bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis
Authors: Sumit Kumar
Published: November 2024

This paper's goal is to investigate how quantum computing might be used to optimize power systems and to address some of the difficulties that quantum computers may encounter, along with solutions. The fundamental ideas of quantum computing are also covered, along with how they differ from classical computations. Within the framework of smart grids, quantum computing (QC) presents itself as a next-generation alternative approach to address impending computational difficulties. QC is a relatively new but exciting technology that uses the special properties of quantum mechanics to analyze data and perform calculations. This new paradigm can solve optimization, simulation, and machine learning issues more effectively and quickly than ever before by overcoming the obstacle of computational constraints. Recent significant advancements in the development of sophisticated quantum hardware and software techniques have increased the viability of applying QC in a variety of research fields, including smart grids. It is clear that a great deal of research has already been done, and that research is remarkably ongoing. As a result, this article further defines the prospective smart grid applications and presents the research findings of the most current articles, emphasizing their recommendations for applying QC approaches for diverse smart grid applications. It states their plans, methods, and outcomes. The limits of the most recent quantum computers are also discussed in this research, along with how they might significantly affect the optimization of energy systems.
Design of an Improved Model for Spectrum Sharing and Resource Management Using Deep Q-Network, Federated Averaging, and Ant Colony Optimization
Authors: Ashish Bajpai
Published: November 2024

Indeed, the ever-evolving wireless communication requires DSS and resource management for effectiveness. The old methods of spectrum allocation have a strategic shortfall since they are static and thus not adapted to real-time network conditions. Besides, more important challenges include issues over privacy and inefficiency of resource utilization. In the light of such problems, this paper proposes a new method by bringing together three deep-learning techniques, which are Deep Q-Networks, Federated Averaging, and Ant Colony Optimisation. Each is chosen because of its various competencies and complementary capabilities. First, DQN is chosen because it has the capability to handle high-dimensional action space. DQN applies deep learning to approximate the Q Value function, which provides the optimal decision of spectrum allocation based on real-time network states, such as spectrum usage and interference levels. With this scheme, up to a 20% gain in network throughput and up to a 15% cut in wasted spectrum compared to traditional methods are achievable. The second component FedAvg tackles the model training collaboration issue with privacy. It enables FedAvg for privacy-preserving resource management, where multiple devices update the global model collaboratively by communicating model updates without exchanging raw data samples. For this approach, the resulting method leads to a further increase in data privacy by reducing data leakage as high as 40%, with an improvement in resource management accuracy of up to 15%. ACO was employed to optimize resource allocation and improve spectrum efficiency. ACO emulates the natural foraging behavior of ants to solve combinatorial optimization problems. Consequently, it has led to up to 25% increase in spectral efficiency and up to 20% improvement in resource allocation balance sets. All these methods put together will provide a wholesome solution that not only optimizes spectrum and resource management but also promises privacy and adaptability. This represents severe improvement in overcoming the weaknesses of the existing approaches and will provide a strong foundation for next-generation wireless networks.
Analyzing Electrical Bikes Risk Factors Using Rough Set Theory and the Hybrid Logistic Regression Model
Authors: R. Venmani
Published: November 2024

The increasing popularity of electric bikes (e-bikes) has brought to light various risk factors associated with their use, necessitating a thorough analysis to enhance safety and reliability. This research paper aims to identify and evaluate the risk factors of e-bikes by employing Rough Set Theory (RST) and a Hybrid Logistic Regression Model. This research underscores the importance of comprehensive risk analysis for e-bikes and demonstrates the effectiveness of combining Rough Set Theory with logistic regression for predictive modeling. The findings of this study reveal that rider behavior, particularly compliance with traffic rules and use of safety gear, is the most influential factor in e-bike safety. The technical specifications of e-bikes, including battery performance and braking systems, were found to be critical in preventing accidents.
Exploring Parallel Algorithm Implementation and the Role of Parallel Processing in Multistage Interconnection Networks
Authors: Abhay B. Rathod
Published: November 2024

Parallel processing is a key way to get good speed on difficult computer jobs thanks to how quickly computer technology is improving. Multistage Interconnection Networks (MINs) are very important in this situation because they make it easy for multiple working elements in parallel computer systems to share data and work together. The focus of this study is on how parallel methods can be used in MINs, with a focus on how these networks make it possible for nodes to communicate and process data quickly. We look at the layout and design of MINs and talk about how well they can handle apps that use a lot of data by spreading out work. We look at some of the most important parallel algorithms, like matrix multiplication, sorting, and Fast Fourier Transform (FFT), to show how they use the unique features of MINs to make computations faster and more flexible. The study also looks into the problems that come up when you try to use parallel methods, such as delay, timing issues, and load balance. It also talks about ways to deal with these problems, like route algorithms, timing methods, and fault-tolerant systems. The study shows how important parallel processing is for making high-performance computer systems work better. This helps make progress in areas like science models, big data analytics, and artificial intelligence. Through looking at how parallel algorithms and MINs work together, this study shows how parallel processing can speed up complicated calculations and make systems more efficient, leading to more advanced and scalable computing solutions.
New Results on Third Hankel Determinant for Certain Subclass of Bi-Univalent Functions
Authors: Zainab Sadiq Jafar
Published: November 2024

This paper introduces new discoveries on the third Hankel determinant for the subclass C(β, δ) of bi-univalent functions in the open unit disk area D. The objective of this course is to ascertain the boundaries of the Hankel determinant of order 3, represented as H_3 (1). Furthermore, these calculations determine new boundaries for the third Hankel determinant inside the C(β, δ) family.
Comparative Evaluation of Machine Learning Predictions for Concrete Properties
Authors: Aboli A. Ravikar
Published: November 2024

The accurate prediction of concrete properties, such as compressive strength, is crucial for ensuring the safety and efficiency of construction projects. In recent years, machine learning (ML) techniques have emerged as powerful tools for modeling complex relationships between input variables and concrete properties. This study presents a comparative evaluation of various machine learning models, including Support Vector Machine (SVM), Random Forests (RF), Gradient Boost Regression (GBR), XG Boost, Adaptive (ADA) Boost, Light GBR to predict key concrete properties. A comprehensive dataset, consisting of 14 parameters of M30 Grade Concrete was used to train and test the models. The performance of each model was assessed based Mean Square Error and coefficient of determination (R²).
Results indicate that the ADA Boost algorithm performed better in predictions giving 97.09% accuracy and with least Mean Squared Error of about 1.485. Therefore, ADA Boost algorithm can be applied to develop predictive model for assessing the performance of self-healing smart concrete. The findings underscore the importance of selecting the appropriate machine learning model based on the specific characteristics of the data and the desired balance between prediction accuracy and computational efficiency. This research provides valuable insights for engineers and researchers aiming to adopt machine learning approaches in the material science and construction fields, paving the way for smarter, data-driven decision-making in concrete design.
MHD Non-Uniform Heat Source / Sink for Microorganism Bioconvection Flow on Casson Nanofluid Over an Exponential Stretching Sheet
Authors: Avinash B. Raut
Published: November 2024

This study explores the numerical analysis of Casson nanofluid flow over an exponential stretching sheet, incorporating the effects of magnetic field, nonuniform heat source/sink,  activation energy and motile microorganisms. The primary aim of the current study is to investigate the effects of these factors on the bioconvection dynamics within the fluid. By using a bvp4c algorithm, the governing equations that describe the nanofluid flow, taking into account of the non-Newtonian Casson fluid behaviour over an exponential stretching sheet’s velocity, temperature and concentration profiles along with the influence of activation energy on chemical reactions. Additionally, the study examines how motile microorganisms interact with the nanofluid flows, affecting the bioconvection patterns and overall fluid behaviour. The analysis provides a comprehensive understanding of the interplay between these variables and their effect on heat and mass transfer in the system. The results reveal that, for higher activation energy increases the temperature gradient and concentration fields, affecting the overall bioconvection process. The motile microorganisms impact to the bioconvection process by affecting the concentration and velocity fields. Their presence modifies the bioconvection patterns, which can either enhance or suppress the convective transport depending on their density and motility. Also, an increase in the values A∗ and B∗ leads to a enhance the fluid temperature.
s STAR p STAR Continuous Maps in Topological Spaces
Authors: R. Sudha
Published: November 2024

Goal of this present study seeks to discover and to study the different kind of continuous map and irresolute map is known as semi star pre star continuous map (briefly s*p*continuous) and semi star pre star irresolute map (briefly s*p* irresolute) in topological spaces. As well as discuss a few fundamental characteristics of this continuous map. Further investigate the relationship between the newly defined map and the existing continuous map and irresolute map with suitable examples.
The Circular Number of a Graph
Authors: S. Sheeja
Published: November 2024

If I_c [S]=V(G), then a set S⊆V (G) is a circular set of G. The circular number of G, represented by cr(G), is the lowest cardinality of a circular set of G. A cr-set of G is any circular set with cardinality cr(G). In this study, we determine the circular number of certain standard graphs. It is demonstrated that there exists a connected graph G such that dn(G)=a, g(G)=b and cr(G)=c for each integer a,b, and c with a>2,b>2, and c>2. The corona of graphs and circular number of joins were also explored.
A Novel Approach to Confidentiality Preservation in Big Data Using Distinct Contextual Sensitivity Hashing
Authors: Gowthami V
Published: November 2024

Despite the rapid development of technological innovations (IT), hypersensitive large-scale data assembling and efficiency have gotten better. To preserve the confidentiality of patients in the field of health care, it is necessary to reduce redundant confidential data whereas implementing hazardous big data sets which need to be discovered with the goal to gather appropriate data. Over the past decade, an array of preserving confidentiality approaches has been implemented employed by employing the quasi-identifier (QI) with application which includes healthcare services. Nevertheless, because of the enormous quantity of the majority of databases, protecting confidentiality across near-identifiers remains challenging in situations of enormous amounts of data. Because of datasets evolving constantly, traditional methods experience more time utilisation and reduced knowledge utility. In this paper, researchers present an advantageous Distinct Contextual Highly sensitive with Hellinger Convolutional Learner (DCS-HCL) technique that preserves anonymity yet optimising the value of information across enormous medical databases. In the beginning a Distinct Impact Contextual Delicate Hashing, or framework is generated using the input that has been provided Massive Dataset, and this model examines each of the distinct and affect variables prior implementing the results to Contextual Sensitivity Hashing. This serves as a basis enabling the development of highly computationally effective anonymous information through correlating associated QI-classes. For the purpose of preserve the confidential nature associated with personal data that is unstructured, an Hellinger Convolutional Neuro Security Conservation algorithm has been provided. This is accomplished through modifying CNN's algorithms strength and biases simultaneously processing QI-class data to maximize correctness and reduce data loss. The assessment's outcomes indicate that the approach we propose surpasses conventional approaches when it comes to of execution time, resource utility, loss of information, and correctness towards maintaining confidentiality using large-volume unorganized information sets.
An Introduction to Ternary Γ-Semirings
Authors: G. Chandrasekhar
Published: November 2024

In this research article has to introduce the concept of ternary Γ-semirings.We first consider the congruences and ideals of ternary Γ-semirings then we construct a new ternary Γ-semiring and to be discussed formation of ideals on this ternary Γ-semiring. Also with the help of congruences induced by homomorphism of a ternary Γ-semiring.After that we will establish some of isomorphism theorems and identified the some of commutativity in the diagrams. particularly  some fundamental results of ternary Γ-semiring were proved and strengthened.
VMCNN: Integrating Vedic Mathematics for Enhanced Convolutional Neural Network Performance in Image Classification Tasks
Authors: Geetanjali Kale (Rao)
Published: November 2024

In the rapidly advancing field of deep learning, Convolutional Neural Networks (CNNs) have emerged as a cornerstone for various image classification tasks. However, the existing CNN architectures and optimization methods often grapple with trade-offs between computational efficiency, accuracy, and generalizability. This work addresses these limitations by integrating principles of Vedic Mathematics, an ancient Indian mathematical system known for its efficiency and simplicity, into CNN optimization. Traditional optimization techniques often fall short in balancing the computational load with the accuracy of the model, particularly in diverse and complex datasets. To overcome these challenges, we propose a novel model that incorporates specific Vedic Mathematical Sutras, namely Urdhva-Tiryakbhyam, Anurupyena, Nikhilam Navatashcaramam Dashatah, Shunyam Saamyasamuccaye, Paravartya Yojayet, Sankalana-vyavakalanabhyam, Ekadhikina Purvena, and Gunitasamuchyah, for optimizing internal operations of CNNs. These Sutras were meticulously selected for their potential to simplify computational processes, enhance parallel processing capabilities, and optimize training algorithms. The application of these sutras has led to a remarkable improvement in the performance of CNNs across various datasets, including ImageNet, CIFAR, ChestXRay8, and Architectural Heritage Datasets. The optimized CNN models demonstrated a significant enhancement in classification accuracy (9.5% increase), precision (8.3% increase), recall (8.5% increase), area under the curve (AUC) (4.9% increase), MAE (5.5% improvement), and reduction in delay (6.5% decrease) compared to existing methods. The integration of Vedic Mathematics into CNN optimization not only paves the way for more efficient and accurate image classification models but also opens new avenues for interdisciplinary research, blending ancient mathematical wisdom with contemporary artificial intelligence techniques. This advancement has profound implications for various applications, including medical imaging, autonomous vehicles, and heritage conservation, thereby contributing significantly to the field of AI and computational efficiency.
Secure and Efficient Data Hiding in the Cloud with Quantum-Resistant Encryption
Authors: Salma Banu
Published: November 2024

Traditional steganographic methods in multi-cloud environments face significant challenges in cloud security, including complex key management, high computational overhead, and vulnerabilities to various attacks regaring secure data concealment. This paper presents a novel steganographic mechanism tailored for single cloud environments that address these issues. The proposed mechanism leverages a combination of homomorphic encryption, elliptic curve cryptography (ECC), and lattice-based cryptographic techniques to ensure robust security against classical and quantum attacks. Confining operations to a single cloud simplifies credential and key management, reducing operational complexity and enhancing system efficiency. The proposed approach mitigates the risks associated with multi-cloud environments and offers strong resistance to brute-force, differential, and steganalysis attacks. The implementation and validation of the mechanism demonstrate its efficacy in maintaining data integrity, confidentiality, and availability while minimizing computational resources. The results indicate that this approach significantly improves the security and performance of steganographic operations in cloud storage environments, setting a new standard for secure data concealment in cloud computing.
Some Reverse Topological Indices of Comet and Double Comet Graphs
Authors: John Rafael M. Antalan
Published: November 2024

Introduction: Chemical Graph Theory or CGT for short, is a transdisciplinary field of Mathematics wherein graphs are used to represent chemical compounds. Under this representation, the atoms of a chemical compound are expressed as vertices, while the bonds connecting these atoms are expressed as edges. Once the graph representation of a chemical compound has been determined, graph-theoretic techniques can now be used to determine various topological indices associated with the chemical compound. Topological indices are invariants of a graph that have predictive power for the chemical properties of a chemical compound. In CGT, trees, being connected and acyclic, have been an essential class of graphs. This is because, most compounds have acyclic molecular structures. In the 2023 study by Gowtham and Husin, various reverse topological indices of a family of trees called bistar graphs have been determined.
Objectives: Motivated by the work of Gowtham and Husin, we determine some reverse topological indices of another family of trees called comets and double comets. Double comets are natural extension of bistar graphs. We also give a certain computational application of our results on double comet graphs. Finally, we investigate the relationship between the reverse topological indices of bistar graphs and double comets.
Methods: Several important graph-theoretic concepts were used to analyze some important properties of comets and double comets, leading to the computation of their reverse vertex degree based topological indices.
Results: The following reverse vertex degree-based topological indices for comets and double comets were determined: Reverse sum-connectivity, First reverse Zagreb, Second reverse Zagreb, Reverse arithmetic-geometric, Reverse geometric-arithmetic, Reverse Sombor, and Reverse Nirmala. A computational application of the results to a certain chemical compound (2,2,4,4-Tetramethylpentane) or C9H20 were also demonstrated.
Conclusions: The results of the paper provided an extension to an existing result on the reverse vertex degree-based topological indices of bistar graphs by considering double comet graphs. Several topological reverse vertex degree-based topological indices were also computed for comet graphs. The computed topological indices were also applied in determining some invariants of a certain chemical compound. For future studies, we recommend that reverse vertex degree based topological indices of other family of trees will be considered.
Learning Management in Increasing Students' Learning Motivation
Authors: Yuli Supriani
Published: November 2024

The context of this study is that numerous students experience a lack of enthusiasm for learning due to various factors, including environmental influences, insufficient motivation, disinterest in certain subjects, and inadequate utilization of technological advancements, leading to students' reluctance to study and a decline in academic performance. The objective of this research is to elucidate the role of learning management in enhancing students' motivation to learn. Employing a descriptive qualitative approach, the study examines learning management, learning motivation, and student dynamics. The findings are presented descriptively, revealing that at MA Aliyardi, learning management practices encompass typical procedures of planning, organizing, implementing, and evaluating. However, challenges in implementation are noted, such as some teachers' reliance on outdated lesson plans and deficiencies in resources and infrastructure.
Optimization of Fuzzy Economic Order Quantity using Machine Learning and Weka
Authors: P.Sujatha
Published: November 2024

Inventory management is a crucial need for businesses because it requires a significant financial and human resource investment. Machine Learning models are used by e-commerce huge companies to manage their inventory to the demand for specific products. Businesses can benefit from inventory management as a service to increase sales and forecast product demand. Inventory Management might be revolutionised by AI and ML technologies, which would allow businesses to automate repetitive processes, make data-driven choices, and instantly optimise inventory levels. By analysing vast amounts of both historical and current data, these technologies can spot inclination, forecast, and recommend, all of which increase inventory accuracy and lower the chance of stockouts or overstocking. The most efficient solution is determined using the Kuhn-Tucker Method for Nonlinear Programming, which has an impact on the monthly cost. The proposed model employs the Signed distance approach for defuzzification and the Pentagonal fuzzy number to find the lowest cost and met the demand. To evaluate the inventory concept, purchased a dataset and calculated the profit and non profit using apriori algorithm and use the Weka software to analyse the average order quantity and total cost.
A Nonlinear Analytical Approach to Social Dynamics: Modeling the Impact of Moderate Da’wah on Promoting Tolerance and Social Cohesion in Indonesia
Authors: Test Ifa Khoiria Ningrum
Published: November 2024

This study applies a nonlinear analytical approach to examine the role of Daiyah Fatayat (DIFA) in promoting tolerance and moderate da’wah in Indonesia. The research focuses on modeling the social dynamics that emerge from the interactions between da’wah messages and community responses, using a mathematical framework inspired by nonlinear systems theory. The rising threat of radicalization, which jeopardizes interfaith harmony, serves as the impetus for this study. By incorporating nonlinear modeling techniques, this research analyzes the application of key Qur’anic verses such as Q.S. Al-Baqarah: 221 and Q.S. Ali Imran: 104 within DIFA’s cadre training. The model captures how these verses influence tolerance and social cohesion over time. The study employs a combination of qualitative case study methods, including interviews, observations, and document analysis, and integrates these findings into a computational model. Results suggest that DIFA’s use of moderate da’wah can act as a stabilizing force within communities, fostering increased social harmony. This study highlights the potential for applied nonlinear analysis to provide insights into complex social phenomena such as radicalization and the promotion of tolerance.
A Mathematical Problem Formulation Based on Number Theory
Authors: Vinayak Kishan Nirmale
Published: November 2024

A branch of mathematics known as number theory is primarily concerned with the study of whole numbers. In that sense, number theory is used less in design than it is in analytics, computation, and other fields. Its inability to be used directly in any application was the issue. However, when combined with the computing power of modern PCs, number theory provides intriguing solutions to real-world issues. It serves a variety of functions in several industries, including computing, numerical analysis, and cryptography. The study of whole numbers is the primary focus of number theory. The fundamental structure of number theory has been gradually improved as a result of the dedication made by mathematicians throughout history to advancing the study of numbers, and as a result, a comprehensive and integrated field has been established. Number theory is a foundational subject that influences many other subjects and has a big impact on teachers. Number theory has been applied in statistics in a few fascinating ways. This overview paper's goal is to highlight particular noteworthy uses of this type. In number theory, indivisible numbers make up an interesting and challenging field of study. The main component of number theory is structured by diophantine equations. A Diophantine equation is an equation that needs necessary arrangements. This paper's first section looks at a few issues related to indivisible numbers and the function of Diophantine equations in Plan Theory. It is explained why Fibonacci and Lucas numbers commit to a semi-remaining Metis structure.
Enhancing Network Security in Distributed Systems Using Middle Roman Dominating Functions
Authors: R. Vinodhini
Published: November 2024

A Middle Roman dominating function (MRDN) on a graph G = (V,E) is a function f:v→{0,1,2,3} satisfying the condition that every vertex u with f(u)=0 is adjacent to at most one vertex v with f(v)=2 or 3. Further if a vertex is assigned 2, then at most two of its vertices can be assigned 0 and if a vertex is assigned 3, then all its neighbours can be assigned 0. The weight of a MRDF is the value f(V(G))=∑_uϵV▒〖f(u〗). The Middle Roman domination number γ_MR (G) is the minimum weight of a MRDF on G. In this paper, we introduce Middle Roman Domination number denoted as γ_MR (G), study the properties of the function, present some characterization and determine γ_MR (G)-value for some graphs. Middle Roman Dominating Functions (MRDFs) present a unique approach within graph theory, with significant implications for various fields in computer science. By assigning values to vertices under specific constraints, MRDFs enable the optimization of resources, enhancing network security, load balancing, and energy efficiency in distributed systems. This paper explores the application of MRDFs in scenarios such as intrusion detection, resilient network design, task scheduling, and sensor activation. By minimizing the overall weight while maintaining functional requirements, MRDFs provide an effective strategy for addressing challenges in network topology, resource allocation, and fault tolerance. The versatility of MRDFs makes them a valuable tool in the development of robust and efficient computing systems.
An Analysis of Artificial Intelligence Implications and its Impact on Marketing. A Systematic Review
Authors: Simerjeet Singh Bawa
Published: November 2024

Artificial intelligence (AI) is being used by marketers to track and forecast the future purchasing behaviour of their target audience and to gain further insights into their journey. The three areas of artificial intelligence that best demonstrate its potential are machine learning, big data, and resilient solution. Big data allows marketers to quickly and manually sort through enormous amounts of data and combine it. Businesses can use this information to ensure that the correct message is sent through the right channel to the right target at the right time. Marketers can use machine learning (deep learning) to analyse and interpret vast amounts of data. They have the ability to predict consumption trends and keep an eye on, assess, and analyse consumer spending and behaviour. Effective decision-making proves that we live in a time when robots can think like humans and have a complete comprehension of the universe. Automated systems have the ability to promptly identify concepts and trends throughout diverse datasets, interpret human emotions and actions, and provide pertinent customer support. They have the ability to quickly predict the decisions and actions of their clients and use that knowledge to solve issues down the road. More innovative and intelligent ads, reliance on bots, better content delivery, continuous learning, prevention of fraud and data breaches, sentiment analysis, voice and image recognition, predictive customer services, sales forecast, customer segmentation, etc. are just a few of the ways that marketers can expect a greater impact from AI in the coming years. The study aims to explore how artificial intelligence has changed the marketing environment overall, as well as to better comprehend the function that AI plays in it.
Efficient Algorithms for Vehicle Plate Detection in Dynamic Environments
Authors: Meriga Kiran Kumar
Published: November 2024

One of these advanced search methods makes use of NPR systems to read license plates. A multitude of technologies work together to enable the system to take a photo of a car and digitally detect whether or not the license plate number can be read from it. The term "automatic license capture" refers to the procedure of extracting the license text from the pixel data of digital photographs. In this research, we provide a license plate identification technique that makes use of morphological techniques (easyOCR, imutils) applied to pictures. The primary goal is to employ and integrate several morphological procedures based on image processing, grayscale conversion, two-dimensional filtering, and licensing in order to detect and understand the license of specific automobiles from vehicle photos.
After completing the above steps, use the segmentation technique to identify the license text by comparing patterns and OCR. The system can accurately and quickly identify the driver's license from the vehicle image.
Nonlinear Optimization for Optimal Fault Detection and Classification in Power Systems Using Novel Semi-Supervised KNN Approach
Authors: Debshree Bhattacharya
Published: November 2024

Power system reliability is paramount for ensuring uninterrupted energy supply. This paper addresses the critical challenge of accurate fault detection and classification in power systems, particularly in the context of increasing renewable energy integration. Traditional protection systems, while effective, often struggle to adapt to evolving grid conditions and emerging fault types. To overcome the limitations of traditional supervised learning methods, which require large amounts of labelled data, we propose a novel semi-supervised learning approach based on the K-Nearest Neighbor (KNN) algorithm for robust fault detection and classification in power systems. The proposed method leverages the strengths of semi-supervised learning, effectively utilizing both labelled and unlabelled data to improve classification accuracy of transmission line faults. By iteratively training the KNN model on an expanding dataset, the system can adapt to changing fault patterns and enhance its performance over time. To enhance the robustness and accuracy of fault classification, we formulate the fault detection and classification problem as a nonlinear variational inequality (NVI)-based problem. This NVI framework captures the inherent nonlinearity of power system dynamics and the variability of fault events, enabling the KNN classifier to accurately identify faults under diverse operating conditions. The proposed method is evaluated through extensive simulations in MATLAB. The effectiveness of the proposed approach is demonstrated through simulations on a 200 km transmission line under various fault conditions. The results indicate that the KNN classifier can accurately identify and categorize different fault types, contributing to improved system reliability and operational efficiency. This research provides a promising solution for enhancing power system protection, enabling timely fault detection and response, and ultimately minimizing system disruptions.
Enhancing Low Energy Technology Performance: Optimizing Low Noise Amplifier through Stabilization and Input-Output Component Matching Design
Authors: Shinjini Yadav
Published: November 2024

Advancements in technology, marked by increased miniaturization, integration, and accessibility, have propelled the widespread adoption of CMOS technology in wireless applications. This trend has extended beyond consumer electronics to encompass diverse fields, including medical applications like body temperature and heart rate monitoring. The rising demand for swift data monitoring via portable wireless devices has underscored the importance of energy-efficient components like LNAs. A thorough literature review will be used to analyze current trends in LNA requirements and designs, one of the many goals of the suggested research article. The study also seeks to identify the best LNA structure and parameters for particular application spectra or frequency bands.
Stability Analysis of Two Point Boundary Value Problem on Time Scales
Authors: G. V. Ramana
Published: November 2024

Lakshmi Kantham and M. Rama Mohana Rao contributed a lot for the theory of integro- differential equations of Volterra type and its various properties. In this paper, an attempt is made to explore the existence of a solution for a matrix integro-dynamical equation of Volterra type (MIDT) with two-point boundary value problem (TBVP) in terms of Green’s matrix (GM) on time scales (TS). And properties of GM and stability of the system on TS are discussed.
Evaluating the Role of Data Privacy Regulations in Secure Software Development Life Cycles (SDLC)
Authors: Shaurya Jain
Published: November 2024

In today’s data-driven landscape, data privacy regulations such as GDPR, CCPA, and HIPAA play a pivotal role in shaping secure software development practices. This study examines how these regulations influence each phase of the Software Development Life Cycle (SDLC), resulting in a Secure Software Development Life Cycle (S-SDLC) that emphasizes privacy by design. Using a mixed-methods approach—including quantitative surveys, qualitative interviews, and case studies from healthcare, e-commerce, and finance sectors—this research explores the adoption of privacy measures across SDLC phases, highlights compliance challenges, and identifies best practices. The findings reveal that while privacy regulations enhance security, user trust, and risk management, they also pose challenges, especially within agile development environments where balancing compliance with flexibility is complex. To address these issues, this study recommends adopting privacy automation tools, agile-compatible privacy frameworks, and cross-functional privacy teams to optimize compliance efforts. This research contributes to understanding how data privacy regulations drive a proactive, privacy-centric approach in software development, ensuring that security and compliance become integral to digital innovation.
A Connection Between Knot Theory and Linear Algebra
Authors: Ayman Abouzaid Ezelarab Aboufattoum
Published: November 2024

In knot theory literature, a lot of references treated the topic of computing the Alexander polynomial and proved that the Alexander polynomial is a knot invariant. In this paper, we concentrate on an unconventional connection between knot theory and linear algebra in computing the Alexander polynomial and introduce an approach depending linear algebra tools to show that the Alexander polynomial is a knot invariant. Also, we connect this approach to the way of Fox coloring to suggest another way to prove that the 3-coloring is a knot invariant and confirm what is already known about the 3-coloring of the trefoil and the figure eight knots.
Harvest Harmony: Integrating Linear and Nonlinear Machine Learning Models for Precision Crop Recommendation
Authors: B Ramesh Naidu
Published: November 2024

This research presents a comprehensive crop recommendation system that combines linear and nonlinear machine learning models to forecast suitable crops based on soil and environmental factors. The study evaluates the performance of Logistic Regression (linear) alongside Random Forest, Gradient Boosting, and XGBoost (nonlinear models) in delivering accurate crop suggestions. Precision, recall, and F1-score metrics are employed to assess the models' effectiveness. The dataset incorporates vital soil characteristics, such as nitrogen, phosphorus, and potassium levels, and environmental parameters like temperature, humidity, pH, and rainfall. The results demonstrate that while linear models like Logistic Regression offer simplicity, nonlinear models such as XGBoost excel in capturing complex data patterns, providing more precise recommendations. This dual-model strategy empowers farmers to make informed decisions tailored to specific farming conditions, boosting agricultural productivity. The study underscores the importance of integrating both linear and nonlinear machine learning techniques to achieve optimal crop recommendations, encouraging resource efficiency and sustainable agricultural practices.
Homomorphic Encryption and Secure Multi-Party Computation: Mathematical Tools for Privacy-Preserving Data Analysis in the Cloud
Authors: Mayuri Arun Gaikwad
Published: November 2024

With more and more people using cloud computing and storing and handling data remotely, protecting the privacy and safety of private data has become very important. Homomorphic encryption and safe multi-party computing (MPC) are two new mathematics tools that offer strong ways to analyze data in the cloud while protecting privacy.  When you use homomorphic encryption, you can do calculations directly on protected data, so you can process data securely without having to decode private data. This method makes sure that data stays protected while operations are being done, keeping it safe from people who shouldn't have access to it or seeing it. Cloud service providers can use homomorphic encryption to do different types of analysis on protected data, like collecting, searching, and machine learning, without revealing the private information that lies beneath. Secure multi-party computation protects privacy in situations where multiple people work together to analyze data. MPC allows for joint analysis without letting other people see individual datasets by spreading computations across multiple entities, each of which holds a piece of the data. MPC uses cryptographic protocols and methods to make sure that processes are done without revealing private inputs. This lets multiple people work together to analyze data while keeping privacy.  These math tools can be used for many different types of data analysis jobs in the cloud, such as predictive modeling, machine learning, and statistical analysis. They also make it safe for different groups to share and work together on data, like businesses, academics, and people, without putting data protection at risk.  There are still problems with how homomorphic encryption and safe MPC can be used in the real world and how they can be scaled up. These problems are mostly related to the amount of work that needs to be done and how efficiently it works. The main goal of ongoing study is to create improved methods and programs that will make these techniques work better and be easier to use in the real world.
Secure Routing and Hybrid SqueezenetFQuantumNN for Intrusion Detection in MANET
Authors: Sunita Usturge
Published: November 2024

An efficient Intrusion Detection System (IDS) is essential to secure Mobile Ad-hoc Networks (MANETs) against malevolent attacks. The present IDS still faces challenges in enhancing detection accuracy and in decreasing the false alarm rate. To overcome the above issues, a secure routing protocol for intrusion detection in MANET has been proposed here. In the proposed model, initially, MANET is simulated, which is followed by routing based on Dynamic Source Routing (DSR) routing protocol. Then, intrusion detection is done at Base Station (BS). At the BS, the log files are acquired from the dataset at first and it is normalized using Quintile normalization to transform the data into a structured form. Then, the desired features are selected in the feature selection phase using Wave–Hedges metrics for removing redundant and irrelevant data. Finally, intrusion detection is performed using the proposed hybrid SqueezenetFQuantum Neural Network (SqueezenetFQuantumNN) which is devised by the fusion of Squeeze Net and Quantum Neural Network (QuantumNN). The effectiveness of the proposed SqueezenetFQuantumNN is measured depending on evaluation metrics and is found to attain accuracy of 95.72%, True Positive Rate (TPR) of 93.42%, and True Negative Rate (TNR) of 91.39%.
Improving Glioblastoma Multiforme Recurrence Prediction through Integrated Radiomics and Deep Learning Techniques
Authors: Disha Sushant Wankhede
Published: November 2024

Glioblastoma multiforme (GBM) is one of the most lethal forms of brain cancer, with a five-year survival rate of only 4% to 5%. The recurrence rate is alarmingly high, reaching up to 90%. While tumor-treating fields have shown potential in extending survival, their efficacy in treating recurrent GBM remains limited. This study aims to leverage Deep Learning (DNN) to predict the recurrence of GBM in patients, both pre- and post-surgery. Utilizing advanced computational techniques, this research employs radiomics to analyze brain tumor images, aiding clinicians in identifying tumor spread, predicting post-surgical recurrence, and estimating patient survival. Pre-surgery, Multi-Parametric Magnetic Resonance Imaging (MP-MRI) scans are used to detect tumor locations and forecast potential recurrences. To enhance image processing, Z-score normalization and spatial resampling are applied. Additionally, a model was developed to address the issue of imbalanced data in medical imaging. The study utilized Contrast-Enhanced T1-Weighted Imaging (CE-T1WI) MRI to assess treatment effectiveness and predict recurrence-free survival. A Deep Neural Network was trained to forecast tumor recurrence, identifying patients at risk of early recurrence. Feature extraction from brain images was performed using the Inheritable Bi-Objective Combinatorial Genetic Algorithm. The accuracy of the recurrence predictions was validated and compared against other models, including CNN Inception-V3, CNN AlexNet, and VGG16, using the Python programming language. Results indicate that the proposed method surpasses existing techniques by 3%, 4%, and 5% in accuracy, specificity, and sensitivity, respectively. This research demonstrates that in a retrospective patient population, predictions of patient survival and time to recurrence exhibit high sensitivity, specificity, and accuracy, offering a promising tool for improving GBM management and patient outcomes.
Localizing Mobile Target using Kalman Filtering and Least Square Boosting Ensemble Learning based Approach
Authors: Mahadev Mahajan
Published: November 2024

Trilateration-based target localization using received signal strengths (RSS) within wireless sensor networks (WSN) often results in inaccurate location estimates due to the considerable fluctuations in RSS measurements encountered in indoor environments. Enhancing the precision of RSS-based localization systems has been a primary area of interest in extensive research efforts. This study introduces two range-free algorithms, Ensemble Learning (EL) and EL+KF, which leverage RSS measurements for localization. In contrast to trilateration, the EL- based localization approach enables the direct estimation of target locations based on field measurements, eliminating the need for distance calculations. Notably, unlike other cutting-edge localization and tracking (L&T) scheme like the support vector regression (SVR), the LSBoost (Least Squares Boosting) EL based localization architecture can be trained very quickly using RSS measurements to determine the mobile target's position. Furthermore, the proposed EL-based localization scheme incorporates the Kalman filter (KF) to achieve additional refinement in target location estimates. To assess the localization effectiveness of these proposed algorithms in the presence of noisy RF channels and dynamic target motion models, rigorous simulations were conducted. Thanks to the robust generalization capabilities of EL, the simulation results reveal that the presented EL-based localization algorithms exhibit superior performance compared to trilateration and the SVR-based localization scheme, particularly in terms of indoor localization accuracy.
Information Theory and Coding Techniques for 5G Wireless Communication Systems: Towards Efficient Spectrum Utilization
Authors: Rupesh Mahajan
Published: November 2024

The introduction of 5G wireless communication networks has initiated a new period of connectivity, offering extremely high data rates, minimal delay, and extensive device compatibility. However, in order to fully use the capabilities of 5G, it is crucial to prioritize the effective exploitation of spectrum. Efficiency in data transmission is achieved by the optimization of data transmission, error minimization, and maximization of throughput, which are facilitated by information theory and coding techniques. This work examines the convergence of information theory and coding methods in the context of 5G wireless communication systems, with an emphasis on improving the efficiency of spectrum usage. The core of information theory is centered around the notion of entropy, which measures the level of uncertainty linked to a random variable. Information theory offers valuable insights into the underlying constraints of communication networks by utilizing concepts like channel capacity and error-correcting codes. When it comes to 5G, it is crucial to comprehend these limitations in order to develop transmission strategies that effectively utilize the spectrum resources at hand. Coding techniques, such as forward error correction (FEC) and channel coding, are essential for reducing the impact of noise, interference, and fading in wireless channels. Forward Error Correction (FEC) is a technique that enhances the reliability of transmitted data by introducing redundancy. This allows receivers to repair errors without requiring retransmissions, thus increasing the efficiency of the available spectrum. Advanced coding techniques such as low-density parity-check (LDPC) codes and polar codes are crucial for delivering high data transfer rates and reliability in 5G networks. By combining various access strategies, such as orthogonal frequency-division multiple access (OFDMA) and non-orthogonal multiple access (NOMA), along with advanced coding schemes, we can achieve higher spectral efficiency and increase the capacity for users. These strategies facilitate the optimal distribution of resources, enabling multiple users to efficiently share the same spectrum while satisfying their quality-of-service demands. Information theory and coding.