute of Technology, Chennai, akshaya.r2022a@vitstudent.ac.in

Abhishek Kumar Singh, Department of Mathematics, Vellore Institute of Technology, Chennai, abhishekkumar.singh@vit.ac.in

Abstract

Hybrid nanofluids (HNFs), which are advanced thermal fluids created by suspending two or more distinct types of nanoparticles in a base fluid to greatly improve heat transfer performance, are widely used in high-efficiency cooling systems for engineering and biomedical applications. Applications for flow over rotating spheres include cooling spherical electronic components, biomedical devices including targeted drug delivery systems, and aircraft thermal shielding. Therefore, the present study investigates the non-Newtonian Casson hybrid nanofluid flow across a rotating sphere with a rough surface and uneven boundaries. Water is regarded as the base fluid in this analysis, and the hybrid nanofluid is created by using Copper (Cu) and Titanium dioxide (TiO$_2$) as suspended nanoparticles. The Modified Buongiorno Model (MBM) framework is used to conduct the inquiry. Based on the prevailing model, the flow's mathematical representation involves defining nonlinear governing equations. After undergoing nonsimilar transformations, a quasilinearization technique and an implicit finite difference scheme are applied to these boundary layer governing equations. The resulting block tridiagonal system of equations is subjected to Varga's algorithm. The outcomes are fairly comparable to those formerly published. The visual findings revealed that the swirl velocity S falls more gradually as the rotation parameter increases, suggesting a deeper rotational impact inside the fluid, whereas the tangential velocity F increases due to increased surface-driven flow. Greater rotational momentum transfer from the spinning sphere to the surrounding fluid is promoted by an increase in swirl velocity S within the boundary layer, which is caused by an increase in $\beta_2$ , which represents surface roughness or defects. Furthermore, the fluid temperature inside the flow region increases in tandem with the intensity of thermal radiation. Furthermore, because of enhanced diffusion, a larger Brownian motion parameter (Nb) causes a decrease in nanoparticle concentration close to the wall, intensifying the concentration differential across the boundary layer.

Keywords: Casson Hybrid NanofluidSpinning sphereIrregular boundariesImplicit Finite Difference SchemeThermal radiation

Ms. sophia Navis Mary D

Assistant Professor | Ethiraj college for women

Abstract: Decode the Attention mechanism of Modern Large Language Models: A study

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Ms. A. MABEL PRINCES

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Odd-Sum Coloring of Ladder Graphs

Panel Details:

Day: 1

Session No: 1

Panel No: 7

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 712

Dr. Pavithra T

Assistant Professor | AMET Deemed to be University(AMET-IST Campus), Chennai-603305

Abstract: Numerical Study on Electric field induced Magnetization Dynamics in the biquadrant, anisotropic and antisymmetric ferromagnetic nanowire

Abstract Preview

Numerical Study on Electric field induced Magnetization Dynamics in the biquadrant, anisotropic and antisymmetric ferromagnetic nanowire

Pavithra T, Department of Physics, AMET Deemed to be University(AMET-IST Campus), Chennai, pavithrathiruvalluvan@gmail.com

Kavitha L, Department of Physics, School of Basic and Applied Sciences, Central University of Tamil Nadu, Thiruvarur-610005, lkavitha@cutn.ac.in

Abstract

The propagation of electromagnetic (EM) solitons in low-dimensional magnetic nanostructures has attracted significant attention due to their potential in nanoscale information processing. In this study, we focus on an antisymmetric ferromagnetic nanowire system by incorporating higher-order effects, namely biquadratic and anisotropic interactions, into the governing dynamical equations. To obtain a precise evaluation of soliton dynamics, the multiple-scale perturbation approach is employed, which allows the reduction of the system to coupled ordinary differential equations describing the soliton velocity and amplitude. These equations are solved numerically using the fourth-order Runge–Kutta method, ensuring accurate treatment of the nonlinear and dispersive contributions. The results demonstrate that higher-order interactions significantly modify both the amplitude and velocity of EM solitons, influencing their stability and propagation behavior. Such findings provide deeper insight into the controllability of soliton dynamics in ferromagnetic nanowires and highlight their potential applications in advanced spintronic and magnonic devices.

Keywords: Electromagnetic SolitonRunge-Kutta MethodBiquadratic Spin InteractionAnistropic Spin InteractionFerromagnetic NanowireMutiple Scale perturbation Approach

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. M PARUVATHA VATHANA

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Geodesic Dominated Coloring in Certain Graphs

Abstract Preview

Geodesic Dominated Coloring in Certain Graphs

M Paruvatha Vathana, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, paruvathavathana.m2020@vitstudent.ac.in

R Jayagopal, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, jayagopal.r@vit.ac.in

Abstract

A geodesic dominated coloring of a graph $G$ is a proper coloring in which each color class is dominated by at least one geodesic. The minimum number of colors required for such a coloring is called the geodesic dominated chromatic number, denoted by $\chi_{\mathrm{dom}}^{g}(G)$. This concept builds upon the classical dominated coloring framework, offering fresh perspectives on vertex domination and graph coloring. By incorporating geodesics into domination, geodesic dominated coloring extends traditional domination by requiring color classes to be dominated through shortest-path structures, rather than adjacency alone. This reinterpretation reflects how the geodesic dominated coloring problem extends the classical domination framework into a new dimension. This shift highlights the evolution from a vertex-centric domination approach to a class-centric one influenced by geodesic structures. In this paper, we determine geodesic dominated chromatic number for several fundamental graph classes, including circulant networks, generalized $k$-ary trees, line graphs, and ladder-based topologies. For each class, we establish exact values or sharp bounds of geodesic dominated chromatic number and provide algorithmic constructions to realize optimal colorings. We further analyze line graphs, which extend the theory to edge interactions, and cycle of ladder and extended cycle of ladder graphs, deriving closed form expressions for their geodesic dominated chromatic numbers and demonstrating how structural extensions impact domination requirements. The results are not limited to theoretical bounds; constructive algorithms are presented to demonstrate realizable optimal solutions. This dual emphasis strengthens the connection between theory and practice, as algorithmic strategies can be directly applied to network designs requiring minimal redundancy and robust coverage. From a technical perspective, this study highlights the role of structural graph properties in determining coloring behavior. These contributions provide closed-form results and algorithmic strategies that enrich the broader study of distance-based domination. The proposed algorithms not only formalize constructive proofs of these bounds but also highlight applications in communication networks, hierarchical decision systems, and resilient routing structures where shortest-path based coverage is essential.

Keywords: proper coloringgeodesic dominated coloringline graphcirculant networksgeneralized $k$-ary treescycle of ladderextended cycle of ladder

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Ms. NITHIYA V

Research Scholar | SCHOOL OF ADVANCED SCIENCES, DEPARTMENT OF MATHEMATICS, VIT CHENNAI.

Abstract: Numerical investigation of heat transfer inside a closed enclosure with radiation effect: A comparative study

Abstract Preview

Numerical investigation of heat transfer inside a closed enclosure with radiation effect: A comparative study

Nithiya V, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, nithiya.v2022@vitstudent.ac.in

Prabhakar V, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, prabhakar.v@vit.ac.in

Abstract

Heat transfer enhancement plays a crucial role in various engineering and science applications, such as resonators, cooling systems, electronic equipment, and the MROI array telescope, all of which have been studied in recent years. One of the most effective ways to cool the system is through natural convection. For the current study, we considered a water-filled dome-shaped enclosure to examine the behaviour of natural convection with thermal radiation effect and appropriate boundary conditions. The physical model is solved using the Galerkin finite element method for the parameters - Rayleigh number ($Ra = 103$ to $106$), Prandtl number ($Pr = 5.828$), and radiation parameter ($Rd = 0, 2, 6, 10$). The results obtained using the Mathematica software are validated by comparing with the results from the COMSOL Multiphysics software. The findings are presented in terms of streamlines, isotherms, mean Nusselt number, and the mean temperature profile. Furthermore, a comparison study is made with the rectangular-shaped enclosure. The obtained results indicate that the mean Nusselt number increases with an increase in both the Rayleigh number and the radiation parameter. On the other hand, the mean temperature falls with an increase in the radiation parameter and rises with an increase in the Rayleigh number. The mean Nusselt number reaches its maximum at Rayleigh number $10^6$ and radiation parameter $Rd = 10$ compared to the rectangular enclosure. Finally, the dome-shaped enclosure shows an enhanced heat transfer compared to a rectangular enclosure; thus, the fluid moves smoothly inside the enclosure.

Keywords: Natural convectionHeat transferFEMEnclosure

Panel Details:

Day: 1

Session No: 1

Panel No: 3

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 708

Dr. DEVA NITHYANANDHAM

Post Doctoral Fellow | Shenzhen MSU-BIT University, Shenzhen, Guangdong Province, China

Abstract: LIFT-LLIE: A Lightweight Intuitionistic Fuzzy Technique for Low-Light Image Enhancement

Abstract Preview

LIFT-LLIE: A Lightweight Intuitionistic Fuzzy Technique for Low-Light Image Enhancement

Deva Nithyanandham, MSU-BIT-SMBU Joint Research Centre of Applied Mathematics, Shenzhen MSU-BIT University, Shenzhen, China and School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China, devamathz@gmail.com

Saravanakumar Ramasamy, MSU-BIT-SMBU Joint Research Centre of Applied Mathematics, Shenzhen MSU-BIT University, Shenzhen, China, saravana@smbu.edu.cn

Abstract

Image enhancement is a significant research area in image processing with broad applications, ranging from surveillance to medical imaging. Low-light image enhancement remains a challenging problem due to the inherent uncertainty, noise and loss of structural details in under-illuminated images. To address this issue, intuitionistic fuzzy generator-based low-light image enhancement models have been developed. However, the existing approaches often rely on additional contrast enhancement methods, which increase dependency on external techniques. To address these limitations, this study introduces a lightweight intuitionistic fuzzy technique for enhancing low-light images without the need for supplementary methods. The proposed method effectively adjusts intensity distributions while preserving local structures and subtle details, ensuring natural and visually pleasing results. Experiments on benchmark datasets, along with comparative analyses against state-of-the-art methods, demonstrate the superiority of the proposed approach in terms of quantitative and qualitative results. These results highlight the model’s efficiency, robustness and practical applicability for real-world low-light imaging scenarios.

Keywords: Intuitionistic fuzzy setIntuitionistic fuzzy generatorLow-light image enhancementParameter optimizationEntropy measure

Panel Details:

Day: 1

Session No: 1

Panel No: 17

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Monisha.L

Research scholar | Vellore institute of technology

Abstract: QSPR Analysis using Degree-Based Edge Entropy Indices for Angiotensin II Receptor Blockers (ARBs) Drugs

Abstract Preview

QSPR Analysis using Degree-Based Edge Entropy Indices for Angiotensin II Receptor Blockers (ARBs) Drugs

Monisha. L, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, monishalogu1998@gmail.com

Jayagopal. R, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, jgopal89@gmail.com

Abstract

Angiotensin II Receptor Blockers (ARBs) constitute a group of antihypertensive agents that specifically inhibit the interaction of angiotensin II with the angiotensin type 1 $(AT_1)$ receptor, a central component of the renin–angiotensin–aldosterone system (RAAS). By antagonizing this receptor, ARBs prevent vasoconstriction, decrease aldosterone release, and enhance sodium and water excretion, thereby effectively lowering blood pressure. These drugs are extensively employed in the treatment of hypertension, heart failure, chronic kidney disease, and in patients following myocardial infarction, providing significant cardiovascular protection. Since their introduction in the 1990s, ARBs have become a cornerstone in global cardiovascular therapy, offering a safe and effective alternative for millions of patients worldwide, especially those who are intolerant to ACE inhibitors. A topological index is a molecular descriptor that quantitatively encodes the structural features of a molecule and has been extensively employed in modeling diverse physicochemical properties within quantitative structure–property relationship (QSPR) studies. These investigations play a vital role in the early phases of drug development by supporting the identification and optimization of potential candidates and providing economical alternatives to traditional experimental methods. This study focuses on an extensive set of Angiotensin II Receptor Blockers (ARBs), including Losartan, Valsartan, Irbesartan, Candesartan, Olmesartan, Telmisartan, Eprosartan, Azilsartan, Fimasartan, and Olmesartan Medoxomil. In this study, QSPR models will be developed using degree and entropy topological indices, and regression analysis is employed to reveal the most significant correlations with the physicochemical properties of ARB drugs. This integrative approach characterizes the molecular features of ARB drugs, providing a robust framework for understanding their pharmacological activity and therapeutic potential.

Keywords: Degree edge partitionEntropy based topological indicesAngiotensin II Receptor BlockersQuantitative structure property relationship

Panel Details:

Day: 1

Session No: 1

Panel No: 4

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 709

Mr. Jishnu Vijay

Research scholar | VIT Chennai

Abstract: Enhanced forecasting of ocean wave parameters via wavelet integrated ML models

Abstract Preview

Enhanced forecasting of ocean wave parameters via wavelet integrated ML models

Jishnu Vijay, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, jishnu.vijay2023@vitstudent.ac.in

V. Prabhakar, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, prabhakar.v@vit.ac.in

Abstract

This study presents an advanced forecasting approach for ocean wave parameters such as significant wave height (SWH) and average wave period (APD) using hybrid wavelet-based machine learning (WLML) models. Measured buoy data from four NOAA-NDBC stations (Buoy IDs: 46221, 46222, 46225, 46229) recorded in 2023 were analysed. Traditional LSTM and random forest (RF) models were enhanced through discrete wavelet decomposition, enabling multiscale feature extraction and effective noise reduction. The proposed WL-LSTM and WL-RF frameworks demonstrated substantial improvements in prediction accuracy. Comparative analysis using error metrics and visual validation through time series graphs and scatter plots shows that the WLML models achieved up to $75\%$ error reduction across standard evaluation metrics compared to their baseline counterparts. The proposed approach is used to forecast extreme wave events in each location (highest $5\%$ values of each dataset) and confirms the superiority over the corresponding baseline models. The results confirm the efficacy of wavelet integration in improving the reliability of wave forecasting systems, especially in dynamic sea-state conditions.

Keywords: LSTMRandom ForestMachine learningDeep learningWavelet

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Mr. Eswar N

Research Scholar | Vellore Institute of Technology, Chennai Campus

Abstract: On the Locating-Domination and Locating-Total Domination Numbers of Middle Graphs of Certain Classes of Graphs

Abstract Preview

On the Locating-Domination and Locating-Total Domination Numbers of Middle Graphs of Certain Classes of Graphs

N. Eswar, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, eswar.n2019@vitstudent.ac.in

R. Jayagopal, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, jayagopal.r@vit.ac.in

Abstract

Domination in graphs has been a fundamental area of research in graph theory due to its strong theoretical foundations and widely applicable in facility location, and fault-diagnosis in interconnection networks. A dominating set of a graph $G=(V,E)$ is a subset $S\subseteq V(G)$ such that every vertex in $V(G)\setminus S$ is adjacent to at least one vertex of $S$. The domination number $\gamma(G)$ is the minimum cardinality among all the dominating sets. Over the years, several refined versions of domination have been introduced. Among them, the locating-dominating set (LDS) and the locating-total dominating set (LTDS) have gained special attention. A locating-dominating set of $G$ is a dominating set $S\subseteq V(G)$ such that $N(u)\cap S\neq N(v)\cap S$ for all $u,v\in V(G)\setminus S $. The locating-domination number $\gamma^{L}(G)$ is the minimum cardinality among all the locating-dominating sets. A locating-total dominating set of $G$ is a total dominating set $S\subseteq V(G)$ such that $N(u)\cap S\neq N(v)\cap S$ for all $u,v\in V(G)\setminus S$. The locating-total domination number $\gamma^{L}_{t}(G)$ is the minimum cardinality among all the locating-total dominating sets. These problems are computationally hard in general graphs, yet they capture essential aspects of network monitoring and fault-tolerant systems. Locating-dominating sets (Locating-total dominating sets) can be used to determine the exact location of an object in a graph if devices can detect only objects in its neighbourhood and the object cannot occupy the same vertex as detection devices. In this paper, we investigate two domination variants under a structural transformation of graphs known as the middle graph, denoted by $M(G)$. For a given graph $G=(V,E)$, the middle graph $M(G)$ is defined as the graph whose vertex set consists of both $V(G)\cup E(G)$. Two vertices in $M(G)$ are adjacent if and only if their corresponding elements in $G$ are either incident (vertex–edge) or adjacent (edge–edge). This construction enriches the original structure and often alters domination-related parameters in non-trivial ways, thereby motivating a systematic study of locating-domination and locating-total domination in middle graphs. We examine four well-studied graph families, namely the path graph ($P_n$), star graph ($K_{1,n}$), wheel graph ($W_n$) and friendship graph ($F_n$). For each case, we derive exact results for the minimum cardinalities of the locating-domination set (LDS) and the locating-total domination set (LTDS) in their corresponding middle graphs.

Keywords: location-dominating setlocating-total dominating setmiddle graphstar graphwheel graphfriendship graph

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Mr. PANDIYAN R

Research Scholar | Vellore Institude of Technology, Chennai

Abstract: Design of Artificial Intelligent Based Air Quality Monitoring, Prediction and Control Using IoT

Abstract Preview

Design of Artificial Intelligent Based Air Quality Monitoring, Prediction and Control Using IoT

D. Mercy, Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering Thalavapalayam, Karur Tamil Nadu, India, mercyd.ece@mkce.ac.in

S. Saju, Department of Biomedical Engineering, KIT-Kalaignar Karunanidhi Institute of Technology, Kannampalayam (Post),Coimbatore, Tamil Nadu, India, sajube82@gmail.com

Pandiyan Rengan, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India., pandiyan.r2022@vitstudent.ac.in

Arul Rajagopalan, Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India, rarul@vit.ac.in

Abstract

Air pollution poses a significant threat to public health and well-being, necessitating innovative solutions to mitigate its adverse effects. The proposed Intelligent Air Quality control system integrates the Internet of Things (IoT) for real-time data collection, artificial intelligence (AI) for predictive analysis, and automated responses. Unlike traditional alerts, this approach proactively monitors, analyses, and responds dynamically to pollution levels in real-time urban environments and densely polluted locations. The proposed system uses AI algorithms to analyse IoT-based sensor data, predict pollution levels, and activate purification components, such as water sprays and air filters, efficiently and also provide autonomous decision-making and adaptive responses to changing environmental situations. The prototype, outfitted with AI-enhanced sensors, persistently tracks real-time air quality parameters, including $PM_{2.5}$, $PM_{10}$, $CO_2$, and $VOC$ concentrations. The AI algorithm analyses this data, identifying specific pollutants and determining optimal purification settings. It can predict pollution peaks and automatically increase purification intensity when necessary. Based on the AI’s recommendations, the system adjusts the fan speed, filtration stages, and power to achieve effective purification with low energy consumption. The modular design supports flexible deployment across multiple locations, making it ideal for outdoor and indoor public spaces like metro stations, malls, public squares, and sports venues. Additional modules can be attached or removed as needed, adapting the prototype’s purification capacity to various scales. The system contributes towards public awareness of air quality by providing real-time data accessible through mobile apps or public displays. This engagement not only educates but also encourages healthier behaviours, such as avoiding high-pollution zones during peak times. This information may help promote pollution-mitigating habits, such as limiting outside activities during periods of low air quality. AI-enabled may interface with other smart city sensors and systems, augmenting the city's capacity to monitor and regulate air quality holistically. This integrated method results in synchronized air quality control across diverse metropolitan areas.

Keywords: Internet of Things (IoT)Artificial Intelligence (AI)Air Quality measuringSensorssmart city

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Ms. ARUNA DEVI M

Research Scholar | Vellore Institute of Technology

Abstract: Numerical solution of Gardner’s equation using Spatial-Temporal Pseudospectral Method

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. Rubasri. S

RESEARCH SCHOLAR | Bharathiar university

Abstract: Synchronization of Complex Networks Under Deception Attacks Using Aperiodically Intermittent Event triggered Control and Average Delayed Impulsive Gains

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Ms. LAKSHMI RAMESH

RESEARCH SCHOLAR | VELLORE INSTITUTE OF TECHNOLOGY, CHENNAI

Abstract: On the Mostar indices of the line graphs of the subdivision graphs

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Ms. FATHIMA TABREZ

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Star Coloring of Graph Amalgamation

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Ms. Deepika R

Research scholar | SRM Institute of Science and Technology

Abstract: A Multiparty Public Key Exchange Protocol Using Tropical Matrix Algebra

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Mr. Inturu Srinivas

Senior Research Fellow | Vellore Institute of Technology, Vellore

Abstract: Wave Scattering by Bottom-mounted Asymmetric Inverse T - shaped Breakwater

Abstract Preview

Wave Scattering by Bottom-mounted Asymmetric Inverse T - shaped Breakwater

Inturu Srinivas, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu , India, srinivasinturu703@gmail.com

Sekarapandian Natarajan, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu , India, sekarapandian@gmail.com

Rajesh Ranjan Dora, Indian Institute of Technology Madras,IIT P.O., Chennai , India, rajeshranjandora@gmail.com

Abstract

Protecting nearshore areas with artificial breakwaters or other defence measures is a primary concern in the field of coastal engineering. Finding the ideal shape to regulate wave diffraction is one of the most crucial objectives in the subject of breakwaters. A range of floating or fixed surface breakwaters with different designs has been created to improve wave attenuation and save building costs. In this sense, breakwaters with a T-shaped or inverse T-shaped shape can fulfill this need. Research on asymmetric inverse T-shaped breakwaters has been lacking; however, there are plenty of articles available on inverse symmetric T-shaped breakwaters. Here, we have taken into consideration the inverse asymmetric T-shaped breakwater, whose two side blocks differ in height or width. The eigen function expansion method is used to solve the problem, and the results are verified by the existing literature. Additionally, for the accuracy of our method, the energy balance relation is used to validate the reflection and transmission coefficients. The impact of various structural and physical factors on the transmission and reflection coefficients is examined in order to evaluate the performance of an asymmetric inverse T-shaped breakwater. Additionally, the wave forces acting on the breakwater are calculated and examined for various structural and physical parameters. By comparing it with the symmetric T-shaped breakwater, the effectiveness of the asymmetric inverse T-shaped breakwater is determined.

Keywords: Inverse T-shaped breakwaterEigenfunction expansion methodReflection and transmission coefficientWave forces

Panel Details:

Day: 1

Session No: 1

Panel No: 12

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. NARENSHAKTHI T

Research Scholar | SAS, Vellore Institute of Technology, Vellore

Abstract: Command filter-based adaptive $H_{\infty}$ asymptotic tracking of delayed networked control systems

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Ms. RAMYA M

RESEARCH SCHOLAR | VELLORE INSTITUTE OF TECHNOLOGY, CHENNAI.

Abstract: An Algorithm Based on Pseudospectral Method in Time and Space Direction for Hyperbolic Conservation Law

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. P. ANTO VINN CHRISTY

Ph. D RESEARCH SCHOLAR | BHARATHIAR UNIVERSITY, COIMBATORE

Abstract: Stabilization of stochastic discrete-time systems with time delay

Panel Details:

Day: 1

Session No: 1

Panel No: 1

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 701

Ms. JIMONA J J

RESEARCH SCHOLAR | SRM INSTITUTE OF SCIENCE AND TECHNOLOGY KATTANKULATHUR

Abstract: Enhancing Digital Image Security with an Arccos Derived Chaotic Map

Abstract Preview

Enhancing Digital Image Security with an Arccos Derived Chaotic Map

Jimona J J, SRM Institute of Science And Technology, Kattankulathur, jj3361@srmist.edu.in

Perumal R, SRM Institute of Science And Technology, Kattankulathur, perumalr@srmist.edu.in

Abstract

This paper introduces a one-dimensional chaotic map based on the inverse trigonometric arccos function for secure image encryption. Analytical evaluations, including bifurcation analysis, positive Lyapunov exponents, and maximized Shannon entropy, confirm that the proposed map demonstrates strong nonlinear properties and maintains stable chaotic behavior across a wide parameter range. Building on these characteristics, we design an encryption scheme that integrates HKDF-derived key scheduling, chaotic keystream generation, FisherYates pixel permutation, and ARX-based diffusion. The scheme ensures both confusion and diffusion, with a decryption process that is the exact inverse of encryption, guaranteeing accuracy. Extensive experiments on benchmark images validate the robustness of the method. Results show high NPCR and UACI values, uniform histograms, entropy values close to the ideal 8, and near-zero pixel correlation, all of which confirm strong resistance against statistical and differential attacks. In addition, elevated PSNR values demonstrate reliable decryption quality. Comparative analysis with existing chaotic encryption approaches highlights that the proposed method not only achieves near-optimal cryptographic performance but also offers competitive efficiency. Overall, these findings establish the arccos-based chaotic map as a secure and effective tool for digital image encryption.

Keywords: Image encryptionarccos chaotic mapHKDF key schedulingSecurity analysis

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. Ruby R

Research Scholar | SRM Institute of Science and Technology

Abstract: Mixed Convective MHD Oscillatory Jeffrey Fluid Flow through Asymmetric Porous Channel: Influence of Hall Current and Lorentz force.

Abstract Preview

Mixed Convective MHD Oscillatory Jeffrey Fluid Flow through Asymmetric Porous Channel: Influence of Hall Current and Lorentz force.

R Ruby, SRM Institute of Science and Technology, Kattankulathur, rr8278@srmist.edu.in

J. Sasikumar, SRM Institute of Science and Technology, Kattankulathur, sasikumj@srmist.edu.in

Abstract

The Jeffrey fluid model provides an accurate representation of non-Newtonian viscoelastic behaviour, incorporating relaxation and retardation phenomena, hence rendering it applicable in various engineering contexts. This work investigates the Hall current effects in mixed convective MHD oscillatory flow of a Jeffrey fluid within an asymmetric porous channel. The governing nonlinear partial differential equations, which include the Hall current force, porosity, thermal radiation, and mixed convection effects, are established under the Boussinesq approximation and appropriate oscillatory boundary conditions. An approximate analytical solution is derived for the velocity and temperature profiles. The findings indicate that the Hall current substantially modifies the primary and secondary flow fields, diminishes magnetic damping, and amplifies cross-flow motion within the channel. The combined forced and free convection enhances the velocity of the fluid in the direction of buoyancy force. The research offers novel insights into transport mechanisms in non-Newtonian magnetohydrodynamic flows within porous media, pertinent to applications including petroleum reservoirs, magnetohydrodynamic power production, and plasma dynamics.

Keywords: Mixed convectionMHDOscillatory flowHall currentJeffrey fluidAsymmetric channel

Panel Details:

Day: 1

Session No: 1

Panel No: 3

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 708

Ms. Susmitha G

Research Scholar | VIT, Chennai

Abstract: Coverage-Aware Scheduling in Directional Sensor Networks Through Harris Hawks Optimization

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. VIJAYALAKSHMI N

Research scholar | VIT CHENNAI

Abstract: Viscous dissipation and Joule Heating effects on Bioconvective MHD Williamson nanofluid flow with Motile Microorganisms over an inclined stretching surface.

Abstract Preview

Viscous dissipation and Joule Heating effects on Bioconvective MHD Williamson nanofluid flow with Motile Microorganisms over an inclined stretching surface.

Vijayalakshmi N, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, vijayalakshmi.n@vitstudent.ac.in

P. Vijay Kumar, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, vijaykumar.p@vit.ac.in

Abstract

Understanding heat and mass transport in nanofluid systems is crucial for advancing next-generation biomedical and energy technologies. By combining the effects of Joule heating, viscosity dissipation, chemical reaction, heat generation/absorption, magnetic field, and bioconvection of motile microorganisms, this study investigates the magnetohydrodynamic (MHD) flow of a Williamson nanofluid via an inclined stretching surface. Similarity transformations are used to reduce the system's governing equations to nonlinear ODEs, which are then solved using the bvp4c method. It is found that whereas Joule heating and viscous dissipation primarily affect the thermal field, transport parameters control changes in velocity, concentration, and the distribution of microorganisms. The design of bio-inspired cooling systems, nanoscale heat exchangers, microfluidic devices, and diagnostic technologies can all benefit from these ideas.

Keywords: MHDWilliamson nanofluidstretching surfacethermophoresisBrownian motionchemical reactionheat generation or absorptionBioconvectionMotile microorganismsJoule heating and viscous dissipation

Panel Details:

Day: 1

Session No: 1

Panel No: 3

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 708

Dr. B. Amutha

Assistant professor | St. Joseph's College of Engineering, OMR, Chennai, 600119.

Abstract: An Advanced Neutrosophic Max-Min Semiring and its Aspects in Network Security

Panel Details:

Day: 2

Session No: 1

Panel No: 4

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 712

Ms. A RAMALAKSHMI

Research Scholar | VIT CHENNAI

Abstract: One-Dimensional Fractional Volterra Integro-Differential Equation solving Pseudospectral Method

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

Dr. Hassan Tahir

Postdocotoral Fellow | Shenzhen MSU-BIT University

Abstract: Stochastic Analysis of Worm Spread in WSNs: Lyapunov Stability and $SE_1E_2IR$ Modeling for Enhanced Security

Abstract Preview

Stochastic Analysis of Worm Spread in WSNs: Lyapunov Stability and $SE_1E_2IR$ Modeling for Enhanced Security

Hassan Tahir, MSU-BIT-SMBU Joint Research Center of Applied Mathematics, Shenzhen MSU-BIT University, Shenzhen, China, hassantahir@smbu.edu.cn

Yan Chen, School of Mathematical Sciences, East China Normal University, Shanghai 200241, China, ychen@stu.ecnu.edu.cn

Abstract

In this study, we apply the tools of stochastic calculus to address critical security challenges in wireless sensor networks (WSNs) that arise due to operational constraints, particularly focusing on the rapid spread of worms through network nodes. To achieve this, we present an innovative model that incorporates two exposed states, based on a stochastic susceptible--exposed state 1--exposed state 2--infectious--recovered ($SE_1E_2IR$) system to describe the dynamics of worm propagation within WSNs. The approach involves establishing the existence and uniqueness of an ergodic stationary distribution using suitably constructed Lyapunov functions. We find that incorporating random environmental perturbations into the model yields a more accurate representation of the dynamics than deterministic models, which often overestimate the propagation potential of these threats. Numerical simulations affirm the effectiveness of the analytical approach in understanding worm propagation within WSNs. This study not only enhances the understanding of worm propagation dynamics in WSNs but also provides a methodological framework for network security applications.

Keywords: Wireless sensor networksErgodic stationary distributionExtinctionStochastic epidemic model

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. KOWSALYA P

Research Scholar | Bharathiar University

Abstract: Fixed-time Synchronization of Hidden Markov Jump Inertial Reaction-Diffusion Neural Networks with Time Delay using Detector Based Boundary Control

Abstract Preview

Fixed-time Synchronization of Hidden Markov Jump Inertial Reaction-Diffusion Neural Networks with Time Delay using Detector Based Boundary Control

P. Kowsalya, Bharathiar University, Coimbatore, kowsalyamadhu97@gmail.com

R. Rakkiyappan, Bharathiar University, Coimbatore, rakkigru@gmail.com

Abstract

This paper addresses the fixed-time synchronization problem for continuous-time hidden Markov jump inertial reaction-diffusion neural networks with time delay using a detector-based boundary control approach. The abrupt changes in network parameters and structure are represented through a hidden Markov jump model that incorporates both hidden and observed states. A key feature of this model is its ability to handle varying levels of detection information, such as complete information, no information, and others, allowing for a general observation framework. Based on the states estimated by the detector, we design a mode-dependent boundary synchronization controller under Neumann boundary conditions. Unlike traditional full domain controller designs, the proposed approach reduces the number of controllers required, thereby lowering implementation costs. In addition, a security control law is developed to ensure fixed-time synchronization of the considered inertial reaction-diffusion neural networks within a fixed settling time. The demonstrated settling time of the designated inertial reaction-diffusion neural networks is provided. Moreover, a novel Lyapunov function is constructed to capture system state information and address challenges arising from reaction-diffusion terms, with synchronization conditions established through inequality techniques. Finally, comparative examples are presented to validate both the correctness and the superiority of the proposed method.

Keywords: Inertial reaction-diffusion neural networksHidden Markov jumpFixed-time synchronizationDetector based boundary controlSettling time

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

Mr. A VIGNESH

PhD Research scholar | Bharathiar university, Coimbatore.

Abstract: Stability and Domain of Attraction Analysis of Stochastic Nonlinear Delay Systems

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

Ms. S.Sugarami

PhD Research Scholar | Annamalai university

Abstract: Accretion Flow Around a Black hole with Electromagnetic Fields and Plasma Behaviour

Panel Details:

Day: 1

Session No: 1

Panel No: 12

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. RAMYA

Assistant Professor | SRM INSTITUTE OF SCIENCE AND TECHNOLOGY, RAMAPURAM, CHENNAI

Abstract: Oscillation of Third-Order Composite Trinomial Delay Differential Equations

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. Joviyal Rose.A

Student | VIT Vellore

Abstract: A Study of Smart BI for Data Insights and Visual Analytics in Indian Scenario

Abstract Preview

A Study of Smart BI for Data Insights and Visual Analytics in Indian Scenario

Joviyal Rose A., Vellore Institute of Technology, Vellore, joviyalrose.a2024@vitstudent.ac.in

Isha Karmakar, Vellore Institute of Technology, Vellore, isha.karmakar2024@vitstudent.ac.in

Jitendra Kumar, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, jitendra.kumar@vit.ac.in

Abstract

Business Intelligence (BI) systems are widely used to extract insights from structured databases, but traditional query methods such as SQL remain inaccessible to non-technical users, limiting data-driven decision-making within organizations. To address this limitation, we present SmartBI, a natural language interface for relational databases that allows anyone to access data through conversational queries. The system transforms user inquiries into structured PostgreSQL queries by utilizing an LLM alongside LangChain's SQL agent executor and implements through Streamlit to offer a dynamic, chatbot-style interface. A dataset containing $1000$ retail sales transactions was utilized for testing, with outputs evaluated against actual results generated in Excel. Experimental findings show that SmartBI consistently reproduces query outcomes on tasks including city-specific lists, row counts, and customer segmentation. For arithmetic queries, the system arrived at exact ground-truth values, demonstrating reliability in numeric reasoning. In addition to precision, SmartBI generates descriptive insights and interactive visuals like pie, bar, and line graphs, thereby improving understanding for non-technical viewers. These results establish SmartBI as a reliable and user-friendly BI tool that bridges the gap between human language and structured data. In future development, its capabilities will be enhanced to accommodate intricate joins, semantic discrepancies between natural language and schema attributes, as well as improved visualization options to better serve a wider range of analytical applications.

Keywords: SmartBINatural Language ProcessingBusiness IntelligenceConversational AnalyticsData Visualization

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. Gowrisankar S

Assistant Professor(Sr.Gr) | PSG College of Technology

Abstract: Fixed time synchronization for stochastic complex networks with time-delay

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. RAGHA PRIYA S P

Research Scholar | VISTAS

Abstract: Optimizing Inventory Management Under Partial Swarn Algorithm Using Heptagonal Fuzzy Number

Panel Details:

Day: 1

Session No: 1

Panel No: 14

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. S. Dinesh

Research Scholar | SRM Institute of Science and Technology, Kattankulathur.

Abstract: Unsteady MHD Free Convection of Two-Immiscible Casson Fluids in a Porous Channel: Effects of Concentration, Radiation, and Entropy Generation

Abstract Preview

Unsteady MHD Free Convection of Two-Immiscible Casson Fluids in a Porous Channel: Effects of Concentration, Radiation, and Entropy Generation

S. Dinesh, Department of Mathematics and Statistics, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur , Chengalpattu, India, ds7870@srmist.edu.in

S. Lakshmi Priya, Department of Mathematics and Statistics, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur , Chengalpattu, India, lakshmis3@srmist.edu.in

Abstract

This study numerically investigates the unsteady, two-region immiscible magneto hydrodynamic (MHD) free convective flow of a Casson fluid through a vertical channel filled with a porous medium, now extended to incorporate a concentration profile. The governing nonlinear equations, accounting for velocity and thermal slip conditions, are solved using a perturbation technique. The model includes the effects of porous medium permeability, Joule heating, viscous dissipation, and thermal radiation. To further enhance the physical relevance, mass transfer is introduced via a species concentration field, enabling the study of solutal buoyancy forces alongside thermal effects. The analysis considers several key dimensionless parameters, such as the Hartmann numbers for both fluid regions, Darcy number, permeability ratio, thermal and solutal Grashof numbers, radiation parameter, Prandtl number, Schmidt number, heat source, and, where applicable, the chemical reaction rate. The influence of these parameters on velocity, temperature, and concentration distributions is thoroughly examined. In addition, the study evaluates entropy generation and Bejan number to understand the thermodynamic behavior of the system. Graphical representations are provided to illustrate how these parameters affect the flow dynamics and transport characteristics. The rates of heat and mass transfer are quantified through Nusselt and Sherwood numbers, respectively, while skin friction and flow rate values are presented in tabular format. All results are analyzed and visualized using MATLAB software to ensure clarity and precision in interpreting the physical behavior of the flow system.

Keywords: MHDHartmann numberCasson fluidDarcy numberBejan number

Panel Details:

Day: 1

Session No: 1

Panel No: 12

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. M Mukil

Research Scholar | Vellore Institute of Technology, Chennai Campus

Abstract: Alleviate Latency In Electronic Trading Under Uncertainty: A Neutrosophic Fuzzy Working Vacation Queueing Model

Abstract Preview

Alleviate Latency In Electronic Trading Under Uncertainty: A Neutrosophic Fuzzy Working Vacation Queueing Model

M Mukil, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, mukil.2020@vitstudent.ac.in

V. Vidhya, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, vidhya.v@vit.ac.in

Abstract

Advancements in technology enable an individual to trade through cloud systems. This opens an opportunity for many individuals to participate in the market investments. On the other hand, risk factors are increasing due to the involvement of many traders, which in turn may cause issues like system throttling and network traffic congestion. Cloud-based servers should build a high-frequency trading system that synchronizes clocks across instances to meet the regulatory demand. Market efficiency is determined by bid-ask price. Faster systems help to keep this price small, which makes the market more efficient. The time delay between placing the ask or sell order and the actual time of execution of the order by the exchange or brokerage is latency, and it is measured in milliseconds/microseconds. The lower the latency, the faster the system will make the market productive. To address the latency in cloud-based market trading, the order request, its execution, and the varied execution rate due to network maintenance or congestion are modelled as a single-server working vacation Markovian queueing model. Factors like network jitters, unpredictable market fluxes may escalate latency, thus the queueing model is framed in the neutrosophic fuzzy background. This will provide the information on the possible profit and loss percentage of trading in a certain period. Traders can avoid high losses, and exchanges/ brokers may take the necessary actions to eliminate technical and hardware issues.

Keywords: Electronic tradingLatencyWorking vacationNeutrosophic Fuzzy set

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. Kaleeswari M

Research scholar | The Gandhigram Rural Institute (Deemed to be University), Gandhigram, India

Abstract: A Surrogate-Assisted NSGA-II Approach for Optimal Control of Biostatisticians Professional Competency

Abstract Preview

A Surrogate-Assisted NSGA-II Approach for Optimal Control of Biostatisticians Professional Competency

Kaleeswari Murugan, Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India, kaleeswaris20@gmail.com

David R Katerere, Pharmaceutical & Biotech Advancement in Africa (PBA2), Department of Pharmaceutical Sciences, Tshwane University of Technology, Arcadia Campus, Pretoria, South Africa, KaterereDR@tut.ac.za

Muthukumar Palanisamy, Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India, pmuthukumargri@gmail.com

Abstract

Biostatistics plays an important role in designing, analyzing, and interpreting data in the life sciences. Biostatisticians are crucial in clinical trials, systematic reviews and meta-analysis, observational and complex interventional studies, and statistical genetics. However, due to differences in education, institutional context, and workforce training opportunities, the professional competency of biostatisticians is not homogeneous. To address this, we demonstrate a novel optimal control framework incorporating synthetic data generation, multi-objective optimization, and surrogate-assisted evolutionary algorithms to analyze and improve the bio-statistical competency. In the absence of empirical data, we generated a synthetic dataset that demonstrates professional competencies using a Conditional Tabular Generative Adversarial Network (CTGAN). Although real-world workforce development issues are complex, nonlinear, and include competing objectives, we utilized a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve for a bi-objective optimization of maximizing professional competency and minimizing intervention costs. Using the Pareto optimal front, we can provide a visual context to examine the trade-off of the effect of improving competency against available resources. To further improve efficiency and reduce computational expense, we incorporated a Random Forest surrogate model into the NSGA-II to approximate the objective functions and improve the explored solutions in the solution space. The comparison indicates that the surrogate-assisted method found near-optimal solutions, with reduced evaluation cost and improved convergence over regular NSGA-II. These results demonstrate how training, resource allocation, and competency development pathways for biostatisticians can be informed by AI-driven optimal control approaches. Apart from methodological contributions, this framework provides a basis for future empirical validation and evidence-based workforce planning in health sciences.

Keywords: BiostatisticiansCompetencyOptimal controlNSGA-IIPareto optimal frontRandom Forest surrogate model

Panel Details:

Day: 1

Session No: 1

Panel No: 14

Time: 3:45 PM - 6:00 PM

Venue: N/A

Dr. P.PUSHPA

ASSOCIATE PROFESSOR | St.Peters Institute of Higher Education and Research

Abstract: Stochastic Time Series modelling for Foreign Tourist Arrivals in India

Panel Details:

Day: 1

Session No: 1

Panel No: 16

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. RAJAKUMARI RAMMOORTHI

RESEARCH SCHOLAR | VELLORE INSTITUTE OF TECHNOLOGY, CHENNAI CAMPUS.

Abstract: Modeling and heat transport analysis of a time-dependent convection in a Williamson fluid over a permeable cylinder with slip effects

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Modeling and heat transport analysis of a time-dependent convection in a Williamson fluid over a permeable cylinder with slip effects

Dhivya Mohanavel, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, India., dhivya.m@vit.ac.in

Rajakumari Rammoorthi, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, India., rajakumari.r2021@vitstudent.ac.in

Abstract

This work explores the natural convection flow of a Williamson-type non-Newtonian fluid over a moving, permeable cylindrical surface subjected to a constant heat flux. The fluid under consideration exhibits both shear-thinning and shear-thickening characteristics. The governing equations describing the momentum and heat transfer processes are formulated and numer-ically solved using an implicit finite difference method, applying constant heat flux conditions at the boundary. The influence of key physical parameters—namely the Weissenberg number, Reynolds number, Grashof num-ber, Prandtl number, and permeability factor—on the flow and thermal profiles is thoroughly examined. The findings reveal that an increase in the Weissenberg number induces a reduction in fluid velocity at the time of enhancement in temperature field. Additionally, greater permeability at the cylindrical boundary al-lows more fluid to pass through the surface, thereby improving thermal transport efficiency. To evaluate the sensitivity of the velocity gradient to the Weissen-berg number and permeability parameter, Response Surface Methodology (RSM) is utilized. The accuracy of the numerical results is further corroborated by comparison with experimental data

Keywords: Williamson fluidcylinderconstant heat fluxporous mediumheat transfer

Panel Details:

Day: 1

Session No: 1

Panel No: 13

Time: 3:45 PM - 6:00 PM

Venue: N/A

Dr. Adarsh S

Professor | TKM College of Engineering Kollam

Abstract: Complexity analysis of Meteorological Drought across the Indian subdivisions of contrasting climate using Recurrence Theory

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Complexity analysis of Meteorological Drought across the Indian subdivisions of contrasting climate using Recurrence Theory

Naishana N, TKM College of Engineering Kollam, naishanan14@gmail.com

Adarsh S, TKM College of Engineering Kollam, adarsh_lce@yahoo.co.in

Ameena N, TKM College of Engineering Kollam, 14ameenan@gmail.com

Aami S Nath, TKM College of Engineering Kollam, 230246@tkmce.ac.in

Fathima Nidha CT, TKM College of Engineering Kollam, nidhactfathima@gmail.com

Abstract

This study proposes the application of Recurrence theory for analyzing the complexity of Meteorological Drought across six rainfall homogeneous subdivisions in India. Using the longest available records of subdivisional scale rainfall data of $1871-2016$ the standardized precipitation Inex (SPI) indicator is computed firstly, for diverse time scales of $3, 6$ and $12$ months representing short, medium and long-term droughts. Then Recurrence theory is applied for developing recurrence matrix after finding the optimal embedding dimension (ED), time delay (TD) and recurrence threshold. The TD and ED requirements are found to be more for droughts of larger time scale. The recurrence matrices of each SPI series are interpreted using Recurrence Quantification Measures (RQMs) in the form of Recurrence Rate (RR), Determinism (DET), Laminarity (LAM), Entropy (ENT) and mean Diagonal Length (L). The analysis shown that DET, LAM and ENT of SPI increase with increase in time scale. It is noted that the complexity in terms of LAM is more for wet regions like Assam-Meghalaya (AM) and Kerala on comparing with the arid and semi-arid and drought prone subdivisions of Chattisgarh, West Rajasthan, Vidarbha and Marathwada. It is also noted that long-term drought (SPI of $12$-month scale) is more deterministic than short to medium scale droughts irrespective of the subdivisions. Moreover, the SPI series of drought prone regions are more deterministic than the SPIs of wet regions irrespective of the time scales. The useful insights gained from recurrence analysis and RQMs are helpful for developing appropriate forecasting models of drought of contrasting climatic zones.

Keywords: DroughtSubdivisionsClimateSPIRecurrence

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. Raveena R

Research Scholar | VELLORE INSTITUTE OF TECHNOLOGY, CHENNAI

Abstract: A Novel Risk-Aware Supplier Selection Strategy for Hospital Procurement using CRITIC, PIV, and MAUT

Abstract Preview

A Novel Risk-Aware Supplier Selection Strategy for Hospital Procurement using CRITIC, PIV, and MAUT

Raveena R, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India, raveena.r2023@vitstudent.ac.in

Umamaheswari S, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India, umamaheswari.suk@vit.ac.in

Abstract

Hospital supplier selection is a critical decision-making process, as the availability of essential medical supplies directly influences patient care, treatment outcomes, and the resilience of healthcare systems. Infirmaries must manage suppliers under stringent conditions of time sensitivity, quality assurance, and risk mitigation. To capture these dimensions, criteria such as sustainability, quality, risk management, and operational performance are incorporated, reflecting the multifaceted requirements of hospital procurement. This research employs the CRITIC method to assess the relative importance of criteria, applies the PIV method to rank supplier performance, and utilizes MAUT to transform scores into utility values for optimized allocation of procurement orders among suppliers. The integrated framework is novel in its ability to capture interdependencies, provide rankings, and support effective procurement decisions. The proposed methodology enhances procurement efficiency, reduces operational risks, and safeguards continuity of critical healthcare services, offering decision-makers a robust tool to strengthen hospital procurement and improve patient care.

Keywords: CRITICHealthcare Procurement criteriaHospital Supplier SelectionMulti-attribute utility theoryProximity index value

Panel Details:

Day: 1

Session No: 1

Panel No: 5

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 710

Ms. Reshma R

Research Scholar | Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India.

Abstract: Mittag-Leffler Stabilization of Fractional-Order Singular Time-Delay Systems using Hybrid Controller

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. Dhivya Balaraman

Research Scholar | Vellore Institute of Technology, Chennai Campus, Tamil Nadu

Abstract: Numerical Study and Sensitivity Analysis of Casson Hybrid Nanofluid Flow in a Rotating Channel with Riga Plates

Abstract Preview

Numerical Study and Sensitivity Analysis of Casson Hybrid Nanofluid Flow in a Rotating Channel with Riga Plates

Dhivya Balaraman, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, dhivya.b2023@vitstudent.ac.in

Dhivya Mohanavel, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, dhivya.m@vit.ac.in

Abstract

The current study examines the mathematical analysis of Casson hybrid nanofluid flow, utilizing water as the base fluid, and investigates the effects of nanoparticles on two parallel Riga plates, which promote effective flow control and enhanced heat transfer efficiency in solar collector applications. The lower plate is postulated to be stretched and have transient fluid flow attributes. The surface of the moving upper Riga plate is subject to induced squeezing characteristics. The set of governing PDEs, concurrently with the boundary conditions, is restructured into ODEs employing similarity transformations. The transformed equations are evaluated numerically for axial $f'(\eta)$ and transverse $g(\eta)$ velocities and temperature $\theta(\eta)$ via the shooting method. Graphs illustrate and scrutinize the significant results of distinguished parameters on fluid dispersions. The obtained results demonstrate that by enhancing the value of rotation, squeezing, and modified Hartmann number parameters, the velocity profile exhibits dual behavior. Moreover, escalating the value of the radiation parameter elevates the temperature profile. Additionally, sensitivity analysis is employed to identify the effects of the skin friction rate on the parameters: rotation, magnetic, and squeezing. The skin friction rate is observed to have the greatest impact on the squeezing parameter. This study also verified existing findings and demonstrated a satisfactory level of consistency.

Keywords: Riga platesCasson fluidModified Hartmann numberRotating channelHybrid nanofluid

Panel Details:

Day: 1

Session No: 1

Panel No: 3

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 708

Ms. Umadevi G

Research Scholar | Vellore Institute of Technology Chennai

Abstract: Freshness-Driven Sustainable Pharmaceutical Inventory Optimization using DRL and Meta-Heuristics under Random Lead Time and Emission-Aware Preservation

Abstract Preview

Freshness-Driven Sustainable Pharmaceutical Inventory Optimization using DRL and Meta-Heuristics under Random Lead Time and Emission-Aware Preservation

Umadevi G, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India, umadevi.g2022@vitstudent.ac.in

Umamaheswari S, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India, umamaheswari.suk@vit.ac.in

Sai Kumar M, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India, saikumar.m2022@vitstudent.ac.in

Sheng-Pen Wang, Department of Industrial and Business Management, College of Management Chang Gung University, Taiwan, wangsp@mail.cgu.edu.tw

Abstract

The sustainable management of perishable pharmaceuticals presents a critical challenge for pharmacy retailers, who must simultaneously address the distinct characteristics of prescription (Rx) and over-the-counter (OTC) drugs, both of which exhibit freshness-dependent demand, albeit with differing sensitivity levels influenced by regulatory mandates and consumer health preferences. This study proposes a hybrid decision-support framework that integrates analytical modeling, meta-heuristic optimization, and deep reinforcement learning (DRL) to jointly optimize inventory efficiency and sustainability under uncertainty. The inventory system is modeled with freshness-sensitive demand, stochastic lead time determined by fill rate service levels, and deterioration governed by a three-parameter Weibull distribution. Preservation investment is incorporated to decelerate temperature-induced degradation, while its environmental impact, quantified via carbon emissions and associated tax costs, is explicitly captured. The resulting nonlinear model, formulated through governing differential equations, exhibits pseudo-convexity with respect to key decision variables, namely order lot-size, lead time, preservation investment, and service investment. To address analytical intractability, advanced meta-heuristic algorithms are employed to approximate near-optimal solutions and benchmark them against conventional methods. In parallel, a DRL-based policy learning framework is developed to facilitate adaptive, data-driven decision making under real-time operational variability. Extensive numerical experiments using primary data validate the effectiveness of both approaches. Furthermore, a comprehensive sensitivity analysis is conducted on key exogenous parameters, including deterioration characteristics and carbon taxation rates, to assess their impact on the optimal values of preservation investment, lead time, service investment, ordering quantity, and the total inventory cost. The results demonstrate that the integrated framework not only reduces total inventory cost but also enhances sustainability by balancing service quality, environmental impact, and economic efficiency. This dual-methodology provides a rigorous, scalable, and adaptive paradigm for managing perishable pharmaceutical inventories under complex, uncertain environments.

Keywords: Carbon emissionDeep reinforcement learningFreshness-sensitive demandMeta-heuristicsPreservation investmentRandom lead timeWeibull distribution

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Ms. S ARCHANA

RESEARCH SCHOLAR | PACHAIYAPPAS COLLEGE CHENNAI 30

Abstract: Oscillation results for second order non-linear differential equations with non-monotone delays

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

Ms. K. JEYASHRI

Research Associate | VIT - Chennai

Abstract: Heat transfer analysis of MHD hybrid nanofluid in a porous trapezoidal cavity with heated fin

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Heat transfer analysis of MHD hybrid nanofluid in a porous trapezoidal cavity with heated fin

K. Jeyashri, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, jeyashri.2022@vitstudent.ac.in

V. Prabhakar, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, prabhakar.v@vit.ac.in

Abstract

This study investigates natural convection flow and heat transfer characteristics of magnetohydrodynamics (MHD) tetra-hybrid nanofluid in a porous finned trapezoidal cavity with the inclusion of thermal radiation and constant magnetic field ($B_0$). The cavity, which is partially heated from its bottom wall and cooled from its slanted left and right walls, contains a heated fin at the center. The tetra-hybrid nanofluid was made up of copper ($Cu$), silver ($Ag$), alumina ($Al_2$$O_3$), and magnetite ($Fe_3$$O_4$) nanoparticles suspended in water. The governing partial differential equations are numerically solved using the Galerkin finite element method. The computational technique is validated through systematic comparisons with established results. The outcomes indicate that the fluid circulation contours, temperature profile, and local entropy generation are notably affected by the input parameters such as the Rayleigh number, Hartmann number, Darcy number, radiation parameter, and fin length for a nanoparticle volume fraction of $5\%$. The results reveal that increasing the Darcy number enhances the local Nusselt number, temperature distribution, and flow intensity. A higher heat generation/absorption coefficient and radiation parameter further improve the isotherm patterns and local heat transfer. In addition, variations in fin length and thickness are found to strongly influence fluid circulation and thermal distribution within the cavity. These findings highlight the potential of tetra-hybrid nanofluids with magnetic and radiation effects in porous finned trapezoidal cavities for advanced thermal management applications in the energy, electronics, and process industries.

Keywords: Natural convectionMHDThermal RadiationPorous mediumTetra-hybrid nanofluidTrapezoidal cavityHeated fin

Panel Details:

Day: 1

Session No: 1

Panel No: 3

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 708

Ms. ANTHUVAN EZHILARASI P

RESEARCH SCHOLAR | VIT-CHENNAI

Abstract: Darcy–Forchheimer Casson hybrid nanofluid flow in a stenotic artery with the effects of thermophoresis and Brownian motion

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Darcy–Forchheimer Casson hybrid nanofluid flow in a stenotic artery with the effects of thermophoresis and Brownian motion

Anthuvan Ezhilarasi P, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, anthuvan.ezhilarasi2022@vitstudent.ac.in

Dhivya Mohanavel, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, dhivya.m@vit.ac.in

Abstract

Research into the flow of blood through a narrowed artery is crucial in the field of fluid dynamics due to its tremendous importance in biomedical science. By considering these aspects, the current study aims to investigate the theoretical analysis of the behaviour of Casson hybrid nanofluid flow $(\text{Au}/\text{Al}_{2}\text{O}_{3})$ in a stenotic artery, examining the repercussions of Darcy–Forchheimer, thermophoresis, and Brownian motion. Variable viscosity and thermal conductivity have also been used to analyse the influence of flow, heat, and mass transfer. The similarity transformation method leads to ODEs from the governing coupled non-linear set of PDEs. The collocation method is used to build a mathematical model and solve it numerically using a MATLAB solver. Furthermore, the results for velocity and temperature of the nanofluid and hybrid nanofluid cases are presented graphically. The attained results are corroborated by their consistency with previously published findings. It's pertinent to note that the temperature profile rises with growing Brownian motion and thermophoresis parameter values. In contrast, the concentration profile declines as values of the thermophoresis parameter improve.

Keywords: Casson hybrid nanofluidStenotic arteryBrownian motionthermophoresisvariable viscosity

Panel Details:

Day: 1

Session No: 1

Panel No: 3

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 708

Dr. Sandeep Kumar

Post Doctoral Fellow | Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India

Abstract: Linear Stability Analysis of Light-Induced Bioconvection in a Stress-Free Fluid Layer

Abstract Preview

Linear Stability Analysis of Light-Induced Bioconvection in a Stress-Free Fluid Layer

Sandeep Kumar, Fluid Flow Systems Simulation Lab, Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, India, sandeepkumar1.00123@gmail.com

Suneet Singh, Fluid Flow Systems Simulation Lab, Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, India, suneet.singh@iitb.ac.in

Abstract

Phototactic bioconvection arises when light-sensitive microorganisms respond collectively to directed illumination, giving rise to fluid instabilities and convective motion. The present study focuses on the linear stability of such a system when the top surface of the horizontal fluid layer is exposed to a collimated light beam incident from the light source. Both boundaries of the layer are assumed to be stress-free. The governing equations are linearized about the quiescent state, and the resulting eigenvalue problem is solved using a normal-mode formulation coupled with the MATLAB routine bvp4c. The analysis demonstrates that boundary conditions exert a marked influence on stability behavior. In particular, the case with stress-free boundaries at both surfaces exhibits the lowest critical threshold, indicating that the suspension destabilizes earlier than in configurations with one or two rigid boundaries. This enhanced tendency toward instability is linked to the absence of viscous damping at the bounding surfaces, which allows perturbations to amplify more readily. The microorganism concentration fields provide further physical insight. With increasing critical total intensity, the position of the peak concentration gradually shifts toward the upper surface. This migration strengthens density gradients near the top layer and consequently accelerates the onset of convection.

Keywords: Phototacticbioconvectionalgae cellsmicroorganismslinear stability analysisstress-free surfaceNavier-Stokes equations.

Panel Details:

Day: 1

Session No: 1

Panel No: 3

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 708

Mr. Avigyan Saha

Research Scholar | Indian Institute of Technology Bombay

Abstract: Mathematical Modelling and Bifurcation Analysis of Nonlinear Dynamics in Heterogeneous Catalysis

Abstract Preview

Mathematical Modelling and Bifurcation Analysis of Nonlinear Dynamics in Heterogeneous Catalysis

Avigyan Saha, Indian Institute of Technology Bombay, avigyan.saha@iitb.ac.in

Suneet Singh, Indian Institute of Technology Bombay, suneet.singh@iitb.ac.in

Abstract

Mathematical modelling is an indispensable tool for elucidating the intricate dynamics governing chemical reaction systems. Given the inherent nonlinearity of most chemical processes, analytical solutions to the governing differential equations remain elusive. Consequently, computational and numerical approaches serve as cornerstones in understanding and predicting reaction behavior. Heterogeneous catalytic reactions exhibit an astonishingly rich array of dynamical behaviors, including multistability, oscillatory regimes, and chaos, necessitating an in-depth comprehension of the underlying physical and chemical interactions. To rigorously investigate these complex dynamics, a reduced-order mathematical model is formulated, comprising a system of nonlinear ordinary differential equations. We conduct a comprehensive investigation of various bifurcation phenomena by employing phase plane analysis and nullcline plotting in codimension-one framework using MATLAB. Additionally, we rigorously establish the existence and stability of equilibrium points, providing deeper insights into the system's dynamical behavior. Numerical simulations are used to demonstrate different types of oscillations and chaos in the kinetic model of heterogeneous catalytic reactions.

Keywords: Heterogeneous CatalysisBifurcationsKinetic ModelChaosLimit Cycle

Panel Details:

Day: 1

Session No: 1

Panel No: 4

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 709

Mr. Thirugnanasambanthamoorthi M

Research Scholar | Central University of Tamil Nadu, Thiruvarur

Abstract: Design and Analysis of a Meta-Heuristic Approach for Project Schedule Optimization Integrating Genetic Algorithm with CPMPERT for Enhanced Scheduling System

Abstract Preview

Design and Analysis of a Meta-Heuristic Approach for Project Schedule Optimization Integrating Genetic Algorithm with CPMPERT for Enhanced Scheduling System

Thirugnanasambanthamoorthi, Central University of Tamil Nadu, Thiruvarur, India, thiruvitru@gmail.com

C. Vijayalakshmi, Central University of Tamil Nadu, Thiruvarur, India, vijayalakshmi@cutn.ac.in

Abstract

Project scheduling is a critical component of effective project management, directly influencing the timely and cost-effective completion of complex endeavours. Traditional deterministic techniques like the Critical Path Method (CPM) and the Probabilistic Evaluation and Review Technique (PERT) are widely used to identify the longest path of tasks and estimate project duration. However, these methods often operate under simplifying assumptions, such as unlimited resources and fixed task durations, which can limit their realism and optimization capabilities in dynamic environments. This study investigates the application of a metaheuristic Evolutionary Algorithm (EA), specifically a Genetic Algorithm (GA), to solve the project scheduling problem for the development of a "PC Cart" system. The project network, comprising 13 interrelated activities, was first analysed using CPM to establish a baseline duration of 21 weeks and identify a critical path. The GA was then employed to evolve a population of candidate schedules, optimizing for the minimization of total project duration. The algorithm's performance was evaluated based on its convergence speed and solution quality. Results demonstrate that the GA not only consistently converges to the known CPM optimum but also provides a robust framework capable of incorporating additional real-world constraints—such as resource allocation, multi-skilled crews, and time-cost trade-offs—that are beyond the scope of classical CPM/PERT. This research concludes that evolutionary algorithms offer a superior, flexible, and powerful alternative for modern project scheduling, enabling managers to discover more efficient and resilient project plans.

Keywords: Project SchedulingCritical Path Method (CPM)PERTGenetic AlgorithmEvolutionary AlgorithmsOptimizationMetaheuristicProject Management

Panel Details:

Day: 1

Session No: 1

Panel No: 14

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. SARATHKUMAR S

RESEARCH SCHOLAR | PACHAIYAPPAS COLLEGE CHENNAI 30

Abstract: Oscillation results for Second Order nonlinear Advanced Differential Equations

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

Mr. Pratip Dalal

Research Scholar | IIT Bombay

Abstract: Slow Fast Dynamics in Formic Acid Based Electro Oxidation Reaction

Abstract Preview

Slow Fast Dynamics in Formic Acid Based Electro Oxidation Reaction

Pratip Dalal, Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Maharashtra, India, pratip.dalal@iitb.ac.in

Suneet Singh, Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Maharashtra, India, suneet.singh@iitb.ac.in

Abstract

Formic acid electro-oxidation reaction (FAEOR) refers to the process in which formic acid (HCOOH) is oxidized electrochemically electrode surface, typically a platinum catalyst, to produce carbon dioxide ($CO_2$), protons ($H^+$), and electrons ($e^-$) three possible pathways: (i) formate, (ii) indirect, and (iii) direct pathway. This reaction is of great interest in the field of fuel cells, especially Direct Formic Acid Fuel Cells (DFAFCs), because of its high energy efficiency, low operating temperature, and low environmental impact. During the electro-oxidation reaction, formic acid is oxidized to $CO_2$ through the adsorption and oxidation of carbon monoxide and the adsorption of $H_2O$ on the catalyst surface. Although the potential difference of each reaction step provides qualitative and quantitative changes in the FAEOR dynamics, in this work, we have only considered the potential difference due to carbon monoxide oxidation and water adsorption on the electrode surface. Hence, these two potential differences are used as bifurcation parameters. The eigenvalue analysis with the change of the bifurcation parameter reveals that the system has multiple timescales due to the broad difference in magnitude of the real parts. Fast Fourier Transform (FFT) also guarantees the presence of multiple well-separated peaks, characterizing the slow-fast behavior in the phase space.

Keywords: Bifurcation AnalysisElectrochemical ReactionMultiple time scalesNonlinear Dynamics

Panel Details:

Day: 2

Session No: 1

Panel No: 2

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 710

Ms. BOURNA JOTHI E

Research Scholar | Vellore Institute of Technology, Chennai- 600 127

Abstract: Exponential Stability of impulse fractional order systems with Markovian Jump Parameters

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

Ms. R.priyadharshani

Research Associate | VIT Chennai

Abstract: Edge-Preserving Image Enhancement Using Fractional-Order Derivative Filters

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

Ms. S MAHALAKSHMI

Research Associate | VIT Chennai

Abstract: Deep Learning Models for Brain Tumor Classification using Magnetic Resonance Imaging: Comprehensive Review

Abstract Preview

Deep Learning Models for Brain Tumor Classification using Magnetic Resonance Imaging: Comprehensive Review

Mahalakshmi S, Vellore Institute of Technology, Chennai, mahalakshmi.s2023d@vitstudent.ac.in

Ranjeet Kumar, Vellore Institute of Technology, Chennai, ranjeet.k@vit.ac.in

Abstract

Detecting and classifying brain tumors through Magnetic Resonance Imaging (MRI) has become a significant focus in research, as timely and precise diagnosis greatly influences patient outcomes. Deep learning (DL) techniques—particularly Convolutional Neural Networks (CNNs) and transfer learning—have achieved top performance on publicly accessible datasets, frequently outperforming traditional methods reliant on handcrafted features. This review compiles insights from thirty significant studies based on deep learning techniques and categorizes approaches into CNNs, transfer learning, recurrent networks (RNN/LSTM), auto-encoders, attention/transformers, classical ANNs, and hybrid systems, while also summarizing the datasets employed, preprocessing methods, augmentation strategies, evaluation metrics, and performance ranges. The review highlights practical challenges such as the limited availability and challenges datasets and class imbalance, variations across multicenter data, the need for model interpretability and calibration, and computational limitations for clinical use. Different methods performed an exceptional accuracy rate in the detection/classification between $94 - 99 \%$ subject to datasets. In this paper, a detailed review is presented on the study to explore the future possibility in healthcare research.

Keywords: Brain TumorDeep LearningMRICNNRNN and LSTM

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Ms. Monica T

Assistant Professor | Immaculate College for Women, Cuddalore.

Abstract: QSPR modeling with topological indices of some antibiotic and related drugs

Panel Details:

Day: 1

Session No: 1

Panel No: 17

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Lakshana R

Research Scholar | Vellore Institute or Technology, Chennai

Abstract: Enhanced Analysis of Skin friction and Heat transfer on MHD Burgers fluid under Fuzzified Nonlinear Radiation Effects through $\alpha$-cut approach

Abstract Preview

Enhanced Analysis of Skin friction and Heat transfer on MHD Burgers fluid under Fuzzified Nonlinear Radiation Effects through $\alpha$-cut approach

Lakshana R, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, lakshana.r2023@vitstudent.ac.in

David Maxim Gururaj A, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, davidmaxim.gururaj@vit.ac.in

Abstract

This study investigates the two-dimensional magnetohydrodynamic (MHD) flow of Burgers' fluid over a stretching surface in the presence of fuzzified nonlinear thermal radiation. The novelty of the present work lies in considering the nonlinear radiation parameter as a triangular fuzzy number (TFN) and analyzing its impact on flow characteristics using the $\alpha$-cut approach. The governing nonlinear partial differential equations (PDEs) defining momentum and energy flow are transformed into ordinary differential equations (ODEs) via similarity transformations. The ODEs are converted into FDEs using the $\alpha$-cut approach and numerically solved in MATLAB. This work provides deeper insights into the fluctuations in skin friction coefficient and heat transfer rate under uncertain thermal conditions by fuzzifying the radiation parameter across its appropriate range. The findings show that fuzzy modeling not only effectively captures parametric uncertainty, but also reveals significant changes in the thermal boundary layer and radiative heat transfer. The proposed approach develops a robust framework for dealing with nonlinear and unpredictable radiative effects in complicated non-Newtonian fluid flows, with practical applications in industrial thermal processes and energy systems.

Keywords: MagentohydrodynamicsBurgers FluidHeat transferSkin frictionFuzzificationTriangular fuzzy numbers$\alpha$-cut approach

Panel Details:

Day: 2

Session No: 1

Panel No: 2

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 710

Mr. Neelagandan P

Research Scholar | Vellore Institute of Technology

Abstract: Optimal Charger Deployment for Q-Coverage using Nature-Inspired Algorithms in Wireless Sensor Networks

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Dr. Yugesh S

Assistant Professor | Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam -603 110

Abstract: Sampling and Reconstruction Over Subspaces of Multiply Generated Spline Spaces of Polynomial Growth

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Mr. Sam Jeba Kumar J

Research Associate | Vellore Institute of Technology, Chennai

Abstract: Metaheuristic-Based Optimal Charger Placement for Q-Coverage in Wireless Sensor Networks Using a Hybrid Kookaburra-Osprey Strategy

Abstract Preview

Metaheuristic-Based Optimal Charger Placement for Q-Coverage in Wireless Sensor Networks Using a Hybrid Kookaburra-Osprey Strategy

Sam Jeba Kumar John Jothi, Vellore Institute of Technology, Chennai, samjeba.kumarj2022@vitstudent.ac.in

S. Balaji, Vellore Institute of Technology, Chennai, s.balaji@vit.ac.in

Abstract

Wireless Sensor Networks (WSNs) installed in hostile or hard-to-reach areas usually experience severe challenges with network longevity and sensing reliability because of meager energy supplies. Wireless chargers have been identified as a potential solution for refilling sensor energy without human assistance, but optimal charger placement is a very intricate problem, particularly if sensors need to be covered by multiple chargers to meet high Q-coverage requirements. This paper proposes a Hybrid Kookaburra–Osprey Optimization Algorithm (HKOOA) to optimally place wireless chargers to cover as many Q-covered sensors as possible in an arbitrarily deployed WSNs. The hybrid algorithm combines the exploratory nature of kookaburras and the exploitative diving strategy of ospreys, achieving an optimal balance between global search and local intensification. Large-scale WSNs in adversarial environments with different charger capacities, sensor deployments, and Q-coverage requirements are simulated extensively. Experiments show that the proposed HKOOA – $94.4\%$ outperforms other state-of-the-art metaheuristic algorithms such as Artificial Bee Colony – $87.2\%$ (ABC), Quokka Optimization Algorithm – $81.9\%$(QOA), Whale Optimization Algorithm – $86.8\%$ (WOA), and Dwarf Mongoose Optimization Algorithm – $83.3\%$ (DMOA) in convergence rate and ratio of Q-covered sensors. The results identify the capability of HKOOA as an effective and dependable method for energy sustainability and robust WSNs operation in extreme conditions.

Keywords: Wireless Sensor NetworksQ- CoverageHybrid Kookaburra–Osprey Optimization AlgorithmOptimal Charger Placement

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Mr. Kalidoss B

Teaching Fellow | CEG, Anna University, Chennai - 600 025

Abstract: Variational approach to MHD flow towards a stretching sheet with suction/injection, viscous dissipation and heat flux

Abstract Preview

Variational approach to MHD flow towards a stretching sheet with suction/injection, viscous dissipation and heat flux

M. Chandrasekar, Anna University, mcsekar65@gmail.com

B. Kalidoss, Anna University, bkdoss67@gmail.com

Abstract

The physical problem of steady, laminar, two dimensional MHD flow over a stretching sheet with suction/injection, viscous dissipation and heat flux is mathematically modelled as a simultaneous system of non-linear partial differential equations. To achieve the solution for the consider problem numerically, the Gyarmati's variational principle on the basis of non-equilibrium thermodynamics processes in the theory of continua is adopted. To estimate the transportation fluid fields inside the boundary layer, the appropriate trial polynomial functions have been employed and functionals for the integral variational principle are determined. The Euler-Lagrange equations of the functional are obtained as a system of polynomials involving boundary layer thicknesses of momentum and temperature. The expressions of shear stress, Nusselt number have been derived and the effects of various physical factors involved in the problem are explored. The obtained results are compared with previously published results in the literature, to confirm the validity of the solution procedure. The results depict the significant properties of suction/injection, viscous dissipation and heat flux. This classical variational principle permits hydrodynamic engineers and scientists to discuss the laminar MHD fluid flow and heat transfer problems efficiently and effectively. This variational principle presents a significant advantage over the conventional numerical methods which typically require more extensive calculations.

Keywords: MHD fluid flowVariational principleStretching sheetHeat transferViscous dissipationSuction and Injection

Panel Details:

Day: 1

Session No: 1

Panel No: 13

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Srija R

Research Scholar | Vellore Institute of Technology - Chennai Campus

Abstract: Instability of the swirling radial electric field on heat and mass transfer in Walter-B viscoelastic and viscous fluids

Abstract Preview

Instability of the swirling radial electric field on heat and mass transfer in Walter-B viscoelastic and viscous fluids

Srija R, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, srija.r2021@vitstudent.ac.in

Abhishek Kumar Singh, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, abhishekkumar.singh@vit.ac.in

Abstract

The purpose of this study is to analyse the influence of heat and mass transport on capillary processes at the interface of viscoelastic and viscous fluids. When these processes are interrupted, it may lead to the formation of wave-like patterns and induce instability. The modified equations provide a formula for the quadratic growth rate, which is numerically assessed under the premise of capillary instability while neglecting gravity and fluid flow velocity. Heat and mass transfer both make stability better. On the other side, rotation makes stability even better. The centrifuge number keeps the interface from becoming bigger, which makes it more stable. The Weber number and the density ratio both help make the interface more stable. The perturbation equations derived from potential flow theory and the Walter-B fluid model are used to compute a quadratic growth rate that has undergone numerical validation. This work is done to make spinning more stable and to make heat and mass transmission more stable. Changing certain permittivity-to-conductivity ratios to a radial electric field will cause expansion, decay, or perturbation, and this exposure will have a double impact. The combination of electric fields, rotational effects, and heat transfer makes it feasible to do many things, such as move materials more efficiently, manage fluids at the microscale with precision, and provide medications in a way that is best for each person. By synchronising their movements, these two aspects make it easier to control the fluid's properties and surface shape with greater accuracy and productivity. Capillary instability occurs when liquid jets break apart, droplets develop in inkjet printing, thin liquid films behave differently in microgravity, and other related things happen. This lack of stability may have an effect on both natural and industrial settings.

Keywords: Walter-B viscoelasticRadial electric fieldQuasi-Static approximation

Panel Details:

Day: 1

Session No: 1

Panel No: 13

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Angel Priya E

Research Scholar | SRM Institute of Science and Technology

Abstract: Investigation on Unsteady Dissipative MHD Oscillatory Casson Fluid Flow: Impact of Irreversibility and Entropy Generation.

Abstract Preview

Investigation on Unsteady Dissipative MHD Oscillatory Casson Fluid Flow: Impact of Irreversibility and Entropy Generation.

Angel Priya E, SRM Institute of Science and Technology, Kattankulathur, ae9790@srmist.edu.in

J. Sasikumar, SRM Institute of Science and Technology, Kattankulathur, sasikumj@srmist.edu.in

Abstract

Examining the entropy generation in Casson fluid flows is essential for improving thermal performance in biomedical and industrial systems. The Casson Fluid model is describing the behaviour of non-Newtonian fluid and entropy generation analysis is a method used in thermodynamics and heat transfer to quantify the amount of irreversibility occurring within a system. These analyses provide a framework on cooling channels, next generation biomedical devices and energy efficient Heat exchangers where entropy minimization is crucial. The aim of the present study is to inspect the innovation in the analysis of entropy Generation of Casson fluid in oscillatory flow through an asymmetrical wavy channel, considering the effects of Viscous and Ohmic Dissipation. The governing non-dimensional governing equation with the appropriate boundary conditions are solved using homotopy perturbation method. The Perturbation results are compared with the Numerical results using python. The graphical representations of velocity, temperature and volumetric Entropy Generation are presented. Furthermore, the analysis shows that an increase in the Casson parameter enhances the velocity profile and the absence of magnetic field leads to an increase in the fluid velocity.

Keywords: Casson fluid modelMHDporous channeloscillatory flowEntropy Generation

Panel Details:

Day: 2

Session No: 1

Panel No: 2

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 710

Ms. Kaviya K

Research Scholar | Vellore Institute of Technology, Chennai

Abstract: Deterministic and stochastic modeling of predator-prey dynamics under the combined influence of global warming, wind flow, herd behavior, moonlight, and toxicity

Abstract Preview

Deterministic and stochastic modeling of predator-prey dynamics under the combined influence of global warming, wind flow, herd behavior, moonlight, and toxicity

Kaviya K, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, kaviya.k2023@vitstudent.ac.in

Abhishek Kumar Singh, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, abhishekkumar.singh@vit.ac.in

Abstract

Predator-prey dynamics do not rely entirely on biotic factors, but abiotic components significantly shape the system's behavior, stability, and long-term outcomes. In this study, we develop and analyze both deterministic and stochastic formulations of a tri-trophic predator–prey system that incorporates global warming, wind flow, herd behavior, moonlight intensity, and toxicant exposure. Global warming and wind flow are modeled as factors that decrease reproductive rates throughout the levels of the food chain, while herd geometry and moonlight account for adaptive behavioral responses of prey. Furthermore, the model includes the effect of toxicants impacting both intermediate and top predator species. For the deterministic model, we establish boundedness and positivity of solutions, characterize equilibria, and perform local and global stability analyses. A comprehensive bifurcation study reveals the occurrence of Hopf, transcritical, Bogdanov–Takens, and generalized Hopf bifurcations, demonstrating rich dynamical behavior under varying ecological pressures. The stochastic extension captures environmental fluctuations and provides insights into extinction probabilities, persistence conditions, and noise-induced transitions. Sensitivity analysis highlights the ecological parameters that exert the greatest influence on system stability. Collectively, the results underscore how abiotic drivers, in conjunction with stochasticity, fundamentally alter predator–prey interactions, thereby advancing understanding of ecosystem responses under environmental change.

Keywords: BifurcationGlobal warmingHerd behaviorMoonlightToxicityWind flow

Panel Details:

Day: 1

Session No: 1

Panel No: 4

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 709

Ms. S. Kalpana

Research Scholar | St. Joseph's College of Arts and Science(Autonomous), Cuddalore-607001

Abstract: Distance-Hereditary Fuzzy Graphs and their application in theories of mind

Panel Details:

Day: 1

Session No: 1

Panel No: 17

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. Ishitva Kumar

Student | Vellore Institute of Technology Chennai Campus

Abstract: Optimal deployment of chargers in a rechargeable sensor network with k coverage

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Ms. P V Akhila

Student | Vellore Institute of Technology , Chennai Campus

Abstract: IntelliStock: Hybrid Intelligence for Predictive Refill and Smart Shelf Monitoring

Abstract Preview

IntelliStock: Hybrid Intelligence for Predictive Refill and Smart Shelf Monitoring

P V Akhila, Vellore Institute of Technology, Chennai, akhila.pv2024@vitstudent.ac.in

V Prabhakar, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, prabhakar.v@vit.ac.in

Priyadharshini P, Vellore Institute of Technology, Chennai, priyadharshini.p2024@vitstudent.ac.in

Abinaya K M, Vellore Institute of Technology, Chennai, abinaya.km2024@vitstudent.ac.in

Abstract

Retail inventory management is confronted with key challenges in managing customer demand and cost of operations, resulting in stockouts or overstocking as a result of manual checks and past data. In response, we suggest IntelliStock, a hybrid intelligence solution that combines Long Short-Term Memory (LSTM) networks for forecast-based predictive demand and $YOLOv8$ for real-time shelf surveillance. Through examination of past sales trends, LSTM predicts future demand patterns with great precision $(R^2 = 0.72)$, while $YOLOv8$ handles real-time video feeds from cameras to identify and enumerate items on the shelves in real time. The system automatically sends refill notices when inventory falls below specified limits, allowing proactive restocking. This combination obliterates human intervention, lowers operating expenses, and increases customer satisfaction. In implementation with Python, TensorFlow/Keras, and Ultralytics $YOLOv8$, IntelliStock illustrates a scalable, AI-based solution to optimize retail supply chains. This paper is a proof of concept confirms the promise of marrying predictive analytics with computer vision to transform inventory management, setting the stage for further integration with POS/ERP platforms and multi-store deployments.

Keywords: Retail Inventory ManagementDemand ForecastingLSTM NetworksYOLOv8Computer VisionPredictive AnalyticsSupply Chain Optimization

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Mr. Anubhav Verma

Student | Vellore Institute of technology , Chennai

Abstract: Fake news detection using Bert-Based Large language Models (LLMs): A Comparative and Deployable Approach

Abstract Preview

Fake news detection using Bert-Based Large language Models (LLMs): A Comparative and Deployable Approach

Anubhav Verma, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, anubhav.verma2024@vitstudent.ac.in

Kaliyappan M, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, kaliyappan.m@vit.ac.in

Suman Giri, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, suman.giri2024@vitstudent.ac.in

C. G. Venkat Raghav, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, chakka.ganesh2024@vitstudent.ac.in

Abstract

The widespread of misinformation and propaganda news is a serious social problem in the age of digital communication.There is a pressing need for advanced systems that can reliably and automatically detect misleading content since news spreads quickly in online communities.Traditional detection techniques, such as rule based filters or simple machine learning models, struggle to understand the intricate contextual and linguistic cues inherent in propaganda news posts. This paper compare the performance of state of the art transformer based language models, including BERT, DistilBERT, XLNet, and RoBERTa, for classifying fake news. Based on thorough testing, we find that RoBERTa is the best-balanced model, providing enhanced accuracy and computational efficiency that is adequate for real-time implementation. The optimized RoBERTa model obtained an F1 score of $0.93$ and an accuracy of $93.2 \%$. There are performance trade-offs between computational cost and precision. Compared to long-form news stories, certain models generalize well to short-form ones. RoBERTa was the most feasible option for implementation since it offered high accuracy without incurring undue processing costs.To illustrate the practical applicability of our approach, we developed and deployed a lightweight Chrome browser extension powered by the optimised RoBERTa model. The extension scans news headlines directly from web pages and provides instant feedback, labeling content as real or fake with percentage. Tested on benchmark datasets, our system demonstrates high accuracy, low latency, and usability making it an effective solution for curbing the online spread of misinformation.

Keywords: Fake News DetectionRoBERTaTransformer ModelsBERT VariantsNLPReal-Time DetectionBrowser Extension

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Ms. Nageshwari S

Research Associate | Vellore Institute of Technology, Chennai Campus

Abstract: Optimal feedback control for stochastic switched non-autonomous differential inclusion with deviated argument

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. Subhalakshmi M

Research Scholar | Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai - 600127, Tamil Nadu, India

Abstract: Fractal Perturbations in Radial Basis Functions for Improved Interpolation and Prediction with LSTM

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Dr. S.ARUNA

Assistant Prfessor | St. Joseph's College of Engineering

Abstract: Optimizing Drag in Hypersonic Turbulent Flow over Cone- Ogive Shape: The Role of Jet for Various Angle of Attack

Abstract Preview

Optimizing Drag in Hypersonic Turbulent Flow over Cone- Ogive Shape: The Role of Jet for Various Angle of Attack

S. Aruna, Department of Mathematics, St. Joseph's College of Engineering, Chennai, arunas@stjosephs.ac.in

S. V. Anandhi, Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, anandhi@ritrjpm.ac.in

Abstract

Aerodynamic drag reduction is a critical challenge in optimizing the performance of launch vehicles during hypersonic atmospheric ascent. This study investigates the efficacy of jet-based active flow control techniques for drag mitigation by manipulating boundary layer dynamics and shock-wave interactions on axisymmetric launch vehicle geometries. Shear stress transport $( k- \omega)$ based Computational Fluid Dynamics (CFD) model is employed to characterize the influence of steady and pulsed jet injection on flow separation, pressure gradients, and shock structure modulation. Key parameters of jet, including pressure, Mach number, diameter and injection angle are systematically varied to evaluate their impact on drag reduction. The analysis demonstrates that appropriately timed and positioned jet actuation significantly suppresses boundary layer separation, alters shock-boundary layer interactions and reduces total drag coefficients by up $43.7\%$. The minimum drag condition occurs at zero angle of attack for this shape. These results underscore the potential of jet-induced flow control to enhance aerodynamic efficiency, reduce propulsive energy requirements, and increase payload capacity for future launch vehicle designs.

Keywords: DragJetHypersonic flowTurbulence modelAngle of attack

Panel Details:

Day: 1

Session No: 1

Panel No: 13

Time: 3:45 PM - 6:00 PM

Venue: N/A

Dr. C.Yuvarani

Assistant Professor | Dharmamurthi Rao Bahadur Calavala Cunnan Chetty's Hindu College

Abstract: Dynamic Small-World Graphs for Quantum-Resistant Stream Cipher Encryption

Panel Details:

Day: 1

Session No: 1

Panel No: 17

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Divyabala K

Research Scholar | Vellore Institute of Technology Chennai campus, Chennai.

Abstract: Well-posedness and uniqueness of solutions for abstract neutral integro-differential equations with state-dependent delay and deviated argument

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. Keerthana G

Research Scholar | Vellore Institute of Technology - Chennai Campus

Abstract: Optimal Control and Cost Effectiveness Analysis for a Fractional Smoking Model Using Spectral Collocation Method

Abstract Preview

Optimal Control and Cost Effectiveness Analysis for a Fractional Smoking Model Using Spectral Collocation Method

Keerthana G, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, keerthanag.2023@vitstudent.ac.in

Sagithya Thirumalai, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, sagithya.t@vit.ac.in

Abstract

Smoking is a harmful habit that increases the risk of cardiovascular diseases, cancer, asthma, strokes and imposes economic burdens on society. To investigate smoking-related issues among adolescents, a nonlinear fractional-order mathematical model is developed. The model incorporates five compartments: susceptible males, susceptible females, social smokers, chronic smokers, and quitters, to study the transmission dynamics of smoking. Addiction-free and addiction-present equilibrium states are identified, and the basic reproduction number ($R_0$) is derived to assess the spread of smoking behavior. Local and global stability analyses are performed analytically and numerically. Sensitivity analysis, including normalized forward indices and Partial Rank Correlation Coefficients (PRCC), determines the most influential parameters. Computational solutions are obtained using the spectral collocation method with Chebyshev polynomials, and the method’s accuracy is validated via residual error analysis and comparison with standard techniques. The impact of key parameters, such as transmission, recovery, and quit on each compartment is explored through graphical analysis. An optimal control framework with two interventions is proposed to reduce smoking prevalence, accompanied by a cost-effectiveness analysis to evaluate economic feasibility. The results highlight the need for effective, cost-efficient public health strategies to mitigate tobacco use.

Keywords: Fractional smoking modelStability analysisSensitivity analysisOptimal controlCost-effectivenessChebyshev polynomialPRCCSpectral method

Panel Details:

Day: 1

Session No: 1

Panel No: 16

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. ARMAN AHMAD

Research Scholar | VELLORE INSTITUTE OF TECHNOLOGY

Abstract: Secure Authentication Protocol for IoT enabled WSNs Based on NTRU

Panel Details:

Day: 1

Session No: 1

Panel No: 6

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 711

Mr. Karuppusamy M

Assistant Professor | Bannari Amman Institute of Technology

Abstract: Estimating Reachable Sets and $H_{\infty}$ Filtering for Markovian Switching Systems with Mode-Dependent Time-Varying Delays

Panel Details:

Day: 1

Session No: 1

Panel No: 11

Time: 3:45 PM - 6:00 PM

Venue: AB1 -703

Ms. Rajeswari M

Research scholar | Annamalai University

Abstract: A revisit notion of circulant fuzzy matrices

Panel Details:

Day: 1

Session No: 1

Panel No: 17

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Gayathri V

Research Scholar | Vels Institute of Science, Technology & Advanced Studies, Tamil Nadu, India.

Abstract: Impact of Thermal Radiation on Rotational MHD Bioconvection of an $\mathrm{Al}_{2}\mathrm{O}_{3}\!$ -Water Nanofluid with Chemotaxis and Parabolic Acceleration

Abstract Preview

Impact of Thermal Radiation on Rotational MHD Bioconvection of an $\mathrm{Al}_{2}\mathrm{O}_{3}\!$ -Water Nanofluid with Chemotaxis and Parabolic Acceleration

Gayathri V, Department of Mathematics, Vels Institute of Science, Technology & Advanced Studies, Tamil Nadu, India, gai3chennai@yahoo.com

A Selvaraj, Department of Mathematics, Vels Institute of Science, Technology & Advanced Studies, Tamil Nadu, India, aselvaraj_ind@yahoo.co.in

Abstract

This study investigates the coupled effects of thermal radiation and bioconvection in an unsteady, rotational magnetohydrodynamic (MHD) flow of an aluminium oxide-water $(\mathrm{Al}_{2}\mathrm{O}_{3}\!-\!\mathrm{H}_{2}\mathrm{O})$ nanofluid. The fluid, containing chemotactic microorganisms, moves past an accelerated vertical plate under a time-dependent ramped heating. A key novelty is the inclusion of linearised thermal radiation, modelled using the Rosseland approximation. The comprehensive physical model also incorporates the effects of Brownian motion, thermophoresis, Joule heating, and viscous dissipation to accurately capture the transport phenomena. The governing partial differential equations are non-dimensionalised and solved using a perturbation technique combined with the Laplace transform method. The central aim is to elucidate how the thermal radiation parameter impacts the interdependent velocity, temperature, nanoparticle concentration, and microorganism density profiles. Enhanced thermal radiation is expected to significantly improve heat transfer, creating a thicker thermal boundary layer. This, in turn, amplifies thermal buoyancy forces, accelerating the primary flow and directly influencing the chemotactic response and spatial distribution of the microorganisms. The findings are vital for optimising technologies like microbial fuel cells, geothermal energy systems, and microfluidic biosensors, where precise control over coupled transport phenomena is critical.

Keywords: MHDBioconvectionNanofluidThermal RadiationChemotaxisParabolicRamped Heating.

Panel Details:

Day: 1

Session No: 1

Panel No: 13

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. J MARY CATHERINE

Assistant Professor and Head | Chevalier T. Thomas Elizabeth College for Women

Abstract: Benchmarking Graph Neural Networks for Chaotic Dynamics Prediction: A Study on Lorenz Systems

Abstract Preview

Benchmarking Graph Neural Networks for Chaotic Dynamics Prediction: A Study on Lorenz Systems

J Mary Catherine, Chevalier T. Thomas Elizabeth College for Women, Perambur, catherine_vinod@yahoo.co.in

Abstract

Chaotic systems such as the Lorenz family of models are widely used as testbeds in climate and atmospheric sciences due to their sensitivity to initial conditions and regime-switching behavior. Traditional numerical solvers provide accurate trajectories but are computationally demanding and limited in generalizing across parameter regimes. Recent advances in Graph Neural Networks (GNNs) offer an alternative by representing system states as nodes and their couplings as edges, enabling relational learning of nonlinear dynamics. This paper presents a benchmarking study of different GNN architectures—including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Recurrent Models—on the Lorenz-$63$ and Lorenz-$96$ systems. We analyze their ability to reproduce chaotic attractors, capture long-term predictability, and identify bifurcation-driven regime shifts. The novelty of this work lies in providing the first systematic comparison of GNN variants on canonical chaotic systems, highlighting strengths and weaknesses across architectures. Results demonstrate that while GNNs can effectively approximate short-term trajectories, their ability to capture Lyapunov stability and long-horizon forecasts depends strongly on architectural design. This benchmarking contributes a new perspective on the role of GNNs as lightweight and interpretable surrogates for chaotic dynamics, paving the way for their application in climate and weather modeling.

Keywords: Graph Neural NetworksLorenz AttractorChaotic DynamicsLorenz-$96$BenchmarkingNonlinear SystemsClimate Modeling

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. Dinesh G

Student | Kristu Jayanti (Deemed to be University)

Abstract: Advancing Autonomous AI Agents for Scalable and Secure Enterprise Infrastructure

Abstract Preview

Advancing Autonomous AI Agents for Scalable and Secure Enterprise Infrastructure

Dinesh G, Kristu Jayanti (Deemed to be University), 23csmb11@kristujayanti.com

Ashish Koirala, Kristu Jayanti (Deemed to be University), 23csmb05@kristujayanti.com

Abstract

The rise of autonomous Artificial Intelligence (AI) agents is transforming the management and operation of enterprise AI infrastructures by enabling systems that can independently perceive, reason, and act across distributed environments with minimal human oversight. This research integrates recent advances in modular agent frameworks, multi-agent coordination, and agentic business automation to propose a unified, scalable, and resilient approach to enterprise AI. Key capabilities such as dynamic resource allocation, real-time fault recovery, adaptive decision-making, and enhanced cybersecurity position autonomous agents as foundational components of next-generation systems. Multi-agent architectures, through task partitioning and collaboration, enhance performance and reliability in complex workflows, effectively addressing diverse enterprise demands. To ensure trust, privacy, and regulatory compliance, this work explores security frameworks tailored for distributed agent ecosystems. Lifelong learning and decentralized coordination promote adaptability and robustness, while dynamic role discovery supports flexible task management in evolving operational contexts. Additionally, foundational protocols for agent infrastructure enable coherent context sharing and governance, ensuring transparency and accountability. This comprehensive framework offers actionable strategies for leveraging autonomous agents to drive operational efficiency, innovation, and sustainability. It bridges theoretical developments with practical applications and presents a forward-looking roadmap for enterprises pursuing AI-driven transformation.

Keywords: Artificial IntelligenceMulti-agent SystemsDecision-makingReinforcement Learning

Panel Details:

Day: 1

Session No: 1

Panel No: 15

Time: 3:45 PM - 6:00 PM

Venue: N/A

Mr. S. Gimnitz Simon

Research Scholar | SRM Institute of Science and Technology

Abstract: Symmetry analysis of time fractional $(3+1)-D$ Navier-Stokes equation

Panel Details:

Day: 1

Session No: 1

Panel No: 16

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. R. Sukanya

Research Scholar | Indian Institute of Technology Madras, Chennai 600036, India

Abstract: Multiple scale homotopy perturbation method for non-conservative duffing oscillators

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. DEEPIKA P

Research Scholar | Vellore Institute of Technology, Vellore

Abstract: Equidistribution Grid Method for Singularly Perturbed Parabolic Turning Point Problems with Robin Boundary Conditions

Abstract Preview

Equidistribution Grid Method for Singularly Perturbed Parabolic Turning Point Problems with Robin Boundary Conditions

P. Deepika, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India, deepika.p2021@vitstudent.ac.in

Mohana Sundari L, Department of Software Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India, mohanasundari.l@vit.ac.in

Abstract

In this paper, we investigate a singularly perturbed parabolic convection diffusion problem that possesses a turning point subject to Robin type boundary conditions. To resolve the strong gradients that arise near the boundary layers, we design an equidistribution mesh strategy based on an appropriate monitor function that adapts the spatial grid efficiently to the underlying solution structure. The temporal discretization is carried out on a uniform grid, while the spatial discretization employs a finite difference method constructed on the non-uniform meshes. In the temporal direction, the discretization is performed with the backward Euler approach, while in the spatial direction, the upwind method is employed. A comprehensive theoretical analysis is presented to demonstrate the stability of the method, the precise control of truncation errors, and the convergence that is uniform with respect to the perturbation parameter. The numerical scheme is proven to be first-order accurate in the discrete maximum norm. Rigorous proofs are included for all theorems and lemmas, thereby providing a complete mathematical justification of the proposed methodology. Numerical experiments are carried out to illustrate the theoretical findings and confirm the reliability of the approach.

Keywords: singular perturbationsparabolic partial differential equationsturning pointRobin boundary conditionsboundary layersequidistribution meshes

Panel Details:

Day: 1

Session No: 1

Panel No: 16

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. A. Saraswathi

Research Scholar | Vellore Institute of Technology Chennai India

Abstract: Soliton dynamics of coupled Schrodinger equation with third-order dispersion and nonlinearity

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. SOWMIYAH.S

Research scholar | B.S.ABDUR RAHMAN CRESCENT INSTITUTE OF SCIENCE AND TECHNOLOGY

Abstract: Comparative Analysis of Non-Newtonian Rheological Models: Power Law, Casson, Carreau, Giesekus, Oldroyd-B, Bingham Plastic, Herschel-Bulkley, and Phan-Thien–Tanner (PTT)

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Comparative Analysis of Non-Newtonian Rheological Models: Power Law, Casson, Carreau, Giesekus, Oldroyd-B, Bingham Plastic, Herschel-Bulkley, and Phan-Thien–Tanner (PTT)

Sindhu. J. Kumar, B.S. Abdur Rahman Crescent Institute of Science and Technology, hodmaths@crescent.education

Sowmiyah. S, B.S. Abdur Rahman Crescent Institute of Science and Technology, sowmiyahs_maths_jan2025@crescent.education

Abstract

The non-Newtonian fluids illustrate the complex flow of fluid behaviors that cannot be described by simple linear relationships. There are countable rheological models exists. This paper presents a comparative analysis of eight commonly used models: Power Law, Casson, Carreau, Giesekus, Oldroyd-B, Bingham Plastic, Herschel-Bulkley, and Phan-Thien–Tanner (PTT). These models are analyzed based on their mathematical formulations, physical interpretations like yield stress, shear thinning, and viscoelasticity and typical applications. A comprehensive tabular comparison assists in choosing model selection in research and industry applications that highlights key similarities and differences in research and industry applications. Apparent Viscosity vs. shear rate graphs for each model are included for improved rheological behavior visualization. The Casson model is found to be the best appropriate for simulating blood flow because it accurately captures the moderate shear-thinning tendency and yield stress found in blood.

Keywords: Non-Newtonian fluidsrheological modelsPower LawCassonCarreauGiesekusOldroyd-BBingham PlasticHerschel-BulkleyPhan-Thien–Tannershear thinningyield stressviscoelasticityblood flow modelling

Panel Details:

Day: 2

Session No: 1

Panel No: 2

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 710

Ms. ALYSSA DOMINICA SEQUEIRA

Undergraduate Student | Vellore Institute of Technology, Chennai, Tamil Nadu, India.

Abstract: A Comparative Study on Soft Computing Approaches In V2G And G2V-Based EV Charging Systems

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A Comparative Study on Soft Computing Approaches In V2G And G2V-Based EV Charging Systems

Alyssa Dominica Sequeira, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India, alyssa.dominica2022@vitstudent.ac.in

Prajot Prakash Borkar, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India, prajot.prakash2022@vitstudent.ac.in

Rudra Atharva Gupta, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India, rudraatharva.gupta2022@vitstudent.ac.in

Pandiyan Rengan, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India, pandiyan.r2022@vitstudent.ac.in

Arul Rajagopalan, Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India, rarul@vit.ac.in

Umamaheswari S, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, umamaheswari.suk@vit.ac.in

Abstract

This paper presents a novel intelligent-control strategy for optimizing single-phase AC/DC and DC/DC bidirectional converters operating in vehicle-to-grid (V2G) and grid-to-vehicle (G2V) systems. V2G enables sustainable and smarter energy grids. With efficiency being paramount in applications of electric vehicle charging, this study focuses on incorporating evolutionary algorithms (EAs) to tune PI controller values. The proposed controller in our paper is a PSO (Particle Swarm Optimization), tuned-PI controller. Particle swarm optimization is chosen because of its ease in programming and fast convergence. The proposed strategy for increasing converter efficiency by using an intelligent control technique with the PSO algorithm is used to improve the performance of the bidirectional converters by minimizing the overshoot, undershoot, settling time and rise time of the output voltage. A comparative study is conducted between the conventional tuning methods like PI, PID, GA (genetic algorithm), tuned-PI and the proposed PSO tuned-PI controller. The performance of each controller is analyzed through MATLAB/Simulink software. Further, various error parameters such as ISE, IAE, ITAE, as well as the overall settling time for each controller, are observed. It was concluded that PSO tuned-PI displayed the best results with a settling time of 4.415 as compared to GA-tuned-PI, PID and PI, which displayed a settling time of 4.471, 4.478, 4.591, respectively. PSO tuned-PI also obtained the best efficiency at $85.5\%$ along with its ISE, ITAE and IAE values as 102.3, 5.336 and 9.376 respectively.

Keywords: Vehicle-to-Grid (V2G)Grid-to-Vehicle (G2V)Bidirectional ConverterParticle Swarm Optimization (PSO)Electric Vehicle ChargingMATLAB/Simulink Simulation

Panel Details:

Day: 2

Session No: 1

Panel No: 3

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 711

Dr. F PATRICIA

Assistant Professor | IMMACULATE COLLEGE FOR WOMEN,Cuddalore

Abstract: Performance Modelling of a Single-Server Queue under Multiple Working Vacations and Partial Failures

Panel Details:

Day: 1

Session No: 1

Panel No: 14

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. B. ABIRAMI

Research Scholar | Vels Institute of Science, Technology and Advanced Studies

Abstract: Fractal-Fractional Partial Differential Models for Plasma Cell Cancer Progression

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Fractal-Fractional Partial Differential Models for Plasma Cell Cancer Progression

B. Abirami, Department of Mathematics, Vels Institute of Science, Technology and Advanced Studies, Chennai, abirami.balusamy1@gmail.com

S. Senthil, Department of Mathematics, Vels Institute of Science, Technology and Advanced Studies, Chennai, senthils.sbs@velsuniv.ac.in

Abstract

Plasma cell cancer, is a complex hematologic malignancy characterized by abnormal proliferation of plasma cells in the bone marrow. Traditional mathematical and biological models often fall short in capturing the irregular, scale-invariant behavior of tumor growth and invasion observed in such cancers. This paper explores the application of fractal geometry and fractal-fractional derivatives to model and analyze the progression of plasma cell cancer. By leveraging the inherent self-similar and non-local properties of fractals, combined with the memory-dependent dynamics of fractional calculus, we develop a novel mathematical framework that more accurately represents the spatial and temporal heterogeneity of cancerous tissue. The proposed model utilizes fractal-fractional partial differential equations to simulate tumour growth, nutrient diffusion, and treatment response. Numerical simulations demonstrate that the model captures essential features of plasma cell cancer progression, including irregular boundary development and anomalous diffusion. This approach not only enhances our understanding of the underlying biological mechanisms but also offers a promising direction for predictive modelling and personalized therapeutic strategies in oncology. The intersection of fractal geometry and PDE modelling thus provides a rich, interdisciplinary approach to studying plasma cell cancer. These methods not only improve our theoretical understanding of tumor biology but also hold promise for personalized medicine, where mathematical models can inform individualized treatment plans based on patient-specific data.

Keywords: Plasma cellPlasma cancer cellfractalsbox counting dimensionsnumerical simulationpartial differential equations

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Ms. Vijayaprabha Saravanan

Research Associate | Vellore Institute of Technology, Chennai.

Abstract: Analysis of Malaria Transmission Dynamics and Cost-Effective Strategies in India and Nigeria (2018-2024)

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Analysis of Malaria Transmission Dynamics and Cost-Effective Strategies in India and Nigeria (2018-2024)

Vijayaprabha Saravanan, Department of Mathematics, Vellore Institute of Technology, Chennai, vijayaprabha.s2023@vitstudent.ac.in

Rajivganthi Chinnathambi, Department of Mathematics, Vellore Institute of Technology, Chennai, rajivganthi.c@vit.ac.in

Abstract

Malaria continues to be one of the deadliest vector-borne diseases, especially in sub-Saharan Africa and South Asia, where Plasmodium falciparum is the most prevalent and deadly parasite species. Despite decades of control efforts, malaria transmission continues to pose severe health and socio-economic challenges in highly endemic regions such as India and Nigeria. In this study, we formulate and examine a compartmental malaria transmission model characterized by SEIR dynamics for the human population and SI dynamics for the mosquito vector. The model’s well-posedness is established by proving positivity, boundedness, existence and uniqueness of solutions. Equilibrium points are computed, and their stability is examined with respect to the basic reproduction number, $\mathcal{R}_0$. Using epidemiological datasets from India and Nigeria, the model is parameterized and fitted to capture country-specific malaria dynamics. Global sensitivity analysis is then performed to identify the most influential parameters accelerating or reducing disease spread. To design effective interventions, four optimal control strategies are investigated, combining measures such as mosquito repellent nets, diagnostic methods (microscopy and rapid testing), antimalarial treatment and insecticide spraying. Cost-effective analysis reveals that Strategy I (mosquito repellent nets) is optimal for India, while Strategy IV (insecticide spraying) is most effective for Nigeria. These results provide critical insights into region-specific strategies that can aid policymakers in reducing and eventually eliminating malaria.

Keywords: Cost-EffectivenessGlobal Sensitivity AnalysisOptimal ControlPlasmodium falciparumStability

Panel Details:

Day: 1

Session No: 1

Panel No: 4

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 709

Mr. A Mohammed Hakil

Assistant Professor | C. Abdul Hakeem College (Autonomous), Melvisharam.

Abstract: Fuzzy-N Soft Topology (F, N)

Panel Details:

Day: 1

Session No: 1

Panel No: 17

Time: 3:45 PM - 6:00 PM

Venue: N/A

Ms. Fathima Nasrin Shajahan

Research Scholar | Vellore Institute of Technology - Chennai Campus

Abstract: Stochastic Modeling of Measles Dynamics with Nonlinear Perturbations and Discrete Markov Switching in India (2014–2024)

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Stochastic Modeling of Measles Dynamics with Nonlinear Perturbations and Discrete Markov Switching in India (2014–2024)

Fathima Nasrin Shajahan, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, fathimanasrin.s2023@vitstudent.ac.in

Rajivganthi Chinnathambi, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, rajivganthi.c@vit.ac.in

Abstract

Measles is one of the most contagious viral infections and continues to persist worldwide, challenging public health systems in spite of extensive vaccination efforts. In this work, we develop a stochastic $SV_1V_2EIH$ model to describe the transmission dynamics of measles. It incorporates both nonlinear white noise and colored noise driven by an irreducible continuous-time Markov chain with discrete state variables representing media awareness and environmental perturbations. The model is calibrated using measles case data from India spanning the years 2014 to 2024, allowing for the estimation of key epidemiological parameters. The proposed stochastic model is proven to admit a unique global positive solution for all time. Sufficient criteria involving $R_0^s$ establish the existence and uniqueness of an ergodic stationary distribution, reflecting long-term disease persistence. These results are derived using $\theta$-stochastic Lyapunov functions constructed through a generalized $\theta$-stochastic criterion method, which offers conditions for system stability. In contrast, conditions for disease extinction are obtained under the threshold $R_0^e$. Finally, numerical simulations illustrate and validate the theoretical findings, confirming the model’s effectiveness in capturing the complex interplay among media influence, vaccination strategies, and random perturbations in measles transmission.

Keywords: ExtinctionMarkov switchingMedia coverageMeaslesNonlinear perturbationStationary distribution

Panel Details:

Day: 2

Session No: 1

Panel No: 1

Time: 3:45 PM - 5:50 PM

Venue: AB1 - 709

Mr. Mohan kumar G

Research Scholar | Vellore Institute of Technology-Chennai Campus.

Abstract: Gestation Delay in Predator Dynamics: A Prey–Predator Disease Model with Fear and Prey Refuge Effects

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Gestation Delay in Predator Dynamics: A Prey–Predator Disease Model with Fear and Prey Refuge Effects

Mohan kumar G, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology-Chennai Campus, Chennai, mohankumar.g2023@vitstudent.ac.in

Abhishek Kumar Singh, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology-Chennai Campus, Chennai, abhishekkumar.singh@vit.ac.in

Abstract

This work analyzes an eco-epidemiological prey-predator model in which the prey population is divided into susceptible and infected classes, while the predator feeds on both types of prey. The model incorporates two biologically relevant mechanisms: a fear effect in the susceptible prey and a refuge mechanism for the infected prey. To account for realistic predator growth, a gestation delay is introduced in the recruitment term. We establish the positivity and boundedness of solutions, derive the basic reproduction number $R_0$, and examine the stability of the equilibrium points for both delayed and non-delayed systems. Using the gestation delay as a bifurcation parameter, we demonstrate the occurrence of Hopf bifurcations, leading to oscillatory dynamics. Numerical experiments conducted with MATLAB and MatCont include PRCC sensitivity analysis, contour plots, bifurcation diagrams, phase portraits, and time series simulations, all of which validate the theoretical findings. The results highlight that the combined effects of fear, prey refuge, and gestation delay crucially determine system stability and can generate complex dynamic behavior in prey-predator interactions.

Keywords: Fear effectPrey refugePrey predatorGestation delayDisease model

Panel Details:

Day: 1

Session No: 1

Panel No: 4

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 709

Ms. K.SHEELA

Assistant Professor | Anna Adarsh College for Women, Chennai-40

Abstract: An Optimal EOQ Approach for Trapezoidal Demand , Weibull Distribution for Non -Instantaneous Deterioration with Shortages, Preservation and Credit Policy

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An Optimal EOQ Approach for Trapezoidal Demand , Weibull Distribution for Non -Instantaneous Deterioration with Shortages, Preservation and Credit Policy

K. Sheela, L. N. Government College,(Autonomous), Ponneri, Affiliated to University of Madras, Tamil Nadu, India, sheela.raaj1@gmail.com

E. Rathnakumari, Government Arts College, Nandanam,( Autonomous) Chennai, Affiliated to University of Madras, Tamil Nadu, India, rrathnakumari11@gmail.com

Abstract

Inventory maintenance plays a vital and crucial role in supply chain management for the success of any business organisation. In practice, retailers adopt preservation technologies when dealing with non-instantaneous deteriorating products , such as seasonal fruits, flowers and vegetables. Unlike the assumption of constant demand, the demand for such products usually varies depending on location, seasonality and consumer behaviour. In this study, demand is modelled as trapezoidal, which is divided into three distinct phases : initially, demand rises from zero to a peak level, it then remains steady for a certain period and finally , it slowly decreases to zero towards the end of the season. The deterioration rate of items is assumed to follow a two-parameter Weibull distribution, which is widely used due to its flexibility in capturing time-dependent decay and failure patterns. To reflect realistic financial practices, a credit period is incorporated, allowing retailers to generate revenue before payment is due. If payment is made within the credit period, retailers can avoid additional costs, otherwise interest charges apply. Furthermore, shortages are permitted in the system and are assumed to be partially backlogged, reflecting customer willingness to wait for the product during stockout situations. Theoretical results are established through theorems and the proposed model is supported by numerical examples and sensitivity analysis. An optimal economic order quantity(EOQ) is derived for the considered scenario, leading to the determination of the minimum total cost for the retailer.

Keywords: Trapezoidal demandpreservationdeteriorationshortages backloggedcredit policyeconomic order quantity

Panel Details:

Day: 1

Session No: 1

Panel No: 2

Time: 3:45 PM - 6:00 PM

Venue: AB1 - 702

List of Participants

Mr. kathiresan sivakumar

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Synchronization of time-delay dynamical networks via Secure Intermittent Control with Hybrid Impulses under Dual-Channel Attacks

Mr. Arun Kumar M

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Classification of Martian Surface Features: CNN Architecture Comparison with GLCM-Class Weight Hybridization and Dual-Threshold

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Ms. Haripriya S

Abstract Preview

A Multi-Host Compartmental Model of Scrub Typhus Dynamics with Comorbidity and Intervention Strategies

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Ms. Sashmita Sahoo

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Time and space depended spectral approximation of the Hunter–Saxton equation

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Ms. J. Merlin

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Quantifying Artificial and Natural Fractals: A Python-Based Analysis of Fractal Dimension in L-Systems and Foliage

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Ms. Ashika T

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Fault Tolerant Rough Set Theory

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Mr. FRIDOLIN IMMANUEL L

Ms. NEHA ABHILASH

Mr. Ayush Goyal

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Comparative Study of Graph Neural Networks for Predicting Developmental Toxicity Using Molecular Graphs

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Ms. LAKSHANA S

Ms. SUPRAJA S

Ms. AMITHA ACCA DANIEL

Ms. AMITHA ACCA DANIEL

Ms. Mayuri Santoshrao Bhokare

Mr. Abishek R

Ms. PERIYANAYAGA CLARISHA S

Ms. JAJEETHA T

Mr. Francis A

Ms. Suba S

Mr. Monnishkaran M

Ms. Aardra Mohan

Ms. Karpagavalli S

Ms. Jayabharathi J

Mr. Ayyappan N

Ms. Priyadharshini P L

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Hexic Fuzzy Set Approach to handle uncertainty in Multi-Criteria Decision Making Problem

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Ms. Carlyn Dorcas S

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Pythagorean Fuzzy Approach for Transportation Problem Using Parametric Representation

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Mr. A. Karthik

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A mathematical model of chikungunya transmission with media awareness and the application of optimal control theory

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Dr. J. Jansi Rani

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Comparative Study of Ranking Techniques in Solving Pythagorean Fuzzy Transportation Models

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Ms. Lavanya S

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A Fractional-Order Tumor–Immune Model for Lung Cancer with Combined Therapeutic Strategies

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Ms. Moumitha K

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EEG-Based Imagined Speech Classification Using Privacy-Preserving Distributed Learning Approaches

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Ms. JOSELIN INFANTA J

Ms. Saranya A

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Explainable Myocardial Infarction Classification using Testing with Concept Activation Vectors: Temporal Concept- Based Interpretability for ECG Models

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Ms. J. SUGANTHI

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On the Application of Haar Wavelet Method for Second-Order initial value Problems in Ordinary Differential Equations: A Comparison with Falkner-Type Method

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Mr. P.R.SAKTHIVEL