bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Conjugate Gradient Methods for Image Restoration
Authors: Zeyad M. Abdullah
Published: November 2024

The image restoration problem can be solved with the right techniques and tools. Although the quality of images may be lowered throughout the data collecting procedure, there are ways to do so. Clarity may be enhanced as a result of the procedure. It is the goal of image restoration to restore the original image from the damaged data. Here are some examples of applications in forensic research, picture restoration, medical imaging, astronomical imaging, and more. It can be advantageous to enhance image quality as much as possible, which justifies the expense and difficulty of the restoration methods.  The conjugate gradient method is a numerical optimization technique that places emphasis on the conjugate of the coefficient in order to determine the optimal solution. The current study introduces a novel quadratic function-based coefficient conjugate gradient methodology for the removal of impulse noise from pictures. Global convergence has been shown to occur with the suggested method. The numerical results indicate that the proposed methods have high efficiency and global convergence in image restoration problems compared to the Fletcher-Reevess (FR) method.
On the f-Index of Some Special Graphs
Authors: Haveen J. Ahmed
Published: November 2024

Articles on the topological indices based on the distance between any two vertices on the graph have been intensively increasing recently, as there are several distance formulas in graph theory depending on what researchers have put. The ordinary distance d(u,v) is the length of the shortest path between vertices u and v in a connected graph.  In this paper, the new distance defined on a connected graph is  f-distance based on the ordinary distance. This research includes giving several characteristics related to f-distance after providing a definition of f-distance, in addition to finding f-index and average f-distance for some private graphs.
Fuzzy Relation Properties and Fuzzy Composition Deep Explain with RTAs
Authors: Sophiya A
Published: November 2024

As the number of fatalities on the roads rises, analysts forced to look for models that might foretell a driver's tendency for traffic accidents (RTAs). This study intends to investigate the relationship between drivers' abilities to judge speed and available space in relation to the occurrence of RTAs. The Circulant Composition of Fuzzy Relations Inference System employed as the foundation for the approach used for this goal (CCFRIS). The first CCFRIS's inputs related to drivers' capacity for speed assessment. 200 novice drivers participated in the trial, which tested these skills at test speeds of 35, 55, and 75 km/h. In the driving simulator, the participants evaluated the aforementioned speed numbers from four distinct vantage points. On the other side, space evaluation capabilities of drivers make up the second CCFRIS's inputs. The identical set of drivers underwent both 2D and 3D space evaluation exams. The quantity of RTAs a motorist has experienced is the third CCFRIS structure taken into account. The research found that the space assessment qualities explained participation in RTAs better than the drivers' speed assessment abilities after testing three proposed CCFRIS on empirical data. This study also looks into binary fuzzy relations and fuzzy relation composition characteristics.
Data Encryption and Decryption using Some Integral Transforms
Authors: Gobburi Rekha
Published: November 2024

Ever since the beginning of time, people have valued privacy and information security. Information security is becoming increasingly vital in our digital society. The authentication system's protection of hidden information is crucial. One of the most significant methods used to protect data and secure message transmission is cryptography. There are the two components for the encoding and decoding process which includes the algorithm and key. Cryptography is made more secured by utilising a key for both encryption and decryption.
Introduction: In many circumstances, the sender wants to keep the message private from the public or from unauthorised users. Information is protected using encryption, and the original message is unlocked using decryption. Various Techniques are found in literature and further we extend the findings by combining the Elzaki and Aboodh transforms to encrypt the message and apply their inverse transforms to decrypt it with the ASCII values under modulo 255.
Objectives: In this paper we focused on two stages of encryption and two stages of decryption under modulo 255.
Methods: Using different transformation techniques, we have encrypted and decrypted the data. One is Laplace transform technique while the other is Aboodh-Elzaki transform technique.
Results: The text message is converted to coded message by using both Laplace transform technique and Aboodh-Elzaki transforms technique.
Conclusions: In the current paper, we present a novel method that encrypts data using Laplace transform and decrypts it using the private key, which is under modulo255. The two integral transforms are used to increase the message's level of security during decryption by selecting a new function and altering the other elements.
K- Hausdorff Separation Axiom in Second Order Interval Valued Fuzzy Topological Spaces
Authors: Bhavani Gokila D
Published: November 2024

We studied and introduced a concept SIVFT then present the concept of SIVF subspace and SIVF product topology in SIVF topological spaces. K-Hausdorff Separation Axiom in SIVF topological spaces and its basics are studied.
On Certain Fixed Points for (α,φ,F)-Contraction on S_b- Metric Spaces with Applications
Authors: C. Ushabhavani
Published: November 2024

This work establishes unique fixed point theorems (UFPT) for self mapping in complete S_b-metric spaces (S_b-MS) with the concept of (α,φ,F)-contraction in the context of S_b-MS Furthermore, we show how the results may be used and present applications to integral equations and homotopy theory.
Introduction: In previous work authors were discussed fixed pointon various metric spaces with F -contractions, α-type almost -F- contractions, α-type F -Suzuki contractions, (φ, F)-contraction, F - weak contractions, α −ψ-contractive type, α −ψ-Meir-Keeler contractive mapping, α-ratonal contractive mappings, (α, β) − (φ,ψ)-rational contractive type mappings.
Objectives: Finding the unique fixed point for self mapping in S_b-MS, via (α,φ,F)-contraction.
Methods: with the help of α- admisible mapping, (φ, F)-contraction, α type F -contraction and (α.φ,F)-contraction we have fixed point findings in complete S_b-metric spaces.
Results: we obtained unique fixed point results via (α,φ,F) contractive type for self mapping in complete S_b-MS.
Conclusions:  In this article, Contractive mappings of the (α,φ,F) type are used to show certain fixedpoint results in the context of S_b-MS, along with appropriate example that illustrate the key findings. Appications to integral equations and homotopy are also offered.
Deep Learning Methods for Detecting Chest X-Ray and CT Scan Abnormalities
Authors: M.Sangeetha
Published: November 2024

Deep learning's quick development has created new opportunities to improve medical image analysis, especially in the identification of anomalies in chest CT and X-ray scans. This work investigates several deep learning techniques designed for this particular purpose to enhance the efficiency and accuracy of diagnosis in medical settings. We explore the use of 3D CNNs, transfer learning, and convolutional neural networks, or CNNs, for the analysis of volumetric CT scan information as well as 2D chest X-ray pictures. Comparative analyses show the benefits and drawbacks of various deep learning architectures for identifying a variety of anomalies, including tumors, tumors in the lungs, pneumonia, and other diseases. We also go over the significance of preprocessing methods, assessment metrics specifically designed for medical picture analysis, and dataset preparation. The results highlight how deep learning has the potential to revolutionize chest imaging diagnostics by facilitating the quicker and more accurate identification of anomalies, which will enhance patient outcomes and the effectiveness of healthcare delivery. To spur additional developments in the deep learning-powered analysis of medical images for chest problems, future research topics, and obstacles in this area are also covered.
BUGTGNN: Bayesian Uncertainty and Game Theory for Enhancing Graph Neural Networks through Mathematical Analysis
Authors: Geetanjali Kale (Rao)
Published: November 2024

The ongoing COVID-19 pandemic has emphasized the critical need for rapid and accurate diagnostic methods. Precise classification of chest X-ray images into COVID-19 and non-COVID-19 cases serves as a pivotal tool in effective disease management and control. Existing methods often suffer from trade-offs between accuracy, precision, and computational efficiency, hindering their practical utility. Current approaches mainly rely on traditional machine learning algorithms or Convolutional Neural Networks (CNNs), which while effective, still present limitations in terms of sensitivity, specificity, and computational speed. These constraints necessitate the exploration of innovative techniques for improving classification metrics across multiple dimensions. In this work, we introduce a novel framework for optimizing Graph Neural Networks (GNNs) through mathematical analysis, specifically incorporating spectral methods, dynamic graph sparsification, game-theoretic attention mechanisms, Bayesian uncertainty models, and advanced graph partitioning techniques. When applied to the classification of COVID-19 chest X-rays, our model demonstrated significant improvements—increasing precision by 8.3%, accuracy by 8.5%, recall by 4.9%, specificity by 4.5%, and the Area Under the Curve (AUC) by 5.9%, while simultaneously reducing computational delay by 10.5% across multiple datasets. The proposed optimization strategies showcase the power of interdisciplinary approaches in advancing machine learning techniques for medical applications. The demonstrated improvements in classification metrics and computational efficiency highlight the model's potential for broader adoption in healthcare settings, providing a robust, fast, and more accurate tool for COVID-19 diagnosis.
Leveraging Faster CNN (F-CNN) for Effective Breast Cancer Classification
Authors: Ms. Jyoti Pandurang Kshirsagar
Published: November 2024

Within the scope of this work, a novel classification method for the diagnosis of breast cancer that is based on deep learning is also described. In this particular instance of breast cancer, which is the most common form of cancer in females, early detection is absolutely necessary in order to get better treatment outcomes. Notwithstanding their effectiveness, traditional diagnostic techniques have drawbacks such high expenses and possible errors. The high dimensionality and instability in tumor morphology that are particular problems with breast cancer imaging are intended to be addressed by the suggested techniques. Using publically available datasets for rigorous training and validation, a bespoke deep learning model is designed and implemented, and an extensive evaluation of current deep learning methodologies is conducted as part of the research. The model's accuracy and resilience are significantly improved when compared to the performance of existing classification algorithms. To enhance diagnosis accuracy in the characterization of breast cancer, this study makes utilizes of deep learning, more especially faster convolutional neural networks. The investigation also looks at the model's clinical usefulness, providing information about how it might be incorporated into diagnostic procedures. According to the findings, it appears that the highlighted methodology has the potential to transform the diagnosis of breast cancer by offering a dependable and automated solution that can improve early detection and patient outcomes. In just 3 epochs, the model obtained a remarkable accuracy of 92% on DDSM dataset.
Relatively Prime Domination Number in Triangular Snake Graphs
Authors: A. Anat Jaslin Jini
Published: November 2024

A set S⊆V is said to be relatively prime dominating set if it is a dominating set with at least two elements and for every pair of vertices u and v in S, (deg⁡(u),deg⁡〖(v))〗=1 and the minimum cardinality of a relatively prime dominating set is called relatively prime domination number and it is denoted by γ_rpd (G). If there is no such pair exist, then γ_rpd (G)=0. For a finite undirected graph G(V,E) and a subset  V, the switching of G by  is defined as the graph  (V,  ) which is obtained from G by removing all edges between  and its complement V-  and adding as edges all non-edges between  and V- . This article delves into the discussion of the relatively prime domination number on triangular snake graphs and their complements. The findings reveal that for triangular snake graphs, the relatively prime domination number γ_rpd(G^v) equals either 2 or 3. Similarly, for alternate triangular snake graphs, the γ_rpd(G^v) is determined to be 2 or 3. In the case of double triangular snake graphs, the relatively prime domination number γ_rpd(G^v) is established as 2, 3, 4, or 6, while for double alternate triangular snake graphs, it is 2, 3, or 4. Notably, the complements of alternate triangular, double triangular, and double alternate triangular snake graphs exhibit a relatively prime domination number of 2.
Adaptive QoS-aware Congestion Window Adaptation through Contention Detection in Wireless Networks
Authors: Priya Kumari
Published: November 2024

In wireless networks, maintaining Quality of Service (QoS) while managing congestion effectively is a critical challenge due to changing network conditions. Quality of service (QoS) technique operates on a network to run high-priority applications and traffic reliably despite limited network capacity. This paper proposes an adaptive QoS-aware congestion window adaptation mechanism that leverages contention detection to optimize performance in wireless networks. A cooperative relay node has also been introduced to relay safety messages that failed to reach the destination. According to the proposed method, congestion window size is dynamically adjusted to maintain QoS standards even for high-priority traffic under varying network conditions. Simulation results obtained for our model show that using a dynamic window enhances the performance of the system by allowing higher throughput, stable performance, a decrease in delay, and a higher packet success rate.
Formation of New Failure Censored Reliability LifeTest using Neutrosophic Generalized Exponential Distribution -Neutrosophic Approach
Authors: S.Jayalakshmi
Published: November 2024

The statistical quality control techniques help in maintaining and monitoring the quality standards of manufacturing products. It decides whether to accept or reject the product's quality after inspecting several quantities. In Classical statistics, the sampling plan uses deterministic data to produce a reliable lot decision. In certain situations, the information may be ambiguous, inaccurate, or insufficient to make an accurate determination about the quality of the submitted lots. In that case, Neutrosophic Statistics can be used to analyse the data and produce an appropriate conclusion from ambiguous, uncertain data. In this paper, we propose the Neutrosophic failure-censored reliability test plan, which is considered the Neutrosophic plan of parameters. Assumes that the failure lifetimes follow a Neutrosophic Generalized Exponential Distribution (NGED). In order to calculate the producer and consumer risks, Neutrosophic Statistical Interval Method is used. Example is provided to illustrate tables and numerical illustrations.
Automated Sleepiness Detection via EEG Brainwave Analysis: A Nonlinear Ensemble Approach with Optimized Hyper-Tuning Strategies
Authors: K. Srujan Raju
Published: November 2024

Introduction: Automated sleepiness detection plays a vital role in ensuring driving-related safety, monitoring physical well-being, worker safety, and various other applications. The ability to detect and respond to signs of sleepiness is essential in preventing accidents and improving overall safety and productivity.
Objectives: The primary objective of this work was to explore avenues for enhancing the performance of automated sleepiness detection systems using advanced machine learning techniques. Specifically, the study aimed to determine the improvement potential in accuracy through the application of fine-tuning techniques across several machine learning algorithms.
Methods: A range of machine learning algorithms, including LightGBM, XGBoost, CatBoost, Extra Trees Classifier (ETC), and Random Forest (RF), were employed to evaluate their effectiveness in sleepiness detection. Fine-tuning techniques were applied to these boosting algorithms to assess their impact on performance improvements.
Results: The results revealed significant performance enhancements, particularly for boosting algorithms. CatBoost emerged as the top performer, achieving an accuracy score of 83%, demonstrating its capability in the domain of sleepiness detection. The improvements achieved through fine-tuning were substantial across all algorithms evaluated.
Conclusions: This study highlights the potential of advanced machine learning techniques to make meaningful contributions to automated sleepiness detection systems. The results underscore the importance of fine-tuning in boosting algorithm performance and suggest a growing role for such technologies in improving safety, health, and productivity in various domains.
Suggesting Approximation and Exact Algorithms to Solve New Tri-Criteria Machine Scheduling Problems
Authors: Mohammed Muter
Published: November 2024

This study presents the multi criteria single-machine model. The machine scheduling problem (MSP) for n tasks on a single machine involves minimizing a function of three criteria: total completion time (C_j), maximum earliest (E_max), and tardiness (〖ΣT〗_j), This is an NP-hard issue.
Within this work's theoretical section, we present the mathematical formulation of The presented topic then highlights the usefulness of the dominance rule (DR), which may be used to develop effective solutions. While in the practical part, one of the important exact methods; The proposed MSP tricriteria are solved by applying the Branch and Bound (BAB) method, which finds a set of efficient solutions for 1//F(ΣC_j  ,ΣT_j  ,E_max)  up to n=100 jobs. The BAB approach finds the efficient solutions for the issue in an acceptable amount of time. In addition, we provide two heuristic approaches to address the problem in order to obtain appropriate approximations. The two proposed approaches' good performance is demonstrated by the practical experiments.
Different types of products on Cohesive Fuzzy Graph
Authors: S. Sheelarani
Published: November 2024

In this manuscript, we define the cartesian product, direct product, strong product and Semi-strong product of two cohesive fuzzy graphs, along with providing il-lustrative examples. Furthermore, we have established theorems using these products through rigorous proofs.
Solution of Partial Differential Equations and Their Application by Three Parameters of Integral Transform
Authors: Nour K. Salman
Published: November 2024

This article explains the Nour integral transform technique and denoted by "NO transform ", it's fundamental hypothesis and its power to solve the exact solution of partial differential operator equations (PDOE's) have been introduced and shown by the exact solution of various elementary engineering partial differential operator equations such as:  wave partial equation, heat partial equation, Laplace’s partial equation, telegraph partial equation, and Klein-Gordon partial equation.
Designing of Single Sampling Plan by Attributes Under the Conditions of Borel Distribution
Authors: Jayalakshmi S
Published: November 2024

Acceptance sampling is one of the widely used methodologies in the industry to sentence about the quality of a lot or lots based on random samples. In this modern industrial era, the manufacturing process is well monitored, hence the occurrence of defects is rare. However, the lots formed from a process, in practice, have quality variations, which occur due to random fluctuations. Therefore, the proportion of nonconforming units in the lots cannot be eliminated completely. In this situation, the appropriate distribution to model the number of defects is the Borel distribution. This paper aims to develop a single sampling plan by attributes when the number of defects follows the Borel distribution. Further, the operating characteristic function (OC) of the sampling plan is derived and comprehensive analysis of the performance of the proposed sampling plan is described through its OC curves. The procedure for determining the plan parameters using unity values with operating ratio as a measure of discrimination is discussed. The methodology for obtaining an optimal plan is also presented through a numerical illustration. And a comparative study between SSP under Borel distribution and Zero Truncated Poisson distribution has been done.
Interval Valued Fuzzy Number Matrices on Psoriasis
Authors: R.Divya
Published: November 2024

Psoriasis is common, over term diseases with no remedy. Datasets that are available for psoriasis describe information about the patients suffering with psoriasis diseases and without psoriasis diseases along with their symptoms like: Swollen joints, irritated skin spots, whitish scales on the red patches, Dry skin that may crack, burning sensations surrounding patches. In this paper we discuss various algorithm approaches of interval valued fuzzy number matrix that have been utilized for psoriasis diseases prediction.
The principle goal of this paper is to classify information and assist the consumer in extracting use complete statistics from information and easily identify an appropriate set of rules for correct predictive version from it. In addition, we prolonged our method inside the quarter addition and multiplication factors of interval valued fuzzy number matrix based on membership and non-membership function. Finally, we presented a decision-making problem based on one of the operations of Interval Valued Fuzzy Matrix.
Analytical Solution of Chemical Reaction Systems using Kinetic Models
Authors: Kaleeswari. S
Published: November 2024

It encompasses multiple model kinds, such as spatially distributed versus homogenous, discrete versus continuous, and stochastic versus determinist.  Among all chemical reactions, oscillating reactions are the most exciting.  Here, the reactant and auto catalyst are the two chemical species that make up the system.  The analysis of chemical reaction processes by mathematical approaches has also a long history.  We now shift our focus to mathematical frameworks for the deterministic kinetics of mass motion.  On the positive orthant, these models are systems of related nonlinear differential equations. Reaction-diffusion equations with a nonlinear term resembling Michaelis-Menten's enzymatic reaction kinetics form the basis of the model. In this work, the New Homotopy perturbation method yields a preliminary analytical solution for all values of parameter to the nonlinear differential equations analysing the concentrations in mass-action kinetics and Michaelis - Menten enzyme kinetics, also known as the hill binding kinetics.  A satisfactory level of agreement is found when these results are compared to the numerical result.
Neural Differential Equations: A Comprehensive Review and Applications
Authors: Anjali Nair
Published: November 2024

The field of neural differential equations has witnessed significant growth in the past decade, with a notable increase in published literature. This approach offers several advantages, including the ability to model complex, nonlinear systems and the potential for extrapolation beyond the observed data. There is a need of systematic overview in implementing the use of neural networks in solving differential equations and their special cases. This work is a discussion on architectures of neural networks used in NDEs, training methodologies and applications across various domains. The current work reviews and categorizes earlier study and gives guidelines to mathematicians, researchers in computer science and engineering.  Also, it includes advantages of NDEs and some current challenges and research questions in the field of NDEs.
Introduction: The field of neural differential equations has witnessed significant growth in the past decade, with a notable increase in published literature. This approach offers several advantages, including the ability to model complex, nonlinear systems and the potential for extrapolation beyond the observed data. There is a need of systematic overview in implementing the use of neural networks in solving differential equations and their special cases. This work is a discussion on architectures of neural networks used in NDEs, training methodologies and applications across various domains. The current work reviews and categorizes earlier study and gives guidelines to mathematicians, researchers in computer science and engineering.  Also, it includes advantages of NDEs and some current challenges and research questions in the field of NDEs.
Conclusions: The authors have discussed the observations of ODEs, Neural network and the literature review of NDEs. The overall framework of neural networks for solving differential equations is also covered in this paper. This paper also includes general features of neural algorithms for solving differential equations. Neural networks may approximate any continuous system and stabilize the training process with the help of suitable activation functions. It allows us to solve problems more quickly without wasting processing power or memory. The parallel nature of the network helps to minimize the complexity of the problems, as demonstrated by a comparison with the exact solution, along with modeling various dynamical processes. Additionally, one can solve fractional differential equations using Neural network and many more applications based on it.
Predictive Modelling of Marshall Stability using Multi Linear Regression: A Comprehensive Approach
Authors: Shobha Rani Nadupuru
Published: November 2024

Marshall Stability is a crucial parameter in the field of pavement engineering, serving as a key indicator of the strength and durability of asphalt mixtures. Accurate estimation of Marshall Stability is essential for ensuring the quality and performance of road pavements. This paper proposes the development of Multi Linear Regression (MLR) models as a predictive tool for estimating Marshall Stability based on various input parameters. A comprehensive dataset comprising a range of asphalt mixture compositions and Marshall Stability values is utilized for model training and validation. The input parameters considered in the MLR models include aggregate properties, binder characteristics, and mixture volumetrics. Through systematic feature selection and model refinement processes, robust MLR models are constructed to capture the complex relationships between input variables and Marshall Stability. The performance of the developed models is evaluated using statistical metrics such as coefficient of determination (R-squared), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrate the efficacy of the proposed MLR approach in accurately estimating Marshall Stability, thus providing a valuable tool for engineers and researcher.
Breakthroughs in Low-Cost Hydrogen Production Methods for Fuel Cells
Authors: Ramesh Krishnat Kavade
Published: November 2024

Traditional hydrogen production, like steam methane reforming (SMR), is limited by high greenhouse gas emissions and fossil fuel dependency. However, integrating renewable energy with innovative production technologies offers a more sustainable and cost-effective pathway. Electrolysis, powered by renewable sources such as wind and solar, is a promising method that has seen efficiency improvements through advancements in proton exchange membrane (PEM) and anion exchange membrane (AEM) technologies. Additionally, research into thermochemical water splitting, which utilizes high-temperature heat from solar or nuclear energy, presents a scalable alternative for industrial hydrogen production. Biological hydrogen production using microorganisms, such as algae and bacteria, offers further innovation by leveraging natural processes for hydrogen generation. The integration of renewable energy into hydrogen production not only lowers costs but also provides an effective means of energy storage, contributing to grid stability and reducing reliance on fossil fuels. This paper explores recent breakthroughs in low-cost hydrogen production methods, essential for advancing the hydrogen economy and promoting fuel cells as a clean energy solution.
Characterization of Bipolar Valued Vague Ideals of a Semiring
Authors: K. Anitha
Published: November 2024

Bipolar valued vague ideal of a semiring (BVVI) is described and examined in the present paper. Some characterization theorems are introduced in this paper and intersection, product and strongest bipolar valued vague relation of bipolar valued vague ideals (BVVI) of a semirings are introduced.
A Study on Catastrophic Retrial Feedback Queueing Network Model with Three Nodes in Steady State
Authors: S. Shanmugasundaram
Published: November 2024

Queueing networks, which are effectively used for performance analysis of various systems, including computer, communications, transportation networks, and manufacturing, are discussed in this article. Under the catastrophic condition, we evaluate a queueing network with feedback and retrial for single server. In the third node the customer can decide whether feedback or leave the system with probabilities t, u and 1-t-u. Also using Little's formula, we are determining the likelihood of having idle customers and determining n number of customers in the system. We are deriving  for the above model. The purpose of the numerical example gives the richness of our result and the feasibility of this concept.
Review and Analysis of Mathematical Approaches for Adaptive Authentication using Machine Learning
Authors: Ritu Agrawal
Published: November 2024

In today’s technological era, the combined problem of assuring user productivity and maintaining strong security standards has become quite a key challenge. As security considerations, have not to hurt productivity, significant efforts must be taken to achieve such goals. The notion of adaptive authentication comes into play to avoid friction in user experience so that a balance between security and user productivity can be maintained. Traditional authentication frameworks are becoming more obsolete due to their ineffectiveness in incorporating additional risk criteria such as location, network type, and operating system into the authentication management process. Adaptive authentication is a combination of authentication and machine learning (ML) technologies that can help in realizing secure intelligent systems. The goal of this research is to provide a complete review of the current applications of various machine learning algorithms often employed in adaptive authentication techniques. Throughout this study, the advantages, limits, and suggestions for future research will be thoroughly discussed.
A Comparative Analysis of Memory Models: SC, TSO, and ARM in Concurrent Programming
Authors: Mohammed H. Abdulwahhab
Published: November 2024

Concurrent programming has long been a challenge due to the complex interactions between processors and shared memory. The intuition of programmers often diverges from the actual behavior of concurrent programs due to factors such as modern processor technologies, compiler optimizations, and performance-driven data store implementations. To address this discrepancy, memory models were introduced to formally define the permissible and prohibited behaviors in concurrent program execution. Memory models can be broadly categorized into strong and weak models. Sequential Consistency (SC) is a strong model that preserves the order of events in each thread or process, aligning with programmers' expectations. In contrast, weak models, such as Total Store Order (TSO) and ARM, allow for relaxed ordering, potentially leading to unexpected behaviors. This paper presents a practical comparison of SC, TSO, and ARM memory models. A suite of six litmus tests were executed on architectures implementing each model to highlight their differences. The results were analyzed using the axiomatic models of each architecture to understand why certain behaviors were accepted or rejected. The herd tool was employed to simulate memory model behavior. The findings revealed a significant disparity in the acceptance of behaviors across the three models. SC emerged as the most restrictive, followed by TSO, while ARM demonstrated the least stringent acceptance criteria. These results underscore the importance of understanding memory model nuances when developing concurrent programs to ensure correct and predictable behavior.
Developing Computational Thinking through Mathematics Learning in Indonesia: Scientific Mapping and Bibliometric Analysis
Authors: Ahmad Gufron
Published: November 2024

This research aims to explore and map the teaching methods used in developing Computational Thinking (CT) through mathematics learning in Indonesia, using a bibliometric analysis approach. Based on a literature review from Scopus and Sprott-indexed databases, this research analyzes trends, contributions and challenges in the implementation of CT at various levels of education. Teaching methods such as Scratch, Problem-Based Learning (PBL), Augmented Reality (AR), and robotics were identified as the most effective strategies in improving students' computational thinking skills. The findings show that the majority of publications published in educational and CT-related sources about teaching mathematics are mostly about teaching computational skills and teaching computer programming using practice and algorithmic thinking, although these methods have had a significant impact, some limitations, such as lack of instrumentation, limited technological resources, and curriculum adaptation are still challenges. This analysis also highlights opportunities for further research in developing more comprehensive evaluation instruments and improving technology access in computational education. It is hoped that the results of this research can provide guidance for educators, policymakers and researchers to increase the integration of CT in the mathematics curriculum, as well as expand the scope of computational research in the Indonesian educational context. This research opens new insights to overcome barriers and optimize the potential of CT in the future.
Nonlinear Reinforcement Learning-Based Dynamic Test Case Prioritization with Anomaly Detection for Continuous Integration
Authors: Srinivasa Rao Kongarana
Published: November 2024

This research article introduces DynamicR-TCP, a nonlinear approach to dynamic test case prioritization (TCP) leveraging reinforcement learning and anomaly detection. Designed to enhance the efficiency of software testing in continuous integration (CI) environments, the proposed model dynamically adapts to changing conditions by learning complex patterns from historical test data. A reinforcement learning agent, employing policy and value networks, guides nonlinear prioritization by optimizing the sequence of test case executions. The model integrates a sliding window strategy for adaptive focus on smaller test subsets and uses an experience replay buffer for iterative learning. An anomaly detection layer monitors test outcomes for deviations, triggering retraining when needed to maintain reliability. Experimental evaluation using the Defects4J dataset demonstrates the model’s superior fault detection rates, faster time to first fault, and higher computational efficiency compared to contemporary methods. An ablation study highlights the nonlinear synergy among the key components—reinforcement learning, sliding windows, and anomaly detection—showing significant performance gains when combined. This comprehensive framework advances software testing by providing an adaptive, efficient, and scalable solution, ensuring robust performance in dynamic CI environments.
The Role of Optimization Techniques in Advancing Big Data Analytics : A Survey
Authors: E.Ramesh Babu
Published: November 2024

In the era of data proliferation, big data analytics has emerged as a vital tool for organizations seeking actionable insights from vast and diverse datasets. This paper surveys the criucial part of optimization techniques in enhancing the performance and efficiency of big data analytics. By addressing the challenges due by the volume, velocity, and variety of big data, optimization techniques enable more efficient resource utilization, faster decision-making, and improved predictive accuracy. Key applications in the areas such as healthcare, finance, smart cities demonstrate the transformative advantage of combining big data analytics with advanced optimization strategies. This survey highlights the pivotal role optimization plays in scaling data-driven insights, ensuring organizations can fully harness the power of their data.
GENERALIZED α-ADMISSIBLE ALMOST z-CONTRACTIONS INVOLVING SIMULATION FUNCTIONS IN A METRIC SPACE
Authors: DIPTI
Published: November 2024

In this paper, we present some fixed point results in complete metric spaces using generalized α-admissible mappings embeddedin the simulation function. These results serve to generalize and unifyseveral related fixed point results found in the existing literature. Tovalidate our findings, we provide a specific example illustrating the application of these results.
A New Type of (ϵ)- Lorentzian Para-Sasakian Manifolds
Authors: Shadab Ahmad Khan
Published: November 2024

The current investigation commences by introducing a novel category termed (ϵ)-Lorentzian para-Sasakian manifolds, employing the generalized symmetric metric connection of a specific type(α,β). Several fundamental outcomes concerning with these manifolds are derived. Subsequently, we delve into the examination of conformally flat and Weyl-semi-symmetric (ϵ)-Lorentzian para-Sasakian manifolds, utilizing the generalized symmetric metric connection of the type(α,β).
Advanced Automated Number Plate Recognizer using Machine Learning Technique
Authors: Gopal D. Upadhye
Published: November 2024

The paper presents a highly accurate automated number plate recognition (ANPR) algorithm designed to correctly recognize Indian license plates with over 99.5% accuracy. The system utilizes OpenCV, Python, and machine learning models in combination to achieve this high level of precision. The algorithm captures and processes images to recognize and identify license plates, including the colours from the plates. Initial plate recognition is performed using Haar cascades, which are subsequently transferred to YOLO v3, enhancing both accuracy and speed. The system incorporates sophisticated image pre-processing techniques—including grayscale adjustment, thresholding, erosion, detail, and contour detection—to ensure images are optimized for character separation and recognition. This integrated approach not only increases recognition rates but also handles images more efficiently, particularly in scenarios where traditional systems may fail. As a result, it paves the way for robust ANPR implementation in dynamic environments.
Approximation of Functions by n-Euler Product Means of Fourier Series and its Conjugate
Authors: Md Hadish
Published: November 2024

Our research in this work produced new theorems on approximation of functions by n-Eulers product means of Fourier series and its conjugate Fourier series. This new results generalize the results of [5], [7] and [9].
Microwave Propagation in Circular Quarter Wave Transformer made of Teflon and K-12 Composite
Authors: Surendra Kumar
Published: November 2024

Introduction: The business is developing at an extremely quick rate and the prerequisite for the recently made material is generally there for the high-recurrence applications. There are different polymers and clay materials and there are composites currently accessible in the business for high-recurrence applications. Polymer ceramic composites are being used for high frequency applications. The quarter-wave transformer offers a relatively simple method of impedance matching for many RF applications. Low loss circular quarter wave transformer is important for the circular microwave devices like phase shifters antennas etc which is realizable using Fused silica and K12 polymer ceramic composites.
Objectives: Objective of this research work is simulation and fabrication of the circular quarter wave transformer at X-band using TEFLON-K-12 polymer ceramic composites.
Methods: The Teflon and K-12 composite was used to fabricate the circular Quater wave transformer for microwave applications. The composite slab was machined to create the bare circular rod. The bare quarter wave transformer was sputtered in a closed chamber in the silver target. The sputtered rod creates a circular Quater wave transformer that is filled with Teflon-K-12 composite.
Results: The insertion loss of the circular Quater wave transformer was found 0.05 dB.  Thus, a circular Quater wave transformer is formed with polymer and ceramic composite a new material for low loss microwave applications. The practical value holds good in agreement with the theoretical approaches.
Conclusions: The Teflon and K-12 composite was used to fabricate the circular Quater wave transformer for microwave applications. The measured S –parameter results of the circular Quater wave transformer were found satisfactory, and it is found suitable for the low loss microwave propagation. The quarter wave transformer is able to match impedances 50 ohms and 100 ohms.
Design and Evaluation of Electrical Protection for Grid-Connected Microgrid with Renewable Energy Resources
Authors: Shailesh Madhavrao Deshmukh
Published: November 2024

The objective of this article was to establish the dependability of the Over Current protection (OCP) technique in safeguarding microgrids that use inverter connected Renewable Energy Sources (RES) for lower-voltage distribution channels.  The simulation program was used to conduct OCP system experiments to demonstrate its dependability. The simulations included contrasting the system's performance in grid-connected mode, both with and without RES production, and in the island state.  The calculations are performed with a lower-voltage distributing system design.  The average relay tripping time for single line-to-ground (SLG) faults via grid mode, without RES systems, is 0.131 seconds. For line-to-line (LLL) faults, the mean tripping time is 0.121 seconds. Regarding RES generators, the mean relay tripping duration climbed to 0.199 seconds and 0.135 seconds, combining both. This is caused by the fault current generated by the addition of RES production, which limits the current detected by the specified Over-Current (OC) relays. The results indicated that a few Over-current relays failed to activate during the island scenario, resulting in a lack of synchronization and reduced fault currents. In the island scenario, the system underwent further testing at various generation ranges (15%, 50%, and 80%), revealing a slight disparity in mean tripping time across different generation stages.
Formal Methods for Security Protocol Verification: Model Checking and Theorem Proving Approaches
Authors: Sachin Ashok Murab
Published: November 2024

Formal methods are a key part of making sure that cryptographic systems are safe and reliable. For the purpose of checking security protocols, this paper looks into two well-known formal methods: model checking and theorem proving. When you do model checking, you go through all of a system's possible states to see if it meets certain criteria. On the other hand, theorem proving uses formal evidence and mathematical reasoning to show that protocols are correct. In the beginning of the paper, an outline of security protocols and the need for formal proof methods to make sure they are strong against possible threats is given. Then it goes into the ideas and methods behind model checking and shows how it can be used in security protocol analysis. Model checking lets you do automatic checks, which lets you look into all the possible states of a system and find any security holes. The study then looks at theorem proving as an alternative way to check protocols. Theorem proving uses mathematical thinking to make formal proofs that show protocols are right. Although theorem proving usually needs help from a person with the right knowledge, it gives more security by making sure everything is right using strong mathematical foundations. It is talked about what the pros and cons of each method are, taking things like scale, automation, and expression into account. It also talks about important improvements and new tools in each method, showing how people are still working to make security protocol testing techniques more efficient and effective. The last part of the paper compares model checking and theorem proving, focusing on how they work together and how important it is to use both to make sure the security of cryptographic protocols. Also, it shows how important it is to choose the right formal methods based on the needs and features of the protocol in question.
Development and Mathematical Formulation of Secured Memory Efficient Cloning Detection Protocol in WSNs using RSA Algorithm
Authors: Pranav P Chippalkatti
Published: November 2024

This paper is related to the developing an improvised of secured memory efficient cloning detection protocol in WSNs using RSA Algorithm and is used for efficient transfer of data packets with security. NS2 is used as the simulation tool to simulate the outputs. The main aim or objective of the proposed work presented in this paper is to develop a secured memory efficient cloning detection protocol in the mobile adhoc wireless sensor networks. The simulation results shows the effectivity of the methodology that is being proposed by us.
Microaneurysms and Exudates Detection in Retinal Images using Deep Neural Network
Authors: P. N. Maldhure
Published: November 2024

One frequent diabetes consequence that affects the eyes is diabetic retinopathy(DR). The most frequent reasonfor blindness in working-age adults in the world is diabetic retinopathy. One in three diabetics has diabetic retinopathy to some extent. DR affects nearly all persons having type 1 diabetes and more than 60% of people having type 2 diabetes to some extent after 20 years of diabetes. In the US, approximately 4.2 million persons, 40 and older have diabetic retinopathy. In the United States, 12% of all new occurrences of blindness are caused by diabetic retinopathy.A 95% reduction in the risk of blindness is possible with a diabetic retinopathy early detection and treatment. The retina's appearance, the presence or absence of Microaneurysm and Exudates, and the degree of participation are all taken into account in the grading process. It has been demonstrated that deep neural networks (DNNs) work well for automatically grading diabetic retinopathy. The features like Microaneurysm and Exudates that are diagnostic of various stages of diabetic retinopathy are taught to these DNNs utilizing vast datasets of retinal pictures and accompanying grading information.It has been demonstrated that deep neural networks (DNNs) are efficient in automatically detecting diabetic retinopathy from retinal pictures. Sensitivity, specificity, precision, accuracy, and Kappa value are used to measure how well DNNs work in detecting diabetic retinopathy; these values are 95.74%, 92.31%, 96.77%, 94.74%, and 0.87, respectively.