bluuebunny/crossref_metadata_2025 huggingface dataset:
Mathematical Modeling of Adaptive Facial Gesture Recognition for Driver Fatigue Detection in Diverse Lighting Conditions Using Improved Kernel Based Support Vector Machine
Authors: Sanjay Sharma
Published: January 2025

The overwhelm number of fatalities due to road accidents in India which are caused by driver fatigue (about 25 percent of road accidents) is the dreadful problem that have to be addressed [1]. However, the mark of concern is up to 60 percent of these accidents cause deaths or serious injuries. The primary causes of driver fatigue are insomnia and sleep deprivation. Therefore, the emphasis for safety [should] be on alertness to avoid serious fatalities. Now days, the integration of smart algorithms in vehicles has been witnessed in great scale supported by Wireless Sensor Networks (WSN) which are used to monitor and transmit both vehicle and driver states. The purpose of this study is tackling the problem of drowsiness detection that is based on image segmentation and machine learning. The real-time image segmentation, targeting on detecting facial components like mouth and eyes by using image processing, have been successfully carried out as well. Applying input data from sources such as webcam or IoT-enabled camera, video frames go to extraction and segmenting of features using clustering algorithms. Additionally, an area of research associated with machine learning has used Support Vector Machines (SVM) as a method for detecting emotions from facial expressions. Considerable testing in varying light conditions has proven the algorithm's robustness, yielding higher accuracy compared to common researches. The studies are a milestone with regard to the integration of the latest technologies to reduce road accidents due to driver fatigue and the resulting devastating deaths.
Integrating Neural Network and SVM for Early Lifestyle Disease Diagnosis
Authors: Apeksha V. Sakhare
Published: January 2025

The average life expectancy has increased by 15 to 20 years in recent decades due to major advancements in public health. Developments in public policy, economic growth, medical diagnoses, and treatment approaches have all contributed to these developments. However, the 21st century's fast urbanization and industrialization have changed food and lifestyle patterns, which has led to an increase in non-communicable diseases (NCDs). The fact that these diseases, which include obesity, type 2 diabetes, hypertension, and several types of cancer, currently cause 63% of deaths worldwide shows how pervasive they are.
Poor diet, inactivity, and sedentary work habits are behavioral variables that contribute to lifestyle-related illnesses (LRDs), a subgroup of noncommunicable diseases (NCDs) that include diabetes, stroke, and cardiovascular disease. Early detection and care are essential because these behaviors aggravate metabolic problems and other health issues. Despite being common, these illnesses can be difficult to diagnose because of their complexity and reliance on lifestyle and environmental factors. The requirement for a solid and dependable framework to accurately forecast and control LRDs is addressed in this study. It presents the NNSVM model, a hybrid method that combines feature selection, disease prediction, and practical suggestions for bettering one's lifestyle. The system uses lifestyle data and cutting-edge machine learning techniques to predict diseases like diabetes, depression, and hypertension in their early stages. This novel approach may strengthen efforts to prevent illness and assist people in changing their lifestyles in a way that will improve their health.
WasteScan: Efficient Waste Detection and Multi-label Classification Leveraging Object Detection Methods
Authors: Amruta Hingmire
Published: January 2025

This study presents an efficient waste detection and multilabel classification algorithm using single-shot object detection techniques. The objective is to accurately identify and classify various waste objects according to their material, even in scenarios in which objects are densely clustered. The proposed algorithm demonstrates promising outcomes, achieving both exceptional detection accuracy and efficient computational performance. To achieve efficient waste detection, we utilize the YOLOv5, YOLOv7 and YOLOv8 models, known for their ability to detect objects with high precision. These models utilise a one-shot detection technique to predict bounding boxes and class probabilities for many objects in a single run, enabling real-time image processing. This study explored the effectiveness of an incremental learning approach, showcasing notable performance gains for a newly added class. This characteristic is especially crucial for applications that require timely decision-making or monitoring waste in dynamic environments. Furthermore, the models' computational speed guarantees real-time performance, which qualifies them for waste management applications which involve real-time sorting functionalities.
Optimizing Crop Recommendations: A Novel IDCDMO-Enhanced ACGRU Approach for Advanced Agricultural Predictions
Authors: G.Rubia
Published: January 2025

Agricultural productivity is maximised to a greater extent by crop recommendation. The traditional methods of crop recommendation are often rely on limited data, simple models, heuristic rules and expert knowledge which are less dynamic and not fully address the agricultural challenges.  Deep Neural Networks (DNN) play an important role in crop recommendations by providing more accurate crops that are suited to the terrain. The research proposes a novel approach utilizing Attention based Convolutional Gated Recurrent Unit (ACGRU), neural network architecture for crop recommendation. Additionally it integrates Improved Distribution based Chaotic Dwarf Mongoose Optimization (IDCDMO) algorithm for feature selection. This study compares, for crop recommendation, to see the effectiveness of proposed approach. The performance of the IDCDMO¬¬-enhanced ACGRU is compared with ACGRU without feature selection and also with the conventional neural network models such as Feed Forward Neural Network (FNN) and Long Short Term Memory (LSTM). The IDCDMO-enhanced ACGRU significantly outperforming both the ACGRU without feature selection and conventional neural network models in terms of accuracy, precision, recall and f1 score. Therefore, the integration of IDCDMO with ACGRU efficiently enhances recommendation accuracy, improves soil health and adds greater agricultural productivity.
Logistics Service Quality Between Self Built Logistics Mode And Third-Party Logistics
Authors: Murugan K
Published: January 2025

Supply chain management, logistics needs intensive process refinement to meet the challenges ahead due to technology advancement and it became pivotal role in manufacturing organizations. Many of these companies have a logistic role of obtaining, manufacturing, and supplying their products across Universe. Manufacturers and merchants can acquire some relevant and rich-knit original logistical services from experienced third-party logistics services through outsourcing advanced transportation, smart warehousing, automated in-plant material handling, and other added CV services. This relieves the burden from manufacturers to focus on their key organizational operations, as this is critical to attaining efficiency. The importance of logistics operations has risen on the background of globalization and competitive influences, and third-party logistics services have become popular among industrial companies in the past decade. This article provides a review on third-party logistics service providers even though much of the previous work in this area has relied on real time survey-based empirical data. Therefore, the objective of this research is to identify the studies of third-party logistics undertaken in the last decade and to demonstrate the modern trends in this field. A content analysis was conducted on 50 papers which were selected to assess how far third-party logistics research has advanced. Furthermore, this research examines the sample population that was used in the analysis of the results, various sectors that have been explored, various types of research methods employed, various data analysis procedures, data source, and the studies that have been done.
Exploring Fuzzy Nabla Dynamics on Time Scales with the Characterization Theorem
Authors: Hari Kishore. R
Published: January 2025

This paper is committed to proving the local existence, and uniqueness theorem for fuzzy nabla-dynamic equations on time scales (FNDES-) under the Characterization theorem for fuzzy functions with the help of generalized -H (nabla Hukuhara) differentiability, which aims to convert FNDES- into a system of nabla-dynamic equations on time scales (NDES-). If we take as integers then the FNDES- transform to the system of difference equations, or if we decide as Reals then the FNDES- transform to the system of differential equations. Moreover, it is accompanied by appropriate examples that improve the suggested work.
Deep Learning-Based Hybridized Bi-LSTM Model for Author Identification in the Marathi Language
Authors: Sunil D. Kale
Published: January 2025

Nowadays, author identification system for linguistics articles has imperatively needed for copy right violations. It is the activity in which linguistic attempts to identify the original author of an unknown textual information based on the utilized vocabulary and the writing style of the author. There have a lot of existing studies focusing on popular languages like English, Spanish, Chinese, and so on. In this paper, we propose an author identification system for Indian language Marathi based on hybridized Bi-directional long short-term memory (Bi-LSTM) model. The Marathi language totally differs from other popular languages because it uses the Devanagari script. It is one of the complicated scripts available in most Indian languages. The key idea of proposed author identification system is to extract high level feature representation from Marathi script using very deep convolutional neural network (VD-CNN) and hybridized Bi-LSTM model used for the identification of unknown authors. The VD-CNN based feature extraction is performed based on the input of two textual data analysis methods such as Term Frequency- Inverse Document Frequency (TF-IDF) and word embedding. The proposed model is tested with Author wise Marathi Language Text Corpus and simulation results show that the average accuracy for author identification has reached 96.01% when using TF-IDF and 99.16% with word embedding. Moreover, the proposed hybridized Bi-LSTM model provides better performance compared to stand-alone convolutional neural network (CNN) and Recurrent Neural Network (RNN) based models.
Predictive Modelling for Ischemic Brain Stroke Prediction Using Ensemble of Machine Learning Techniques
Authors: Avinash N Bhute
Published: January 2025

According to the World Health Organisation [WHO], more than 15 million people globally suffer a stroke each year, which leads to approx. 5 million fatalities and severe disabilities of 30% of survivors. The CDC rank stroke as the fifth most common cause of death in the world. It’s important to prevent strokes in the first place because they usually happen during the first phase. Technologies that can detect brain strokes before symptoms appear are desperately needed to diagnose the condition early and prevent catastrophic consequences. To guarantee the accuracy and applicability of the model in the medical field, this project will prioritize ethical considerations, the interpretability of results, and collaboration with medical experts. This paper explores the application of machine learning techniques with an ensemble approach, such as Neural Networks, KNN, Naive Bayes classifier, Random Forest, and Logistic regression to predict ischemic brain strokes based on various risk factors like age, hypertension, diabetes, smoking, and cholesterol levels. The proposed models aim to achieve high accuracy, sensitivity, and specificity in stroke prediction, offering healthcare providers a robust decision-making tool. A comparative analysis of these machine learning models is conducted to determine the most effective approach in predicting ischemic brain stroke, focusing on reducing false positives and improving the precision of early diagnosis. The results demonstrate the potential of machine learning in transforming stroke prediction, contributing to better patient outcomes and more efficient use of healthcare resources.
Loop - Free Matching Technique to Optimize Current-Flow within Trees and Unicyclic Networks
Authors: Saffren S
Published: January 2025

Introduction: Electricity is vital for both household and public needs, powering everything from lighting to machinery. Even brief interruptions in power can lead to significant economic and operational disruptions, underscoring the need for robust backup systems like generators and inverters. This paper presents an in-depth analysis of acyclic matching in specific classes of networks commonly found in electrical systems, which closely resemble the configurations of series and parallel circuits in power grids. By examining the induced subgraphs of acyclic matchings, this paper focuses on the precise computations of maximum acyclic matching edges in different types of trees and unicyclic networks. This is particularly relevant for optimizing electrical network resilience and reliability by minimizing power flow redundancy and ensuring efficient load balancing across electrical distribution systems.
Objectives: This paper aims to calculate the acyclic matching number in tree-like and unicyclic networks, emphasizing their importance in managing electrical power flow.
Methods: This paper derives mathematically proven formulas for calculating the acyclic matching number in specific graph types. These mathematical proofs rely on:

Inductive reasoning: Starting with simple base cases (e.g., small trees) and extending the results to more complex structures.
Combinatorial techniques: Counting the number of edges and vertices that can participate in a matching without violating the acyclic constraint.
Graph decomposition: Breaking down complex graphs into simpler components where acyclic matchings are easier to handle, then combining the results.

Each of the derived formulas is supported by rigorous mathematical proofs, which ensure that the acyclic matching numbers calculated for different graph classes are both correct and optimal.The theoretical framework ensures that the results are generalizable to a wide range of graphs encountered in practical applications, particularly in electrical networks.
Results: The acyclic matching problem is solved for caterpillar ladders, star graphs, banana graphs, fire-cracker graphs and corona graphs.
Conclusions: This paper presents proven mathematical formulas to calculate the acyclic matching number for different graph types, such as trees and unicyclic graphs. These findings play a crucial role in optimizing power flow within electrical networks by minimizing redundancy and improving load balancing. The results offer practical solutions for designing more reliable and efficient power systems. While future work may explore more complex network configurations, the current study provides key insights into improving the performance of electrical grids.
Human Emotions Determination using Multimodel Deep Learning Approach
Authors: Anil Kumar Muthevi
Published: January 2025

Emotional Determination and Measurement has always been an important factor in determining the state of any living being. Humans, being the chief emotional creatures, rely utterly on emotions and articulate on it in wellbeing as well. So far, this examination for an in-depth conclusive is confined to same human being. Human Sentiment Analytics, or Emotion AI, which is a component of Natural Language Processing (NLP).The aim of current work is to build over a machine which would result in effective emotion and intent delivery which stands subsequent to the human mind and extend the sentiment analytics to cross over the confined boundaries and get all the data which would help in deeper analysis to get concealed and obvious details as well and build a system which can bring about deeper insights in human emotions with much more efficiency and derive a logical conclusion considering all the factors which can be obtained from the input, which is planned to be Audio-VisualMedia
Comparison Between Different Types of Latin Hypercube Designs Applied to Kriging Models
Authors: Zainab Abdulateef Rasheed
Published: January 2025

Numerous scientific fields employ Kriging models (KMs) to study how physical systems behave. The computer code's response is interpreted by the KM as Gaussian process. A well-designed is an essential part of the KMs. Our study involves the design and analysis that is used in constructing KMs for computer experiments. Evaluating how design affects prediction accuracy is what we are interested in. Latin hypercube designs (LHDs)  are commonly used for building KMs. We investigate many types of LHDs and assess their performances. We built many KMs based on different types of LHDs and the performance of each model is compared. The comparison is performed through some measures that test the KM performance. We apply KMs to the Robot Arm function as a real computer code.
An Ensemble Approach to Phishing URL Detection Using Supervised Machine Learning
Authors: Eldho K J
Published: January 2025

Phishing is a cybercriminal activity which deceives users into providing their private information, such as credit card credentials and sensitive information like passwords which ultimately leads to financial loss. Phishing attacks are still a serious issue in the digital world because they try to trick people into divulging important information. This study aims to address this difficulty by employing supervised machine learning to detect phishing URLs.To improve accuracy, classification methods are specifically utilized. To train and evaluate the classifiers, the study methodology makes use of a dataset that includes benign and phishing URLs along with a variety of attributes. The study compares the effectiveness of conventional single classifiers with ensemble classifiers, such as Random Forest, Gradient Boosting, and CatBoost. The experimental results show that the ensemble classifier performed better than the individual classifiers and detected phishing URLs more effectively and with a higher accuracy. The suggested method emphasizes the value of using many classifiers to increase detection accuracy and robustness, showcasing the efficiency of ensemble learning techniques in improving the classification problem. This research aims to advance cyber security measures by proving the effectiveness of ensemble classifiers in phishing URL detection and provides a dependable and effective way to counteract evolving phishing threats.
Optimizing Non-Linear Fuzzy Functions Using LS and RMIL Methods
Authors: M.Kandakumar
Published: January 2025

The Liu–Storey (LS) nonlinear conjugate gradient method, although structurally similar to the well-known Polak–Ribiere–Polyak (PRP) and Hestenes–Stiefel (HS) methods, has not been extensively explored in the literature. This paper introduces a novel LS-type method inspired by the memoryless BFGS quasi-Newton approach, offering guaranteed global convergence for general functions when combined with the Grippo–Lucidi line search. Furthermore, we propose a modified LS method that ensures global convergence for nonconvex minimization problems when utilizing the strong Wolfe line search. Complementary numerical results validate these theoretical claims. In parallel, this paper also presents the RMIL (Robust Maximum Influence Linearity) method, a new approach for nonlinear analysis using advanced optimization techniques. The RMIL method addresses the challenges of modeling complex, nonlinear relationships by maximizing influence linearity within a robust optimization framework, significantly improving model accuracy and stability. This dual-method approach leverages both LS-type and RMIL methodologies, demonstrating enhanced predictive performance and robustness in nonlinear systems across various applications, including specific field. The integration of these advanced methods highlights their potential for solving complex optimization problems in nonlinear analysis.
Optimizing Switched Reluctance Motors For Electric Vehicles: A Multi-Criteria Decision-Making Approach Using DEMATEL
Authors: K. Indirajith
Published: January 2025

Introduction: Switched Reluctance Motors (SRMs) are gaining prominence in electric vehicles (EVs) due to their simplicity, reliability, and cost-effectiveness. However, challenges such as torque ripple, efficiency optimization, and design complexities limit their performance. Addressing these challenges requires a systematic approach to evaluate and optimize critical factors influencing SRM design and operation.
This study applies a multi-criteria decision-making (MCDM) framework using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to enhance SRM performance. By analyzing interdependencies among 13 critical criteria, the approach identifies key factors such as efficiency and torque ripple that significantly impact motor performance and cost-effectiveness. The findings provide actionable insights for optimizing SRM design, contributing to the advancement of EV technologies and supporting sustainable transportation goals.
 Objective: To enhance the performance of switched reluctance motors   (SRMs) in electric vehicles (EVs) using multi-criteria decision-making (MCDM) analysis with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.
 Methods: A 13 × 13 criteria matrix assessed the interrelationships between critical factors influencing SRM design and operation. The DEMATEL method established interdependencies among the criteria and developed a cause-and-effect relationship model.
 Results: Efficiency and torque ripple were identified as the most influential criteria, significantly impacting the reliability and cost-effectiveness of SRM technology. The analysis offered insights into optimizing SRM design by emphasizing key drivers. Conclusions: Integrating DEMATEL into the MCDM framework effectively addresses complex interrelated challenges in motor design and operation. This structured data-driven method advances decision-making for EV technologies and tackles critical challenges for sustainable transportation.
Implementing Sentiment Analysis with Applied Nonlinear Analysis: Predicting Mental Disorders from Diverse Online Social Network Datasets
Authors: Sushant.A.Patinge
Published: January 2025

Introduction: WHO projected that the depression disorder will increase over the next two decades; therefore, it should be diagnosed early and the sooner it can be detected and prevented. Several machine learning algorithms that may predict depression will be compared in this paper: CNN, LSTM, Bidirectional LSTM, Naive Bayes, Logistic Regression, Random Forest, AdaBoost, and Support Vector Machine. The results show that the models of deep learning, mainly variants of CNN and LSTM, exhibit high accuracy, and traditional classifiers perform exceptionally well. These results highlight how machine learning can contribute towards developing useful tools for the prevention of mental health disorders early in life. In modern life, online social platforms have very subtly integrated into the daily routine of a significant proportion of global citizens.
Analysing Security and QOS for IOT-Based Electronic Health Records using Public Cloud Platform
Authors: Hitesh Gehani
Published: January 2025

Paper-based record-keeping systems in small medical practices create significant obstacles in handling patient information, scheduling, and medication management. These outdated methods result in inefficiencies that negatively impact both patients and healthcare professionals, including missed appointments, prescription errors, and increased administrative work- load. This literature review examines a proposed digital solution aimed at optimizing clinic operations through electronic prescriptions, remote healthcare services, protected data handling, and increased patient involvement. By implementing cloud-based systems, medical practices can boost their operational productivity, enhance patient care, and minimize administrative responsibilities. The transition to digital systems offers numerous benefits, including improved data accuracy, stream- lined workflows, and enhanced communication between health- care providers and patients. By leveraging electronic health records (EHRs), small medical practices can significantly reduce the risk of medical errors and improve overall patient safety. Additionally, the implementation of telemedicine capabilities can expand access to healthcare services, particularly for patients in remote areas or those with limited mobility.
Neutrosophic Nano Topology: An Analysis of Neut.nano αg#ψ-Open and Closed Functions and Their Properties
Authors: T.Nandhini
Published: January 2025

This article provides an in-depth introduction to the fundamental concepts of neutrosophic nano αg^# ψ -open and closed functions within the context of neutrosophic nano topological spaces. These concepts play a significant role in understanding the intricate relationships and behaviors in this specialized field of topology. In addition to presenting these definitions, the article also delves into a broader discussion, exploring the various properties and implications associated with these functions. This extended examination aims to offer readers a comprehensive view of the subject, highlighting potential applications and theoretical advancements that arise from studying these unique functions within neutrosophic nano topological spaces
Investigating the Role of Nonlinearity in Plasma Wave Dynamics: Theoretical and Computational Perspectives
Authors: Nigama Prasan Sahoo
Published: January 2025

Plasma waves are fundamental for understanding many plasma phenomena, and at high enough amplitudes, the wave can also become nonlinear and complex. Research on nonlinear dynamics of plasma waves is critical for many applications, including fusion energy, space physics and plasma engineering. Theoretical aspects of nonlinear plasma wave propagation: wave packets, solitons, modulation instability, and wave-particle interaction. We generalize the standard theories of plasma waves to include non-linear terms and discuss how these non-linear equations can be solved numerically. We further discuss the implications of these nonlinear dynamics in several plasma applications.
Mathematical Modeling of Peer Activities Management in Blended Learning to Enhance Academic Performance
Authors: Sussi Widiastuti
Published: January 2025

Blended learning has emerged as a cornerstone of contemporary education, seamlessly merging digital and in-person instructional approaches. Despite its widespread adoption, one persistent challenge is the lack of sufficient social interaction, which can adversely impact learning outcomes. This study proposes a mathematical model to effectively manage peer activities—encompassing peer learning, peer assessment, peer review, and peer interaction—within blended learning frameworks to enhance academic performance. Focusing on Mechanical Engineering students, the research employs a posttest-only control group design to evaluate two experimental conditions: blended learning without structured management of peer activities and blended learning incorporating mathematically modeled peer activities. Data were gathered through questionnaires and performance assessments based on mechanical drawing projects. Results demonstrate that a mathematically structured approach to peer activity management significantly enhances students' academic performance, corroborated by positive feedback on improved social interaction and peer support. The findings underscore the utility of mathematical modeling as a robust tool for optimizing peer activities and advancing academic success in blended learning environments.
On Bi-complex Emad- Falih Integral Transform
Authors: Esraa Abdul Ridha Alghanim
Published: January 2025

In this Work, we define the Emad-Falih in Bi-complex space with convergence requirements. Additionally, we determine its fundamental features. We show an example of using the Bi-complex Emad- Falih integral transform to solve differential equations involving Bi- complex –valued functions
Parallel Computing Frameworks for Real-Time Decision-Making in Complex Computational Systems
Authors: Kaushalya Thopate
Published: January 2025

Introduction: Making decisions in real-time is quite challenging in complex computational systems, especially for resource-heavy applications. An increasingly higher demand for efficiency and accuracy requires completely novel ideas for optimal resource utilization and minimized execution time. Advanced parallel computing algorithms have emerged as very promising solutions for tackling computationally intensive tasks by exploiting their large-scale parallelism.
Objectives: In this regard, research will seek to come out with an all-encompassing framework on real-time decision-making by optimizing resource use through large-scale parallelization. It will further boost computational efficiencies through workload balancing, memory management, and data partitioning; and hence ensure scalability and precision in applications like weather forecasting, financial market prediction, and large-scale matrix operations.
Methods: This framework brings down application execution time by spreading computational tasks across several processors and cores; it employs optimization strategies such as fine-grain parallelism and overlapping computation with communication to minimize overheads. Speedup, throughput, execution time, scalability analysis, and parallel efficiency are among the evaluation metrics for performance assessment.
Results: The system produces very high improvements in efficiency, scale, and correctness in all instances. In fact, in weather forecasting, the Weather Research and Forecasting model processes fairly efficiently. In financial market predictions, machine learning algorithms can now be applied in real time, and speed and correctness will accelerate. Finally, the large-scale matrix operations will gain speed and correctness through parallel processing.
Conclusions: Integrating high-scale computing systems with good algorithms in large-scale applications, combined with mapping parallel execution onto resource availability, is an ideal combination. This will ensure the real-time processing of huge amounts of data, efficient utilization of resources at this scale, and the obtaining of good accuracy levels. Furthermore, it poses the future possibility of evolvable automatic management systems.
Detection of ARP Spoofing Attack by using ETTERCAP
Authors: Mohammad Daud
Published: January 2025

In our day-to-day life, we share or communicate over the internet in so many ways but to share or communicate we use some set of protocols so that we can send the information. ARP (Address Resolution Protocol) is one of them to communicate over the internet, but there are some chances of being spoofed by using the Address Resolution Protocol as attackers can steal your sensitive information through a Man-In-the-Middle attack. In this attack, a third person can be impersonated or spoofed the IP and we call it an IP spoofing attack. Therefore, to detect this attack we have used the Ettercap tool for detecting the ARP spoofing. In this detection method, we gave an approach in which Ettercap monitors the network and it is a modified Python-based script that is capable of sniffing the ARP packet transmission between the clients. Therefore, Ettercap is used for detecting ARP spoofing which is experimentally studied.
Compartive Reliability Analysis of a System with Linear and Non-linear Membership Functions using (3,2)-Fuzzy Sets
Authors: Lavanya. K
Published: January 2025

In this paper, we propose a multi-objective non-linear reliability global optimization and system cost as two objective functions. As a generalized version of fuzzy set, intuitionistic fuzzy set, Pythagorean fuzzy set, (3,2)-fuzzy set is a very useful tool to express uncertainty, impreciseness in more general way. we have considered (3,2)-fuzzy optimization technique with linear and non-linear membership function to solve this multi-objective reliability optimization model. To demonstrate the methodology and applicability of the proposed approach, numerical examples are presented and evaluated by comparing the result obtained by (3,2)-fuzzy approach with the intuitionistic fuzzy optimization technique at the end of the paper.
Introduction: Reliability engineering is one of the important tasks in designing and development of a technical system. The primary goal of the reliability engineer has been always to find the best way to increase system reliability. The diversity of system resources, resource constraints and options for reliability improvement lead to the construction and analysis of several optimization models. In daily life, due to some uncertainty in judgements of the decision maker (DM), there are some coefficients and parameters in the optimization model, which are always imprecise with vague in nature. In order to handle such type of nature in multi objective optimization model, fuzzy approach is used to evaluate this.
Conclusions: The main purpose of this paper is to give a computational procedure for solving multi-objective reliability optimization model by (3,2)-fuzzy optimization technique to find the optimal solution, which maximize the system reliability and minimize the cost of the system. We have obtained the result in the (3,2)-fuzzy optimization technique was compared with the intuitionistic fuzzy optimization and Pythagorean fuzzy optimization and it shows that the (3,2)-fuzzy optimizations with non-linear membership function gives better reliable system. Thus, the proposed method is an efficient and modified optimization techniques and gives a highly reliable system than the other existing method.
Applications of Fermatean Uncertainty weighted Average Accumulation operator to Multiple attribute group Decision Making
Authors: Lavanya. K
Published: January 2025

In this article, fermatean uncertainty weighted Averaging Aggregation operator has been studied along their several properties namely impotency, boundedness and monotonicity. Secondly we applied this proposed operator to deal with multiple attribute group decision making problem under fermatean uncertainty information. Finally, we constructed an algorithm for multiple attribute group decision making problems with suitable example.
Introduction: Xu demonstrated several operators such as intuitionist uncertainty weighted averaging (IFWA), intuitionist uncertainty ordered weighted averaging (IFOWA) and intuitionist uncertainty hybrid Averaging (IFHA) operators. Xu and Yager explored geometric addition operators such as intuitionistic uncertainty weighted geometric (IFWG) operator, intuitionist uncertainty hybrid geometric (IFWG) operators. They also applied them to multiple alternate group decision making (MAGDH) based on intuitionist fuzzy set (IFS). The advantage of the aggregation operators in this work, we familiarize the noted of (3, 2) Uncertainty weighted averaging aggregation of operator and also discuss some of their basic properties.
Conclusions: The main advantage of using the proposed method and operator is that this method provides more general, accurate and precise results. Therefore, the suggested methodology can be used for any type of selection problem involving any number of selection attributes. This method plays a vital role in real world situations. We ended the paper with an application of fermatean uncertainty decision making problem. In future, some author may develop this gives operators in various fuzzy Environment.
A Study in Tensor Analysis on Fractional Differential Implications
Authors: N. J. Essa
Published: January 2025

This research investigates the synthesis of fractional calculus with tensor analysis in order to establish a comprehensive mathematical structure that successfully overcomes the limitations of classical tensor methods on systems having non-local and memory-dependent behavior. Classical tensor calculus, which relies on integer ordered calculus, often fails when applied to physical and engineering systems where interactions are not localized within immediate space and time neighborhood. Though it generalizes classical calculus by allowing derivatives and integrals of any real or complex order, fractional calculus provides a suitable toolkit for capturing such interactions as it captures processes that depend on their history. The inclusion of these fractional derivatives into tensor calculus would lead to a paradigm shift in our understanding and modeling capability of complicated systems spanning
across multiple fields. By including fractional derivatives, existing theories based on classical derivatives can be extended with tensor fields for more accurate descriptions of some phenomena. These include various forms of physics and engineering such as materials science, biomechanics or geophysical modeling which have systems characterized by anomalous diffusion, viscoelastic material properties, irregular geometrical domains etc.
The study’s primary objectives are to provide a strict definition for the operations involving fractional tensors together with investigating their consequences in different physical situations. It is hypothesized that applying fractional tensors will reveal new aspects of physical phenomena leading to better account for dynamics interaction between them. Among other things this means looking at changes in geometric characteristics (like curvature and torsion) due to fractional derivatives in differential geometry; how do solutions of field equations change in theoretical physics? By constructing computational techniques and theoretical frameworks, this work aims at equipping scientists and engineers with strong models that enable them model complex interacting systems more precisely thereby creating new frontiers in scientific disciplines. Such an improved mathematical framework could also make significant advancements towards understanding fundamentals underlying the universe or developing technologies based upon newly gained insights.
Cluster Analysis for Assisted Healthcare Approval in Pervasive Social Networks
Authors: Vishnu Kumar Kaliappan
Published: January 2025

The idea of pervasive social network (PSN)-based healthcare is prompted by contemporary wireless sensing and mobile computer technology. The main challenge is how a PSN node may safely transmit health information to other nodes in the network to implement the notion. In this research, we suggest a safe PSN-based health service. With its assistance, we were able to develop the subject category infrastructure to recognise the top research fields, the research electricity network to identify the most productive countries and institutions, the journal co-citation map to identify the distribution of core journals, the author co-citation map to identify significant investigators and their co-citation trends, and the manuscript co-citation network to expose the cutting-edge literature and identify co-citation groupings on pervasive technologies.
Security and Power Consumption for Data Availability in Personalized Internet of Things with Cloud-based User Healthcare Applications
Authors: Sakthivel Velusamy
Published: January 2025

Personalized Healthcare is a brand-new patient-centred healthcare strategy that seeks to enhance the current healthcare system. E-health services are provided via personalized healthcare systems to meet the medical and support needs of the aging population. A significant development in the Big Data age is the Internet of Things (IOT), which, through enhanced services, provides a number of good engineering scenarios. Healthcare systems are now using analytics over IOT data streams as a resource of user data to find additional information, forecast early diagnosis, and make decisions in life-threatening situations. The IOT design is examined in this book, with a focus on systems that are internet. Because factors like accuracy and power consumption are important IOT concerns, research initiatives that are involved in boosting the performance of IOT-based healthcare systems are examined. The IOT-based healthcare system with cloud technology is thoroughly studied in this paper's data management procedures. The effectiveness of the IOT-based healthcare system is described, along with its benefits and drawbacks. The majority of research studies are effective at identifying various symptoms and can correctly forecast diseases. The IOT-based healthcare system created specifically for seniors is an effective way to keep track of their medical conditions. The main drawbacks of the current systems are their high power consumption, lack of resources, and security problems brought on by the use of several devices.
Phishing URL Detection with Gradient Boosting Classifier
Authors: Narayana Rao Appini
Published: January 2025

Phishing attacks continue to pose a serious and persistent risk to internet security, jeopardizing people's and businesses' financial stability and privacy. The precise and timely identification of these malicious websites remains a crucial challenge in the field of cyber security. This paper provides a new strategy employing Gradient Boosting Classifiers (GBCs) to overcome this issue. Phishing websites are purposely built to mimic legitimate sites to fool users into providing sensitive information. As a result, many malicious sites exhibit tiny traits that identify them from real ones. Traditional rule-based systems typically struggle to adequately identify these complex variances. In contrast, Gradient Boosting provides an ensemble learning framework that utilises the combined strength of weak classifiers, resulting in a robust model capable of recognising these elusive characteristics. Our experimental findings show that our suggested strategy performs better than others in a number of parameters, including accuracy, precision, recall, and F1-score. Crucially, our model demonstrates exceptional resistance to aggressive tactics that are frequently employed to hide the actual purpose of phishing websites. This study represents a substantial step forward in the fight to improve internet security and shield consumers from the constant danger of phishing scams.
A Review on Machine Learning and Deep Learning Techniques for Medical Internet of Things (m-IoT)
Authors: Lavanya K
Published: January 2025

The mobile health care over Internet of Things (IoT) offers flexibility and fast clinical diagnosis irrespective of distance and viewing displays. The delivery, management, and oversight of medical services have undergone a radical transformation as a result of the integration of Machine Learning (ML) and Deep Learning (DL) techniques in the mobile healthcare domain. To achieve better Quality of Service (QoS), the present networks needs more lime light of research in terms of streaming the medical videos without sacrificing the medical quality of experience (mQoE).Researchers have addressed various issues and proposed numerous solutions in terms of machine learning for IoT in mobile healthcare. The article provides a thorough analysis of contemporary machine learning (ML)/Deep Learning (DL) concepts, with a focus on mobile healthcare domain. It mainly focus on recent developments in health care domain and also opens research issues that need to be addressed in the future A wide range of topics, including the diagnosis and classification of diseases, customised therapy suggestions, remote patient surveillance and their health behaviour analysis have been covered. A comprehensive review has been conducted on IoT in healthcare, Big medical data in IoT, ML and DL models and ML challenges in IoT healthcare. This survey of IoT based medical data transmission is focused on recognising an accurate prediction model. The simulation results and comparison analysis of various ML/DL algorithms have been presented. The medical image classification have been evaluated based on several metrics including prediction accuracy, specificity and sensitivity. These metrics have been compared and tabulated for various learning algorithms like Artificial Neural Networks (ANN),Naïve Bayes Classifier (NB), Reinforcement Learning (RF),Support Vector machine (SVM),Convolutional Neural Networks (CNN). Finally, a frame work for a secured multipath medical video transmission using the hybrid version of the above discussed algorithms has also been suggested.
Even Vertex Odd Edge Root Square Mean Labelling Of Path Related Graphs
Authors: K. N. Babu
Published: January 2025

Consider G be a graph with p vertices and q edges. A graph G is said to be even vertex odd edge root square mean labelling if there exist an injective map f :V(G)→{0,1,2,3,…,2q} such that the induced edge labels are odd and distinct which can be obtained by f^(* ) (e)=⌈√((〖f(u)〗^2 +〖f(v)〗^2)/2)⌉  or  ⌊√((〖f(u)〗^2 +〖f(v)〗^2)/2)⌋. Any graph which admits even vertex odd edge root square mean labelling then it is called as even vertex odd edge root square mean labelling graph.
Pythagorean Fuzzy Ideals in Semiring
Authors: J. Jayaraj
Published: January 2025

In this paper, we introduce the notion of Pythagorean fuzzy ideals in semiring some interesting properties, results are discussed in this paper.
Optimizing Phasor Measurement Units Placement in Power Networks Using Betweenness Centrality
Authors: Saravanan M
Published: January 2025

Introduction: Centrality concepts play a crucial role in determining the significance of a vertex based on its necessity. A network always prefers the shortest path, also known as the geodesic path, for information transmission between nodes. Betweenness centrality, which reduces geodesic distance, makes it easier to strategically position phasor measurement units (PMUs) in a power network. We present a research paper that describes an algorithm specifically designed for PMU placement in power networks.  The algorithm's performance is assessed by running it on the widely used IEEE 14 and IEEE 30 buses, allowing for comparison with results obtained using other methodologies.
Controllability for Volterra Integro-Dynamic Sylvester Matrix Systems with Impulse on Time Scales
Authors: A Sreenivasulu
Published: January 2025

This article presents the complete controllability for a Volterra integro-dynamic Sylvester matrix system with time scale impulses in a finite-dimensional space Rn. We utilized the Banach fixed point theorem and nonlinear functional analysis to determine the existence of a unique solution for the system, and conducted an analysis of complete controllability using the Gramian matrix and various parameter changes. We provided a numerical example using simulation to demonstrate the application of these conclusions for two different time scales, T=R and T=P_1,1.
Energy-Efficient Data Aggregation in Wireless Sensor Networks using Neural Network-Based Prediction Models
Authors: Aruna kumari
Published: January 2025

In this study, we examine how neural networkbased prediction models can be used in Wireless Sensor Networks (WSNs) to build energy-efficient data aggregation strategies. With their widespread use in a variety of applications like smart cities, health care, and environmental monitoring, WSNs are known for their energy-related problems. This study's major goal is to prolong sensor network lifespans while preserving data communication accuracy and dependability. We provide a unique framework that combines data aggregation techniques with predictive modelling, making use of neural networks to predict sensor data and minimise redundant transmissions. Our approach minimises energy consumption related to data transmission, resulting in more sustainable operation of WSNs by accurately forecasting data trends and patterns. Simulations are used to assess the suggested methodology, and the results show significant gains in terms of energy savings, network throughput, and overall system performance. This work adds to the continuing attempts to create WSN architectures that are more intelligent and effective and that can function well in contexts with limited resources.