Abstracts
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.
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.