Paper ID : 47

Presentation ID : M001

Authors: Babita Mehta & P.K. Parida

Title: Local convergence analysis of a family of third order iterative methods using majorant function in Riemannian Manifold

Abstract: This study presents a local convergence analysis of a family of third order iterative algorithms based on the majorant principle for locating a singularity of a differentiable vector field defined on a complete Riemannian manifold. This study shows a clear relationship between the vector field under consideration and the majorant function, which relaxes the Lipschitz continuity of the derivative. Additionally, it enables us to determine the optimal convergence radius and the widest range for the uniqueness of the solution.

Paper ID : 54

Presentation ID : M002

Authors: R Karthika & V Renukadevi

Title: Equivariant LS-category and topological complexity of product of several manifolds

Abstract: The LS-category and the topological complexity are some homotopy invariants of a topological space, and the topological complexity is a close relative of the LS-category. In this paper, we calculate the equivariant version of LS-category and topological complexity of some Z_2-spaces.

Paper ID : 16

Presentation ID : M003

Authors: Kalaiselvi T & Yegnanarayanan Venkataraman

Title : Computation of Various Domination Numbers of a Family of 3-regular Graph

Abstract : Given a graph G = (V, E), a subset S ⊆V is termed as a dominating set if every vertex in V is in S or adjacent to some vertex in S. A dominating set of least size is termed a γ-set and the number of elements of any γ -set is called the domination number, denoted by γ(G). The task of finding a dominating set is in general a hard task. A dominating set S ⊆ V(G) is called a total dominating set if any vertex v ∈ V(G) has at least one adjacent element in S. The size of a minimal total dominating set is referred as the  total domination number of G and it is denoted by γ_t(G). A total dominating set S is called a captive dominating set if any vertex in S is adjacent with at least one element in V – S. The size of a minimal captive dominating set is referred as the captive domination number of G and denoted by γ_ca(G). Suppose that uv ∈ E(G) of G. u is said to dominate v strongly if deg(u) ≥ deg(v). Clearly any vertex of V(G) dominates strongly itself. S is called a strong dominating set if each v ∈ V – S is strongly dominated by some u in S. The strong domination number γstd(G) of G is the minimum size of a strong captive dominating set. S is termed a strong captive dominating set if it is both strong and a captive dominating set. The least number of elements in such a S is called strong captive domination number, γsca(G). A captive dominating set S is called a half certified captive dominating set, if S is a captive dominating set and every vertex in S has at least two neighbors in V-S. The size of a minimal half certified captive dominating set is called as the half certified captive domination number of G and denoted by γ_hcca(G). The neighborhood of v is the set NG(v) = N(v) = {u ∈ V(G): uv ∈ E(G)}. If S⊆ V(G), then the open neighborhood of S is the set NG(S) = N(S) =  ⋃_(v ∈  S)▒〖N_G (v)〗. The closed neighborhood of S is NG[S] = N[S] = S∪ N(S). A subset S of V(G) is a majority dominating set if at least half of the vertices of V(G) are either belong to S or adjacent to the elements of S. That is |N[S]|  ≥ ⌈(V(G))/2⌉. The minimum size of a majority dominating set of G is called as the majority domination number of G and is denoted by γ_m(G).Some interesting results about the computation of these parameters are reported here for flower snark graphs, that are one pertinent family of 3-regular graphs.

Paper ID : 49

Presentation ID : M004

Authors: Hidenori Ogata

Title: Method of fundamental solutions for doubly periodic potential problems

Abstract: In this paper, we propose a method of fundamental solution for two-dimensional doubly periodic problems, especially potential flow problems with a doubly-periodic array of obstacles. In the proposed method, we approximates the solution, which involves doubly periodic functions, by a linear combination of the logarithmic potentials consisting of the theta functions. The method inherits the efficiency of the ordinary method of fundamental solutions and gives an approximate solution which has the same periodicity as the one of the exact solution. Numerical examples show the efficiency of the presented method.

Paper ID : 63

Presentation ID : M005

Authors: R. Deb and A.K. Das

Title: On the solution set of semi-infiite tensor complementarity problem

Abstract: In this paper, we introduce semi-infinite tensor complementarity problem to provide an approach for considering a more realistic situation of the problem. We prove the necessary and sufficient conditions for the existence of the solution set. In this context, we study the error bounds of the solution set in terms of residual function.

Paper ID : 29

Presentation ID : M006

Authors: Biman Sarkar, Priya Sharma and Soumen De

Title : Analysis of oblique wave scattering by a thick bottom-standing barrier placed in between a pair of thin partially immersed barriers

Abstract: The study investigates the interaction of oblique water waves with a configuration consisting of a pair of partially immersed thin vertical barriers on the two sides of a bottom-standing rectangular thick barrier. The eigenfunction expansion method is employed to analyze the system, leading to weakly singular Fredholm-type integral equations. Singularities near the edges of the barriers are addressed using Chebyshev and ultraspherical Gegenbauer polynomials as basis functions. Numerical estimations of reflection and transmission coefficients are presented, demonstrating excellent agreement with existing literature and validating the theory’s reliability and applicability in practical wave interaction scenarios.

Paper ID : 112

Presentation ID : S001

Authors: Po-An Shih, Cheng-Che Wu ,Chia-Hsin Huang and Arijit Karati

Title : Effective Data Transmission in NDN-Assisted Edge-Cloud Computing Model

Abstract: The proliferation of data generated by Internet of Things (IoT) devices has prompted the pursuit of streamlined data retrieval as a fundamental objective. Edge computing performs computations locally while significantly reducing cloud overhead. However, it lacks providing location anonymity while integrating multiple clouds. Named Data Networking (NDN) as a novel Internet architecture provides location anonymity and enhances the efficacy of data exchange through caching. In this paper, we develop an efficient data retrieval system that protects file location privacy across multiple cloud platforms by leveraging NDN and edge computing. Consumers perform data queries on the local network without connecting to the cloud server in our Edge-NDN architecture. The first stage entails a search for edge-cacheable data. If content cannot be located, it is retrieved via an external network connection to the cloud. We estimate the performance of the work using NDN Forwarding Daemon (NFD), ndn-cxx, and jNDN tools. The empirical findings indicate that the proposed framework for facilitating anonymous data communication outperforms the conventional cloud-centric approach.

Paper ID : 85

Presentation ID : M007

Authors: Phani Kumar Nyshadham, Levin Dabhi, Archie Mittal  and Harsh Kedia

Title : Geometric Algorithm for Generalized Inverse of Rank

Deficient Real Matrices

Abstract: An inverse of a matrix which is not necessarily square or is square, but nevertheless singular called rank deficient matrices, is applied to solve ill-conditioned problems such as large sized matrix computations. Such an inverse is referred to as a generalized inverse. Generalized inverses have many applications in engineering problems, such as data analysis, electrical networks, character recognition, and so on. The most frequently used one is a Moore-Penrose type inverse. Several algorithms to compute generalized inverses have been proposed. Many algorithms require to solve large least square systems in minimum-norm sense. Moore-Penrose inverse matrices allow for solving such systems, even with rank deficiency, and they provide minimum-norm vectors as solutions. In this paper, we propose novel geometric algorithm for computing generalized inverse of rank deficient real matrices. While some of the approaches for the formulations are purely based on LU-factorization, the other variations are based on LU and QR factorizations. The uniqueness of the generalized inverse are also proved for the proposed formulations.

Paper ID : 66

Presentation ID : M008

Authors: Manikandan V and Monikandan S

Title: Algorithm for Reconstruction Number of Split Graphs

Abstract: A card G-v of a graph G is obtained by deleting the vertex v and all edges incident with v. The multiset of all cards of G is called the deck of G. A graph is reconstructible if it is determined up to isomorphism from the collection of all its cards. The Reconstruction Conjecture asserts that all graphs of order at least three are reconstructible. The minimum number of cards of G that do not belong to the deck of any graph not isomorphic to G is called the reconstruction number of G. A split graph is a graph in which the vertices can be partitioned into an independent set and a clique. In this paper, we prove that the degree sequence of a split graph G can be found by using some six cards of G. We give an algorithm to find the reconstruction number of split graphs G which uses only six cards of G for most of the cases.

Paper ID : 250

Presentation ID : S002

Authors:  Joyanta Kumar Majhi  and A. K. Das

Title : Pricing and retailer service strategy in a closed-loop supply Chain as a response to show rooming effect

Abstract: Increased awareness of environmental and social responsibility has prompted many manufacturers to adopt product recycling programs. In addition, with the development of the internet and e-commerce, the supply chain structure has changed from a single physical store to a mode in which online and offline outlets coexist. At the same time, consumers usually obtain a product information in a physical store before purchasing it online which is recognized as free-riding or showrooming behaviour. In contrast, the free-riding rate has the same impacts on online and offline price decisions in different decision modes. More specifically, the free-riding rate has both positive as well as negative impact on the online and offline price modes. This study investigates the influence of the showrooming effect on firms pricing and service effort in a dual-channel supply chain. The no-service, ex-ante and ex-post service effort strategies are considered. The equilibrium results show that the showrooming effect enables the firms to benefit the most from the ex-post service efforts. Moreover, the showrooming effect makes the manufacturer set both high and low wholesale prices for retailers in the ex-ante and ex-post strategy. This study further extends the three strategies by considering no show rooming effect. Results show that the greater the show rooming effect, the higher profits firms will obtain using the ex-post service effort strategy.

Paper ID : 153

Presentation ID : M009

Authors: Helda Rajendran and Kalpana Mahalingam

Title :  Properties of m-bonacci words

Abstract: The $m$-bonacci word is the unique fixed point of the morphism $\varphi_m:$ $0\rightarrow 01,~1\rightarrow 02,~2\rightarrow 03,\ldots,(m-2)\rightarrow 0(m-1),~(m-1)\rightarrow 0$. The finite $m$- bonacci word $w_{n,m}$ is defined as $w_{n,m}=\varphi_m^n(0)$. We study some combinatorial properties of finite $m$-bonacci words. We find the values of $n$, such that $w_{n,m}$ is square free. We prove that $w_{n,m}$ is primitive and have a unique representation as a product of two palindromes. We also show that the language $W_m^0=\{w_{n,m}:~n\ge 0\}$ is context-free free and not dense.

Paper ID : 141

Presentation ID : M010

Authors: Ishwariya R

Title : Robust Numerical technique for a class of singularly perturbed nonlinear system of n-differential equations with Robin boundary conditions

Abstract: In this article, a class of singularly perturbed nonlinear system of n-differential equations with unperturbed Robin boundary conditions is considered. The numerical method considered in this work consists of the classical finite difference operator over a piecewise uniform Shishkin mesh and a continuation algorithm is constructed to solve the problems. The method suggested is proved to be essentially first order convergent uniformly with respect to all perturbation parameters. Numerical experiments are carried out for two different types of Robin boundary conditions with and without perturbation parameters.

Paper ID : 254

Presentation ID : M011

Authors : Amit Tripathi, Rachna Bhatia, Pratibha Joshi and Anand Kumar Tiwari

Title : A computational study of time dependent nonlinear Schrodinger equation with cubic nonlinearity

Abstract: This research study presents a computational method to solve one dimensional Schrodinger equation with cubic non-linearity, which describes many important physical phenomena such as propagation of classical waves in dispersive nonlinear media, nonlinear optics, water waves etc. We use modified trigonometric cubic B-spline functions in collocation method to discretize the equation in space variable. This approach converts the equation into system of ordinary differential equations, which has been solved using stability preserving Runge-Kutta method. The computational complexity is observed as linear in size of partition. The implementation of developed approach is easy and the required computational work is also very less. Additionally, the solutions using this approach can be found not only at the discretized mesh points xi but also at any point in solution domain.

Paper ID : 36

Presentation ID : S003

Authors: Redwan Walid, Lavanya Elluri and Karuna Joshi

Title : Secure and Privacy-Compliant Data Sharing: An Essential Framework for Healthcare Organizations

Abstract: Data integration from multiple sources can improve decision-making and predict epidemiological trends. While there are many benefits to data integration, there are also privacy concerns, especially in healthcare. The Health Insurance Portability and Accountability Act (HIPAA) is one of the essential regulations in healthcare, and it sets strict standards for the privacy and security of patient data. Often, data integration can be complex because different rules apply to different companies. Many existing data integration technologies are domain-specific and theoretical, while others rigorously adhere to unified data integration. Moreover, the integration systems do not have semantic access control, which causes privacy breaches. We propose a framework for sharing and integrating data across healthcare providers by protecting data privacy. We use an ontology to provide Attribute-Based Access Control (ABAC) for preventing excess or unwanted access based on the user attributes or central organization rules. The data is shared by removing sensitive attributes and anonymizing the rest using k-anonymity to strike a balance between data utility and secret information. A metadata layer is used to describe the schema mapping to integrate data from multiple sources. Our framework is a promising approach to data integration in healthcare, and it addresses some of the critical challenges of data integration in this domain.

Paper ID : 276

Presentation ID : S004

Authors:  Anisha Mitra and Dipanwita Roy Chowdhury

Title : Guarding the Beats by Defending Resource Depletion Attacks on Implantable Cardioverter Defibrillators

Abstract: Implantable Medical Devices (IMDs) have revolutionized the treatment of critical diseases. However, the increasing reliance on these life-saving devices' wireless functionality has made them vulnerable to cyber attacks. Implantable Cardioverter Defibrillator (ICD) has emerged as a leading IMD owing to the worldwide surge in cardiac diseases. Given the resource-constrained ICD environment, there's a pressing need to develop tailored security measures for protection, moving beyond traditional approaches. In this paper, we present resource depletion attack scenarios in an ICD environment where attackers can exploit ICD's wireless connectivity function. We propose some comprehensive approaches to mitigate such attacks, offering a significant step forward in safeguarding the well-being of patients. This research contributes to the ongoing efforts to secure the Internet of Medical Things (IoMT) ecosystem and underscores the importance of cybersecurity in modern healthcare.

Paper ID : 164

Presentation ID : S005

Authors: Debranjan Pal, Mainak Chaudhury, Abhijit Das and Dipanwita Roy Chowdhury

Title : Deep Learning Based Differential Distinguishers for NIST Standard Authenticated Encryption and Permutations

Abstract: Deep learning-based cryptanalysis is one of the new ideas that has emerged in recent years. By using deep learning-based methodologies, researchers are currently modeling conventional differential cryptanalysis. We use deep learning models, CNN, LSTM, LGBM, DenseNet and LeNet, to generate deep learning-based differential distinguishers that can reveal weaknesses in the encryption schemes. We focus on National Institute of Standards and Technology (NIST) standard lightweight authenticated encryption (AE), such as TGIF-TBC, and, LIMDOLEN- 128, along with permutation methods like SPARKLE-256, ACE-128 and SPONGENT-160. Our research has led us to found that deep learning techniques can generate differential distinguishers for these cryptographic elements. Specifically, we were able to develop differential distinguishers for the SPONGENT-160 permutation up to seven rounds, for the SPARKLE-256 permutation up to three rounds, for the ACE-128 permutation up to four rounds, for the TGIF- TBC AE up to five rounds, and for the LIMDOLEN-128 AE up to fourteen rounds. Notably, this marks the first instance of a deep learning-based differential classifier for the authenticated encryptions TGIF- TBC, LIMDOLEN-128, as well as the permutations SPARKLE-256, ACE-128, and SPONGENT-160, based on our current understanding. When considering various models, both DenseNet and CNN demonstrate strong performance. However, it is the LightGBM (LGBM) model that truly shines as the optimal choice, primarily attributed to its minimal parameter requirements and rapid response speed.

Paper ID : 107

Presentation ID : M012

Authors:  J Jenifa, and J Christy Roja

Title : Overlapping Iterative Numerical Method for Solving Systems of Singularly Perturbed Convection Diffusion Problems with Mixed Type Boundary Conditions

Abstract: We investigate in this article convergence of the overlapping iterative numerical method on a Shiskin type mesh for a system of singularly perturbed convection-diffusion equations with mixed type boundary conditions. The analysis is based on defining some auxiliary problems that allow to prove the uniform convergence of the method in two steps, splitting the discretization error and the iteration error. An error estimate is derived by using supremum norm and it is of order O(N^(−1) ln^(2)N). Numerical experiments are given to demonstrate the theoretical results.

Paper ID : 209

Presentation ID : M013

Authors: Ali Al-Sharadqah and Giuliano Piga

Title :  Concentric Ellipse Fitting Problem: Theory and Numerical Implementations

Abstract: The problem of fitting ellipses has been popular since the 1970's, and remains a prominent area of research in statistics, computer vision, and engineering. This paper aims to address the problem of fitting concentric ellipses under general assumptions which started paying more attention recently due to its applications in engineering. We study two methods of obtaining an estimator of the concentric ellipse parameters under this model, namely, the least squares (LS) and the gradient weighted algebraic fits (GRAF). We address some practical issues in obtaining these estimators. Since our model is nonlinear, obtaining an estimate for the concentric ellipse parameters requires the implementation of numerical minimization schemes. We propose and compare several minimization schemes, and provide several initial guesses which yield the best convergence rates.

Paper ID : 104

Presentation ID : S020

Authors: Vishnu Pendyala, Samhita Konduri and Kriti Pendyala

Title: Analysis of Multi-language Regional Music Tracks using Representation Learning Techniques in Lower Dimensions

Abstract: Machine understanding of music requires digital representation of the music using meaningful features and then analyzing the features. The work in this paper is unique in using representation learning techniques in lower dimensions for analyzing the effectiveness of mel-spectrogram features of assorted music tracks in multiple languages. The features are plotted in three different transformed feature spaces for visual inspection of the fine-grained attributes of the music rendition such as the vocal artist, their gender, language, and standing in the industry. The analysis of the music tracks in a chosen dataset using spectral and non-spectral algorithms such as Principal Component Analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) provide valuable insights into the representation learning of the selected music tracks. UMAP performs better than the other two algorithms and is able to reasonably discern the various subtler aspects of a music rendition.

Paper ID : 157

Presentation ID : S007

Authors: Amit Sardar and Dipanwita Roy Chowdhury

Title :  Key Dependent Dynamic Sbox for Kasumi Block Cipher

Abstract: The core strength of a block cipher lies in its nonlinear substitution operation, known as the SBox. However, the presence of static parameters within the SBox can potentially lead to the exposure of certain information in the ciphertext. In this paper, we present a methodology for the construction of key-dependent SBoxes. These key-dependent SBoxes exhibit resistance against linear and differential cryptanalysis. In this paper, we generate key-dependent 7-bit and 9-bit S-Boxes for the Kasumi block cipher. Furthermore, we demonstrate their resistance against known differential fault attacks.

Paper ID : 100

Presentation ID : M014

Authors:  Samar Idris and Rifat Colak

Title : Lambda – statistical derivative

Abstract: In this study, following recent and new studies, we extend recently introduced statistical derivative and Cesàro derivative to λ−statistical derivative, (V, λ)− derivative and strongly (V, λ)− derivative respectively. We also give the relationship between the λ− statistical derivative and strongly (V, λ)− derivative.

Paper ID : 218

Presentation ID : S008

Authors:  Shuddhashil Ganguly, Himadri Mukherjee , Ankita Dhar , Matteo Marciano and Kaushik Roy

Title : ChiBa – A Chirrup and Bark detection system for Urban Environment

Abstract: The World is developing at a tremendous pace which has been catapulted by large-scale technological advancements. Building mega structures has never been easier and modes of commute have also developed thereby shortening travel-time. Such advancements have also brought along newer sources of pollution which are harming our planet at an even faster pace. Sound pollution is one such agent which has a long-term effect on not only humans but the entire biodiversity. Its effect on life is not immediately observed but the damage becomes visible over time. Birds are one of the most affected creatures due to sound pollution. This is one of the major reasons for declining bird population in the Urban areas. It is very important to preserve biodiversity for a sustainable future. Animals have calls that are melodious and rhythmic and these calls tend to change when they are in distress. An automated system can be very useful in this context which can monitor animal sounds and detect changes in their calls. Deployment of such a system in Urban areas is challenging due to the presence of ambient sounds which is extremely diverse. Thus it is essential to initially detect animal calls in the Urban environment prior to monitoring them. ChiBa is a system proposed to address this problem. Experiments were initially performed with the detection of birds and dogs (most common and loudest creatures in cities) calls in the Urban environment. Tests were performed with over 7K clips comprising of the animal calls as well as Urban ambient sounds. The audios were modeled using a deep learning-based approach wherein the highest accuracy of 99.91% was obtained.

Paper ID : 212

Presentation ID : S009

Authors:  Kokila R  and Thangavelu P

Title : FFT based image registration using Stationary Wavelet Transform and edge features

Abstract: Image registration plays a pivotal role in many image processing applications that involve multiple images for comparison, integration (or) analysis such as image fusion, image mosaics, scene change detection and medical imaging. We propose FFT based image registration using Stationary Wavelet Transform (SWT) and edge features. Two sets of experiments were conducted on a number of images to measure the effectiveness and robustness of the proposed schemes. From our extensive experimental results, it was found that SWT based image registration scheme performs better than edge feature based schemes and Normalised Gradient Correlation (NGC) approach [21] and able to recover scale factor up to 8.

Paper ID : 87

Presentation ID : M016

Authors: Ramkumar S B and Renukadevi V

Title : Stabilizer group of set ideals

Abstract: We derive a necessary and sufficient condition for the stabilizer groups of set ideal on an infinite set X containing a moiety of X is to be isomorphic. Also, we prove that the outer automorphism group of S_{I} is a group of order atmost two if I is isomorphic to its polar.

Paper ID : 35

Presentation ID : M017

Authors: Rachna Sachdeva and Ashok Agarwal

Title : Modified lattice paths and Gordon-McIntosh eighth order mock theta functions

Abstract: In 2004, the second author gave the combinatorial interpretations of four mock theta functions of S. Ramanujan using (n+t)-color partitions introduced by himself and G.E. Andrews in 1987. Very recently, Agarwal and Sood defined split (n+t)-color partitions which generalize the (n+t)-color partitions. Using split (n+t)-color partitions they provided combinatorial meaning to two eighth order mock theta functions of Gordon-McIntosh found in 2000. In this paper, we modify the definition of Agarwal-Bressoud weighted lattice paths and restate Agarwal-Sood results in terms of modified lattice paths. This results in two new combinatorial identities.

Paper ID : 22

Presentation ID : S010

Authors: Aadi Gupta, Priya Gulati and Siddhartha Chakrabarty

Title :  Classification based credit risk analysis: The case of Lending Club

Abstract: In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit derivative based on the idea of a Credit Default Swap, to hedge against an event of default. The results on the test set are presented using various performance measures.

Paper ID : 50

Presentation ID : S011

Authors:Parvathi Pradeep, Premjith B, Nimal Madhu M & Gopalakrishnan E.a

Title :  A Transformer-based Stock Market Price Prediction by incorporating BERT Embedding

Abstract: The stock market trend is known to be volatile, dynamic and nonlinear. Therefore, accurate prediction of the trend and forecasting the stock prices in today's world is one of the most complex tasks. It is because of the events and preconditions, macro or micro, a few being politics, global economic conditions, and unexpected events which affect the stock market trend. Since it is difficult to predict all the contingencies, how long the effect of such parameters last can not be predicted. In this work, we studied the efficacy of different deep learning algorithms to learn the trend in the stock market price to predict the price for the next few days. We considered the stock price, stock index and dollar index and related news data to predict the stock closing price of Apple Inc. Sentence embedding and sentiment scores were extracted from the news data and fed to the deep learning model along with stock price, stock index and dollar index values. The deep learning model was designed using a Transformer consisting of an Encoder stack with attention layers and a set of MLP layers to reshape the predictions. The experiments showed that incorporating sentence embedding improved the prediction rate compared to the state-of-the-art model.

Paper ID : 57

Presentation ID : M018

Authors: Sapan Kumar Nayak and Pradip Kumar Parida

Title :  Real dynamics of a sixth-order family of derivative free iterative method without memory

Abstract: This manuscript introduces the dynamical behavior of a family of sixth-order derivative-free iterative method. When, the proposed method applied on a quadratic equation, and the presence of a parameter $\mu\in \mathbb{R}$, the iterative method creates the dynamical plane. Also, the reliability and stability of the iterative method have been studied using different tools. Moreover, information like convergence to $n$- cycles, different types of fixed points, and the chaotic nature of polynomials are all studied using the convergence plane.

Paper ID : 167

Presentation ID : S012

30. Christopher Samuel Raj Balraj and Nagaraj P

Title: Prediction of mental health issues and challenges using hybrid learning techniques

Abstract: Mental health issues like melancholy, anxiety, and a lack of sleep-in young children, teenagers, and adults are the root cause of emotional stress. It affects how someone feels, thinks, or responds to a certain circumstance or situation. Being in good physical and mental health is a prerequisite for productive work and realizing one's full potential. From childhood to maturity, maintaining one's mental health is crucial. The various causes of mental health concerns that lead to mental illness include stress, social anxiety, depression, obsessive-compulsive disorder, substance addiction, employment issues, and personality disorders. We used openly accessible web datasets to collect the data. The data was label-encoded to improve prediction. The methods employed include logistic regression, Nave Bayes, decision trees, neural networks, and support vector machines. The Decision Tree, the Support Vector Machine, and the neural network, in that order, are the most trustworthy models for stress, depression, and anxiety. The data is put through several machine-learning techniques to produce labels. Based on these classified categories, a model will be created to forecast the mental state of an individual. People over 18 who are working class make up our main market. After finishing, based on the information a user submitted on the website.

Paper ID : 151

Presentation ID : S013

Authors: Sakthidevi I, S J Subhashini, Jeyaraj Jane Rubel Angelina, Venkataraman Yegnanarayanan and Kundakarla Syam Kumar

Title : Leveraging Meta-Learning for Dynamic Anomaly Detection in Zero Trust Clouds

Abstract: In the rapidly evolving landscape of cloud computing, ensuring the security of data and services remains an imperative challenge. The Zero Trust framework, advocating continuous verification and access control, presents a pivotal paradigm to mitigate risks. This research introduces a pioneering approach named "DeepMetaGuard" for addressing dynamic anomaly detection within Zero Trust cloud environments. By amalgamating the Model-Agnostic Meta-Learning (MAML) and Variational Autoencoders (VAEs) – a Deep Anomaly Detection model, DeepMetaGuard stands as a promising innovation. DeepMetaGuard harnesses the potential of meta-learning through MAML, which expedites the model's adaptation to diverse cloud scenarios, thereby enhancing its adaptability to anomalous behaviours. Simultaneously, its integration with VAEs equips the model to identify anomalies across various cloud environments by acquiring generalized knowledge while accommodating distinct traits. To assess DeepMetaGuard's efficacy, a comprehensive simulation analysis is conducted, comparing its performance against existing anomaly detection algorithms. The evaluation encompasses a spectrum of simulation metrics, including Area Under Curve – Precision Recall Metric (AUC-PR), Detection Time, Precision-Recall Gain Curves, and Matthews Correlation Coefficient (MCC). AUC-PR gauges precision-recall trade-offs, Detection Time measures response speed, Precision-Recall Gain Curves visualize incremental performance gains, and MCC balances overall model performance. In this pioneering study, DeepMetaGuard emerges as a proficient contender in dynamic anomaly detection within Zero Trust cloud environments. The amalgamation of meta-learning and deep anomaly detection techniques, as evidenced through the comprehensive evaluation, underscores its potential in redefining cloud security. By introducing DeepMetaGuard and substantiating its effectiveness against established benchmarks, this research contributes to the advancement of cybersecurity strategies in the realm of cloud systems.

Paper ID : 165

Presentation ID : S014

Authors:  Sumathi Ganesan and Mahalakshmi G

Title : ANALYSIS OF BLOOD TRANSFUSION DATASET USING DATA MINING TECHNIQUES

Abstract: Data mining is the practice of using large data sets to analyze and learn from. Here the Blood Transfusion data set is being processed and the required knowledge is gained through data mining classification techniques. The blood Transfusion data set is first being processed through different classification algorithms. The top five algorithms with greater accuracy are noted. Then, the dataset is normalized within the value 0.0 to 1.0 and the same process is carried out. Again, the accuracy is then being checked for any changes. If changes were to be found, they are justified. The top five algorithms that were finalized before normalizing are bagging algorithm, LogitBoost algorithm, J48 algorithm, ClassificationViaRegression and Random Forest algorithm.

Paper ID : 127

Presentation ID : M019

Authors: Sheela Rani M and Dhanasekar S

Title: Fuzzy MCDM techniques for analysing the risk factors of COVID-19 and FLU

Abstract: Decision making is inevitable in day today life. Fuzzy Multi Criteria Decision Making is incorporating for better decision making in almost all kind of complexity problems. In this research notable advantages of VIKOR and TOPSIS techniques employed with fuzzy triangular numbers to analyse the risk factors of COVID-19 and FLU. The comparative analysis is illustrated to find the most influencing risk factors of COVID-19 and FLU by comparing each and every situation of patients. At last, the resistance test also included to check the final rankings and outcome.

Paper ID : 182

Presentation ID : M020

Authors: Narmada Devi R and Sowmiya S

Title: Solving of Assignment Problem by Pythagorean Octagonal Neutrosophic Fuzzy Number

Abstract: The article aims to introduce the Pythagorean Octagonal Neutrosophic Fuzzy Number (PONFN) and its operations. In this paper, deals with the solving of assignment problem by the various ranking procedure based on Pythagorean Octagonal Neutrosophic Fuzzy Number. Comparative Analysis also performed to ensure the framework’s robustness.

Paper ID : 258

Presentation ID : S015

Authors: Rajalaxmi G , Vimal S E  and Janani Selvaraj

Title: Water Body Segmentation for Satellite Images Using U-Net++

Abstract: Satellite images are important for both monitoring and managing natural resources. The ability to identify and manage water resources is made possible by the segmentation of water bodies in satellite data. In this study, U-Net++(Nested U-Net) model was used to separate water bodies in satellite data. The dataset for the project was collected using USGS Earth Explorer and QGIS, and it was divided into 20% for testing and 80% for training. After 70 cycles of training, the U- Net++ model had an accuracy of 97.66%. The U-Net++ model builds on the original U-Net model, which has been widely used for segmentation tasks. The U-Net++ model incorporates skip connections and dense connections to improve model performance. This study's ability to segment the water body opens up a lot of possibilities for controlling and monitoring water supplies, among other things. The accuracy reached with the U-Net++ model demonstrates its capacity for accurate water body segmentation in satellite pictures.

Paper ID : 268

Presentation ID : S016

Authors: Janani Selvaraj and Prashanthi Devi Marimuthu

Title:  Modeling Vegetation Dynamics: Insights from Distributed Lag Model and Spatial Interpolation of Satellite Derived Environmental Data

Abstract: The study proposes a method for modelling vegetation dynamics by combining time series analysis of the Normalised Difference Vegetation Index (NDVI) with spatial interpolation of environmental data. The goal is to provide a comprehensive understanding of how vegetation responds to changing environmental conditions by taking both temporal and spatial aspects into account. To investigate the temporal patterns of NDVI, advanced time series analysis techniques are used in the temporal domain. Distributed Lag Models, in particular, are utilised for modelling to discover the complex interactions between satellite derived NDVI and environmental factors such as Land Surface Temperature and precipitation. This method aids in assessing the delayed impacts of environmental influences on vegetation providing information on both short-term and long-term responses. Simultaneously, spatial interpolation methods are used in the spatial domain to build continuous maps of environmental variables across the study area. These spatial surfaces provide useful information on the geographic variation of environmental conditions. These findings have implications for ecosystem management, assessing climate change, and planning land use, providing a solid platform for informed decision-making in complex ecological systems.

Paper ID : 267

Presentation ID : S017

Authors: Sundareswaran N , Sasirekha S, Vijay M and Vivek Rabinson Rabinson

Title : Supporting Smart Meter Context Management using OWL Ontology and Hyperledger Fabric Blockchain

Abstract: The usage of electrical and electronic appliances is on the rise in both homes and businesses. The smart energy device has various potential applications, including power measurement, power control, and data exchange between smart power plants and individual customer endpoints. However, the current smart energy meters primarily provide data on the overall electricity consumption of a home or business, without considering context or information security. To effectively manage energy, it is essential to have a knowledge interpreter and a secure information storage system, as most households and industries lack awareness of energy consumption, data privacy, and actions that can reduce demand. Hence, this study proposes a context- aware smart energy metering system and a secure information storage management system based on blockchain. Moreover, we analyzed the Sustainable Data for Energy Disaggregation (SustDataED2) dataset. Similarly, the Hyper Ledger Fabric (HLF) blockchain system functions as a storage ledger, ensuring the integrity

of information and protecting it against malicious attacks.

Paper ID : 79

Presentation ID : S018

Authors: Jaya Sudha , Hariprasath C and Senthil Kumaran R

Title: Design of Microstrip Rectangular Dual Band Antenna for MIMO 5G Applications

Abstract: In today's wireless communication networks, microwave antennas play a pivotal role in ensuring efficient and reliable connectivity. This is centered on the creation of small rectangular patches for multiband applications using several sorts of flawed soil structure methodologies. This study introduces a proposed design for a microstrip rectangular dual-band antenna specifically tailored for MIMO (Multiple-Input Multiple- Output) applications in the context of 5G technology. The antenna is designed to operate at two frequency bands of 3.5 GHz and 6 GHz. The proposed antenna is composed of a rectangular patch with a slit and a rectangular ground plane. The proposed antenna is compact, low-cost, and suitable for 5G MIMO applications. The rectangular patch antenna presented in this study demonstrates a remarkable return loss of -20.02 dB, indicating its excellent impedance matching capabilities. This antenna operates efficiently at a frequency of 5.8 GHz. The suggested antenna performs admirably and has high radiation efficiency. The U-shaped Defected Ground Structure (DGS) employed in this study exhibits an impressive bandwidth of 500 MHz at two central frequencies: 5.7 GHz and 8.8 GHz. Additionally, it achieves a bandwidth of 300 MHz within the frequency range of 8.7-9 GHz. The simulated Frequency Dependent Ground Structure (FDGS) analysis reveals that the 10 dB return loss bandwidth percentage is 5.26%, covering the frequency range of 7.4-7.8 GHz. Moreover, the FDGS achieves an 8.94% bandwidth (11.6-12.7 GHz), demonstrating its effectiveness in providing a wide operating range for the antenna. The FDGS enhances the return loss bandwidth, the radiation characteristics, and the maximum gain by 10 dB, whereas the gain of the dual-band antenna is between 3 and 6 dB. Radiation properties, maximum gain, and 10dB return loss bandwidth have been improved with rectangular DGS. A typical FR4 substrate with a cheap cost and a thickness of 1.6 mm was chosen as the dielectric material to design and construct the fault grounding structure. It has a dielectric constant of 4.4. Validating the modelling findings allows for experimental testing of the fabricated antennas.

Paper ID : 121

Presentation ID : S019

Authors: Sk Hafizul Islam,

Purnendu Vashistha, Aman P.Singh, Aman Kishore and Jitesh Pradhan

Title: ResNet-CPDS: Colonoscopy Polyp Detection and Segmentation Using Modified ResNet101V2

Abstract: Colorectal cancer (CRC) is a global public health concern, and early detection through screening reduces mortality rates. It is one of the common types of cancer with a high mortality rate. Traditionally, colonoscopy is used to detect CRC, which is inefficient. Therefore, an automated Colonoscopy Polyp Detection and Segmentation (CPDS) system can significantly increase the efficiency of colonoscopy. We propose an automated model: ResNet-CPDS, using the modified ResNet101V2 model. We evaluate the performance of ResNet-CPDS and other CPDS models, and compare their accuracy. We also demonstrate that the ResNet-CPDS model outperforms other models for the CVC-ClinicDB dataset.

Paper ID : 38

Presentation ID : M021

Authors: Mohammad Alakhrass

Title: LIEB FUNCTIONS AND PPT MATRICES

Abstract: In this note, we present several inequalities that govern the components of a  2 × 2 PPT matrices. The utilization of Lieb functions enables us to present concise and straightforward proofs for these inequalities.

Paper ID : 279

Presentation ID : M023

Authors: Ayyappan G and Arulmozhi N

Title: Analysis and empirical investigation of queueing inventory system: Two classes of customer under non-preemptive priority, single vacation, and (s, S) replenishment policy

Abstract: The arrival of priority and regular customers is governed by the Markovian Arrival Process, with two distinct categories of customers being observed. The duration of their service times is determined by the Phase-type distribution. The system consists of an infinite capacity for ordinary customers and priority customers having finite buffer capacity of N size. Maximum S items in the inventory. Arriving priority customer which find the inventory depleted is lost (lost sales). If a ordinary customer discovers the inventory to be empty, they may be waiting to queue. The utilization of the (s, S) policy is also implemented within the system. The number of priority and regular customers in the system is analyzed using the Matrix analytic method. Furthermore, a thorough examination of steady-state analysis, busy period, cost analysis, and numerical exemplifications are all carried out.

Paper ID : 281

Presentation ID : M022

Authors: G. Ayyappan , S. Sankeetha

Title: Enhancing Queue Management: Dynamic Server Allocation and Optional Services in Stochastic Modeling

Abstract : Consider a queueing system with a single server, where customer arrivals follow a Markovian arrival process and service times follow a phase-type distribution. The main server has the capability to recruit an additional server when the number of customers in the system exceeds a certain threshold, denoted as $L$. Both servers provide normal service to customers, and optional service is provided upon request. The main server takes multiple vacations, with the durations following an exponential distribution with rate parameter $\eta$, until there is at least one customer in the system. This system can be represented as a Markov chain process, and its steady state can be analyzed using matrix analytic methods. Performance measures such as the average number of customers, waiting time, and system throughput can be evaluated using the steady state probabilities. Numerical and graphical representations can be established to visualize the system’s behavior. By studying this system, we can gain insights into its efficiency, identify areas for improvement, and make informed decisions to enhance overall performance.

Paper ID : 282

Presentation ID : M024

Authors: G. Ayyappan and S. Nithya

Title: Analysis of M[X1], M[X2]/G1, G2/1 Retrial Queue with Priority Services, Differentiate Breakdown, Delayed Repair, Bernoulli Feedback, Balking and Working Vacation

Abstract : In this study, we construct a single server retrial queueing system with two types of breakdowns, delayed repair, Bernoulli feedback, balking and working vacation. Two different categories of customers like priority and ordinary are to be considered. This model proposes non-pre-emptive priority discipline. Ordinary and priority customers arrive as per Poisson processes. For both ordinary and priority customers, the server consistently affords a single service that follows to general distribution. During certain periods, an arriving ordinary customer may balk the system. When the orbit and priority queue are empty after the service is ended, the server takes a single working vacation. In this study, we used probability generating function and supplementary variable technique to solve the Laplace transforms of time-dependent probabilities of system states. In order to accelerate the sensitivity analysis of system descriptions, numerical data are obtained and also examined.

Paper ID : AA1

Presentation ID : M025

Authors:  G. Ayyappan, S. Kalaiarasi

Title: Efficacy Of A Flexible Group Service Queueing Model With Server Malfunction

Abstract : Queueing models in which the services are provided in groups (or blocks or batches) have found to be very useful in real-world applications and such queues been extensively analysed in the literature. In this paper we see one such group service queueing model with server malfunction. The arrival processes is a Markovian arrival. Customers are provided service in groups of varying size from 1 to the fixed constant, say, N. The service time of a batch follows the phase type distribution corresponding to the each size of the group. A group’s service time is taken as the highest of the service times of each customers who make up the group. The server may experience a malfunction at any time, in which case the server will continue to provide service at a slower rate for that particular customer only, rather than moving on to repair and when that particular customer’s service is completed, the server will immediately go through the repair process for rejuvenation. We calculated the steady state probabilities by using the matrix geometric method, then by using it we computed few performance measures. We have studied the busy period and the distribution of waiting time is derived. Results are illustrated with some graphical representations.

Paper ID : 45

Presentation ID : M026

Authors:  Nathan Chane De la Cruz, Rocky Bigcas and Jerico Bacani

Title: The search for solutions of the Diophantine equation a^x + b^y + c^z = w^2 with Pythagorean triple bases

Abstract : This paper explores the solvability in the nonnegative integers of the Diophantine equation having the form a^x + b^y + c^z = w2, where a=2mn, b=m^2−n^2 and c=m^2 +n^2, such that m and n are positive integers with m > n, and a, b, and c are primitive Pythagorean triples. Specifically, the study focuses on the case where m is odd and n is even.

Paper ID : 46

Presentation ID : M015

Authors : Shahin Shaikh and Rupal Shroff

Title : On The Line Zero Divisor Graph Of Small Finite Commutative Rings

Abstract : In this article,the list of line zero divisor graph on n = 1, 2, 3, …, 9 vertices corresponding to zero divisor graph and extended zero divisor graph of commutative rings with unity (up to isomorphism) is provided. List is classified based on the nature of rings as reduced ring or local ring. The conditions on m and n such that Km,n is line zero divisor graph and Kn is line zero divisor graph of star graph Sn (on n + 1 vertices) are given.

Paper ID : 77

Presentation ID :S006

Authors :Krishnamugundh P, Karmel Arockiasamy,Kanimozhi G & Karthika P

Title : Analysis on Fertility of Soil Parameters using Machine Learning Algorithms

Abstract : The prediction of soil fertility is critical for effective agricultural management, and traditional methods for determining soil fertility are time-consuming and risk-intensive. However, with the advent of machine learning and AI techniques, it is now possible to accurately predict soil fertility using soil parameters, weather data, and other relevant factors. In this article, an analysis of soil fertility prediction using Machine Learning (ML) and AI algorithms is presented.   The analysis highlights the importance of adopting a comprehensive approach to soil fertility prediction, incorporating soil parameters such as pH, temperature, moisture content, humidity, NPK (nitrogen, phosphorus, and potassium), organic matter, carbon content, weather, and climatic circumstances. The proposed method offers a quick and precise outcome, enabling farmers to make informed decisions and optimize soil fertility. Overall, the study demonstrates the significant potential of machine learning and AI algorithms for soil fertility prediction and offers practical implications for agricultural management. With the help of ensemble models, it has been observed that Random Forest gave an accuracy of around 92% followed by Extra Tress classifier and other classifiers.