Journal of Global Research in Electronics and Communications(JGREC)
type: channel
description: About JGREC The Journal of Global Research in Electronics and Communications (JGREC) is an international, peer-reviewed journal committed to publishing high-quality research in electronics, communication systems, and computing technologies. Scope & Focus Areas JGREC welcomes cutting-edge research and review articles in (but not limited to) the following areas: Networking & Communication Systems 5G, 6G, IoT, MIMO SDN & NFV Optical & Satellite Networks IoT & Edge Computing Networks Ad Hoc, Sensor & Mesh Networks Cognitive Radio & Spectrum Management Cybersecurity & Cryptographic Systems Network Security & Intrusion Detection Blockchain & Secure Communication Cryptographic Algorithms & Data Privacy Quantum Cryptography Signal Processing & AI in Communications Digital Signal & Image Processing AI & ML for Communication Networks Speech & Audio Processing Wireless Sensor Networks & Data Analytics High-Performance & Cloud Computing Cloud & Edge Computing Quantum Computing in Networking Distributed & Parallel Computing Data Center Networks Embedded & IoT Systems Real-Time Embedded Systems IoT Protocols & Architectures Automation & Smart Systems Publication Model JGREC follows an open-access model to ensure global knowledge-sharing while maintaining rigorous peer-review standards. The journal publishes monthly, accepting original research papers, review articles, technical reports, and industry case studies.
publisher: Saanvi Publications
language: en
publication name: Journal of Global Research in Electronics and Communications(JGREC)
issn: 2321-3175
items: AI-Powered Accessibility Features for Web Applications
items: AI For Cybersecurity: Real-Time Anomaly Detection in Network Traffic Using AI
items: A Survey of Code-Based vs Numerical Approaches for Piping Stress Evaluation
items: AI-Driven Fraud Detection in Insurance Claims: A Deep Learning Framework for FinTech Risk Intelligence
items: Comprehensive Study of Machine Learning Approach for Fraudulent Identification in Real-Time Financial Banking Systems
items: The Role of Artificial Intelligence in Automating SaaS Operations: A Systematic Review of Current Techniques
items: Forecasting Cyber Attacks in Banking and FinTech Platforms: A Review of Data-Driven Approaches
items: High-Accuracy based on Deep Learning Techniques for Pattern Recognition of Wafer Defect
items: The Role of Social Media in Public Health Intelligence: Trends, Techniques, and Challenges
items: A Data-Driven Advanced Deep Learning Model For False News Classification and Identification
items: Deep-Learning-Based Crowd Density Estimation: A Comprehensive Survey
items: A Review Of Generative Ai Applications In Financial Advisory Services: Current Developments, Limitations, And Opportunities
items: Synthetic Defect Data Generation Using Deep Learning Architecture for Improved Wafer Inspection Performance
items: Char Dham Care App a Technological intervention for Holy Pilgrimage with reference to Uttarakhand Char Dham Yatra
items: Enhanced Sentiment Analysis on Online Amazon Reviews Using RoBERTa with PSO-Based Hyperparameter Tuning
items: Automation in Smart Factories: Trends in Robotics, IoT, and Cyber-Physical Systems in Manufacturing
items: The Computational Marketer: A Comprehensive Review of Machine Learning Applications in Digital Strategy
items: Cybersecurity Challenges and Automation Frameworks in Modern Digital Transformation Initiatives
items: Cybersecurity in Financial Institutions: A Survey of Threat Detection, Risk Assessment, and Prevention Strategies
items: Survey on Data-Driven Decision Support Frameworks in Industrial IoT (IIoT)-Based Manufacturing
items: Predictive Analytics For Sales Using Historical Transaction Data And Seasonal Trends
items: A Robust Machine Learning Method for Intelligent Ransomware Identify in Industrial Control Networks
items: A Review of AI-Assisted Stress Prediction Models in Mechanical Design
items: Machine Learning Approaches for Threat Detection in Android Mobile Networks
items: Trends, Challenges, and Future Directions in Machine Learning-Based Housing Price Prediction
items: A Machine Learning Based to the Enhancement of Electrical Fault Detection and Classification
items: AI-Powered Tools in Software Engineering Applications in Code Generation and Quality Assurance
items: A Comprehensive Survey on Blockchain-Based Digital Payment Systems and Their Impact on Financial Inclusion
items: A Systematic Survey of Machine Learning Methodsfor Fraud Identification in Credit Cards
items: Latest Advances in PCMs (Phase Change Materials) for Thermal Regulation Applications: A Survey

AI-Powered Accessibility Features for Web Applications
type: item
description: Accessibility in web applications is a critical aspect of modern web development, ensuring inclusivity for users with disabilities. However, traditional solutions often fall short in addressing real-time, dynamic accessibility challenges. This paper explores the integration of Artificial Intelligence (AI) to enhance accessibility features in web applications. Specifically, it focuses on real-time captioning, screen reader optimization, and voice-controlled navigation. By leveraging advanced AI technologies such as Natural Language Processing (NLP), computer vision, and speech recognition, this research proposes and evaluates innovative solutions that significantly improve user experience, usability, and compliance with accessibility standards like the Web Content Accessibility Guidelines (WCAG). The results demonstrate that AI-powered accessibility tools can transform web applications into inclusive digital spaces, fostering equitable access for all users. Moreover, the research examines how AI can address the limitations of traditional assistive technologies, such as limited accuracy in speech-to-text or static screen reader navigation. By incorporating adaptive, context-aware systems, AI can offer a more personalized and responsive experience for users with various impairments. The research also discusses potential challenges, such as ethical considerations and system performance, in deploying AI-driven accessibility features at scale. The findings highlight the future possibilities of AI in promoting a more inclusive web, ultimately bridging the accessibility gap for diverse user groups.
subject: Research Paper
  topic: section
subject: Web Content Accessibility Guidelines
  topic: keywords
subject: Natural language processing
  topic: keywords
subject: Computer vision
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Web Applications
  topic: keywords
creator: Arun Rathore
rights: Copyright (c) 2026 Arun Rathore (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-07
publication date: 2026-03-07
starting page: 53
ending page: 56
doi: 10.5281/

AI For Cybersecurity: Real-Time Anomaly Detection in Network Traffic Using AI
type: item
description: The large-scale, high-speed characteristics of network traffic in the modern digital world and the constantly changing threat environment requires real-time detection of anomalies in cybersecurity infrastructure. The paper is a synthesis and review of recent studies (2015-2024) at the crossroads of artificial intelligence (AI) and network traffic anomaly detection, with a specific focus on real-time or near real-time detection. The review features classical statistical techniques, ML-based techniques, DL, and hybrid/edge paradigm that allow rapid detection. Then identify key limitations in current frameworks (such as latency, false-alarm rates, model drift, adversarial robustness, and feature- engineering overhead) and propose a conceptual architecture that extends prior work by integrating lightweight unsupervised online learning, edge computing, and adversarial regularization for real-time responsiveness and scalability. The analysis wraps up with unresolved issues and research directions in the future of AI-based anomaly detection of network traffic.
subject: Review Article
  topic: section
subject: anomaly detection
  topic: keywords
subject: network traffic
  topic: keywords
subject: real-time monitoring
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: edge computing
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: Suraj Panthi
rights: Copyright (c) 2026 Suraj Panthi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-26
publication date: 2026-02-26
starting page: 49
ending page: 52
doi: 10.5281/

A Survey of Code-Based vs Numerical Approaches for Piping Stress Evaluation
type: item
description: Piping stress assessment is a major priority of making sure that pipeline systems perform safely, reliably and long term under different loads of mechanical, thermal and environmental conditions. The review gives a detailed theoretical and numerical piping stress analysis framework, which includes categorization of stress, failure modes, design criteria, and code-based and numerical methods of evaluation. Primary, secondary, peak, and occasional stresses are used to describe the theoretical background. Important stress failure approaches are also included, such as: plastic deformation, buckling, fatigue, and collapse. An overview of established piping design codes and the basis of their analysis is made to draw attention to their intended purpose of providing pressure integrity and structural integrity. Complex processes, including nonlinear material behaviour, thermal effects, and residual stress following welding, can be better modelled with the use of advanced numerical methods, particularly finite element modelling, than with simplified code-based approaches. In order to replicate the overlay weld procedures, a detailed finite element model is employed. To predict the distribution of temperatures and the development of stress, this model utilizes a specifically developed heat source, a double-ellipsoidal heat source. The paper highlights the relevance of combining theoretical knowledge, design principles, and computer analysis to obtain correct and trustworthy piping stress analysis in contemporary engineering studies.
subject: Review Article
  topic: section
subject: Piping stress evaluation
  topic: keywords
subject: Stress Classification
  topic: keywords
subject: Piping Design Codes
  topic: keywords
subject: Welding Residual Stress
  topic: keywords
subject: Numerical Simulation
  topic: keywords
creator: Neha Upadhyay
rights: Copyright (c) 2026 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-20
publication date: 2026-02-20
starting page: 42
ending page: 48
doi: 10.5281/

AI-Driven Fraud Detection in Insurance Claims: A Deep Learning Framework for FinTech Risk Intelligence
type: item
description: The growth rate of fraudulent insurance claims is a significant challenge to financial institutions, and it is causing huge losses to the economy and inefficiency in operations. The purpose of this research is to find a solution to this growing problem by creating an automated system that can reliably forecast the number of fraudulent property insurance claims. To combat insurance data misclassifications and improve fraud detection, an Artificial Neural Network (ANN) machine learning model is trained in this study. The model has been trained and evaluated using a publicly available Kaggle dataset, which has 38 customer profiles, claim history, and policy details as features. The use of extensive preprocessing operations such as cleaning of data, label coding, working with missing values and balancing with the help of SMOTE, and normalization with the help of Standard Scaler provide quality of data and resilience. The proposed ANN model is tested with the help of common performance measures, including accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), confusion matrix, ROC curve, and AUC. As the experimental outcome shows, with an accuracy of 96.67, the experiment is more accurate than the current baseline models like XGBoost, Decision Trees and Bi LSTM networks. The results show that ANN is effective in identifying unusual patterns of claims and this can be applied in enhancing the fraud prevention strategies and decision-making in the finance and insurance industry.
subject: Research Paper
  topic: section
subject: Insurance Fraud Detection
  topic: keywords
subject: Finance
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning (DL)
  topic: keywords
subject: Risk Intelligence
  topic: keywords
creator: Dr. Hemant N. Patel
rights: Copyright (c) 2025 Dr. Hemant N. Patel (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-06
publication date: 2026-02-06
starting page: 76
ending page: 82
doi: 10.5281/

Comprehensive Study of Machine Learning Approach for Fraudulent Identification in Real-Time Financial Banking Systems
type: item
description: Financial fraud, which is commonly defined as applying fraudulent techniques to secure funds, has, in recent years, become a significant issue for businesses and organizations. Efforts to weed out such scams by existing means like human inspection and checks are tedious, expensive and subject to inaccuracies. In the near future, advances in artificial intelligence may enable more sophisticated machine learning algorithms to search through massive amounts of financial data for signs of fraud. This study suggests a thorough method for identifying financial fraud using the highly unequal class-marked IEEE CIS Fraud Detection dataset. A Light Gradient Boosting Machine (LightGBM) and a Convolutional Neural Network (CNN) are the two models that are subsequently trained and assessed on a balanced dataset. The results indicate that CNN model is reliable and significantly higher than LightGBM model, CNN model is 99.73% and its accuracy is higher at 99.91%. Moreover, the CNN model has low false negatives and zero false positives in the confusion matrix, which shows its usefulness in the classification of fraud transactions. The results confirm the effectiveness of CNNs to perform this operation, which gives a solid and significantly effective solution to the problem of real-time financial fraud detection.
subject: Research Paper
  topic: section
subject: Financial Identification
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Real-Time Banking Systems
  topic: keywords
subject: Deep Learning (DL)
  topic: keywords
subject: IEEE-CIS Dataset
  topic: keywords
creator: Sandeep Gupta
rights: Copyright (c) 2026 Sandeep Gupta (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-06
publication date: 2026-02-06
starting page: 79
ending page: 85
doi: 10.5281/

The Role of Artificial Intelligence in Automating SaaS Operations: A Systematic Review of Current Techniques
type: item
description: Software as a Service (SaaS) is a fast-paced technology that has revolutionized how applications are accessed, deployed and scaled in an organization and Artificial Intelligence (AI) has been among the technological tools necessary to enable automation in the field. This met-analysis also focuses on the ways in which machine learning, natural language processing and intelligent process automation AI techniques could be integrated into the work of SaaS in order to enhance their performance, security and scaling. The presented paper recapitulates the modern development in regard with automating software-as-a-service (SaaS) operations with referring to frameworks, tools, and means of ensuring that compliance, monitoring, and delivery are made simpler. The report identifies key automation solutions, like process management, AI-driven monitoring, and policy-based governance using peer-reviewed literature. The incorporation of explainable AI to help in decision-making, predictive analytics to act proactively to solve problems, and security automation to maintain compliance are all emerging issues that the research explores. The paper concludes with a set of suggestions about what research should be done next, particularly focusing on intelligent automation models that are flexible, sustainable, and building blocks that can evolve alongside organizations. In summary, have determined that automation is essential to the SaaS long-term sustainability of SaaS operations, but not only a technical enhancement.
subject: Review Article
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Software as a Service (SaaS) Automation
  topic: keywords
subject: SaaS Automation
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Security, Reliability
  topic: keywords
creator: Anoop Sunarthy
rights: Copyright (c) 2026 Anoop Sunarthy (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-06
publication date: 2026-02-06
starting page: 36
ending page: 41
doi: 10.5281/

Forecasting Cyber Attacks in Banking and FinTech Platforms: A Review of Data-Driven Approaches
type: item
description: It is essential to predict cyber-attacks in financial systems and FinTech platforms to protect financial infrastructures with an ever-advancing threat level. The review will discuss evidence-based solutions that improve the predictive abilities of the Security Information and Event Management (SIEM). Through systematic study of machine learning, deep learning, and statistical models on historical security event data, it identifies key methods for predicting attack vectors such as phishing, DDoS, fraud, and malware infiltration. The exploratory paper identifies feature engineering, anomaly detection, and real-time analytics as components that enhance the accuracy of forecasting. Issues such as imbalanced data, concept drift and latency in large-scale environments are addressed, and new methods such as ensemble learning and adaptive models are mentioned. They also check how the feeds on threat intelligence and the behavioral analytics are integrated into the SIEM systems to prevent risks. The evaluation wraps up with the future research directions, which are scalable, explainable and resilient prediction mechanisms needed to protect banking and FinTech ecosystems.
subject: Review Article
  topic: section
subject: Forecasting Cyber Attacks
  topic: keywords
subject: Banking Security
  topic: keywords
subject: FinTech Platforms
  topic: keywords
subject: Data-Driven Approaches
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: SIEM
  topic: keywords
creator: Mr. Himanshu Barhaiya
rights: Copyright (c) 2026 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-06
publication date: 2026-02-06
starting page: 28
ending page: 35
doi: 10.5281/

High-Accuracy based on Deep Learning Techniques for Pattern Recognition of Wafer Defect
type: item
description: In semiconductor production, automated wafer defect pattern detection is a crucial procedure that improves product quality, yield, and efficiency. The paper introduces a powerful deep learning-powered system of classifying wafer defects based on a 1D-Convolutional Neural Network (1D-CNN). The WM-811K wafer map dataset, the biggest publicly available dataset based on actual wafer manufacturing processes in industry, is used to assess the suggested approach. Data analysis and visualization are first conducted comprehensively regarding the distribution of wafer index, frequencies of defects and patterns of spatial failures. A large preprocessing pipeline is used to guarantee the reliability and consistency of data, such as data cleaning, filtering out non-defective and inaccurate samples of failure types, remapping pixel values to grayscale, converting the grayscale to RGB, image resizing and data augmentation. These procedures are highly successful in raising dataset quality and model generalization. The 1D-CNN model is trained to efficiently acquire discriminative defect features after the filtered data is divided into training and testing data. The following measures are used to evaluate the model's performance: accuracy, precision, recall, F1-score, loss, and AUC-ROC. Results of the experiment indicate that the suggested 1D-CNN has 99.0% accuracy and 99.0% AUC-ROC, which is superior to several other cutting-edge methods. The results indicate the practical applicability, soundness, and efficacy of the suggested framework in automated inspection of defects in the wafer of semiconductor manufacturing.
subject: Research Paper
  topic: section
subject: Wafer Defect Pattern Recognition
  topic: keywords
subject: Semiconductor Manufacturing
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Wafer Map Analysis
  topic: keywords
subject: Automated Inspection
  topic: keywords
subject: Defect Classification
  topic: keywords
creator: Dr. Dinesh Yadav
rights: Copyright (c) 2026 Dr. Dinesh Yadav (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-06
publication date: 2026-02-06
starting page: 22
ending page: 27
doi: 10.5281/

The Role of Social Media in Public Health Intelligence: Trends, Techniques, and Challenges
type: item
description: The social media development has transformed the landscape of surveillance and communication of health issues among the masses. This review explains how social media can be used in the area of public health intelligence, and it deals with disease outbreaks, risk communication, and even the assessment of public sentiment. It talks about emerging themes in social media surveillance in the area of public health, including pandemic surveillance, vaccination campaigns, and behavioral health interventions. Through the use of sophisticated analysis tools, researchers and decision-makers able to extract valuable information on large volumes of user-generated content to allow interventions and evidence-based decision-making to take place in a timely manner. Under the circumstances of the COVID-19 pandemic, social media came in quite handy in the manifestation of social opinion on preventive actions, as well as the dissemination of health-related information rapidly. However, there are challenges to its institutionalization in the health systems of the population. This discussion indicates the necessity to develop scaled, ethically controlled and AI-based models as well as to give a summary of the current trends, strategies and challenges. Online democracy on social media can enable equitable use of social media, enhance accountability, and resiliency of global health systems.
subject: Review Article
  topic: section
subject: social media
  topic: keywords
subject: public health intelligence
  topic: keywords
subject: Health surveillance
  topic: keywords
subject: Disease outbreak detection
  topic: keywords
subject: Sentiment analysis
  topic: keywords
creator: Bal Krishna Sharma
rights: Copyright (c) 2026 Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-05
publication date: 2026-02-05
starting page: 15
ending page: 21
doi: 10.5281/zenodo.18440739

A Data-Driven Advanced Deep Learning Model For False News Classification and Identification
type: item
description: The popularity of fake news has become a significant issue in contemporary society due to the rapid growth of internet social media and the ease of sharing information. The unintended consequences of spreading false information include social unrest, political polarization, and a loss of trust in media sources. The paper explores the performance of a deep learning method for automating the separation of fake and real news articles. The hybrid RNN +GRU model has been proposed and the classification performance of the model is highly reliable with an accuracy, precision, recall and F1-score equal to 99.8 on the test data. Further comparative analysis reveals that the proposed model performs greatly except the models that are currently being used, including CNN, Random Forest, XGBoost, ALBERT, and LSTM model. The findings confirm that the RNN + GRU architecture is an efficient and reliable method for false news detection. The paper provides a useful hybrid RNN+GRU model of the reliable fake news detection and it has high levels of robustness and stability as well as a high level of consistency in comparison to the current systems. In general, the results indicate that hybrid sequential models are indeed appropriate for detecting false news in real-world conditions.
subject: Research Paper
  topic: section
subject: Social Media Analytics
  topic: keywords
subject: Fake News Detection
  topic: keywords
subject: Misinformation Analysis
  topic: keywords
subject: Text Classification
  topic: keywords
subject: Semantic Feature Extraction
  topic: keywords
subject: Deep Learning Models
  topic: keywords
creator: Parth Gautam
rights: Copyright (c) 2026 Parth Gautam (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-04
publication date: 2026-02-04
starting page: 8
ending page: 14
doi: 10.5281/zenodo.18439525

Deep-Learning-Based Crowd Density Estimation: A Comprehensive Survey
type: item
description: The importance of crowd density estimate (CDE) in event management, urban planning, and public safety has made it a crucial area of study in computer vision. Conventional computer vision techniques are susceptible to challenging situations in the crowd including occlusions, scaling, and light conditions. Since the implementation of deep learning, convolutional neural networks (CNNs) and, more recently, transformer-based and hybrid designs, have demonstrated state-of-the-art performance in the estimate of crowd density and person count. The paper will provide an extensive review of the deep-learning-based methods of crowd density estimation published between 2015 and 2025 in terms of architectural development, the variability of the datasets, and the advancement of the methodology. The trends, benchmarks, and challenges are consolidated in the survey, which will serve as a roadmap in the future research of deep-learning-based crowd analysis.
subject: Research Paper
  topic: section
subject: crowd density estimation
  topic: keywords
subject: deep learning
  topic: keywords
subject: CNN
  topic: keywords
subject: YOLO
  topic: keywords
subject: computer vision
  topic: keywords
subject: image processing
  topic: keywords
subject: surveillance
  topic: keywords
creator: Shrihari Pathak
rights: Copyright (c) 2026 Shrihari Pathak (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-22
publication date: 2026-01-22
starting page: 76
ending page: 78
doi: 10.5281/

A Review Of Generative Ai Applications In Financial Advisory Services: Current Developments, Limitations, And Opportunities
type: item
description: Artificial Intelligence (AI) has become a digital transformation platform in any industry now, and Generative Artificial Intelligence (GAI) is among the most substantial developments. Compared to older AI systems, which are focused on classification and prediction, GAI has the unique ability to generate original and valuable data and content, including text, images, and financial simulations. GI is transforming the way business activities are conducted in the financial sector by enhancing automation, accuracy, and customization. This synergistic effect of the two enables financial institutions to conduct complex tasks, such as portfolio optimization, risk prediction, fraud detection, and real-time market analysis, more efficiently and with greater accuracy. Financial advisory services have a particular advantage in capitalizing on GAI's capacity to offer client-specific investment recommendations, an automated decision-support system, and flexible insights tailored to client profiles and market conditions. Ethical governance, model transparency, computational costs, and data privacy pose challenges to the adoption of GAI. To eliminate these limitations, explainable AI (XAI), regulatory compliance, and human-AI collaboration should be enhanced. The paper investigates the advances, uses, constraints, and prospects of GAI in the world of finance, particularly in financial advisory services. The findings suggest that GAI will continue to transform financial processes and create a new paradigm in which human skills and AI intelligence will work together to drive innovation and sustainable value generation.
subject: Research Paper
  topic: section
subject: Generative Artificial Intelligence (GAI)
  topic: keywords
subject: Financial Advisory
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Financial Technology
  topic: keywords
subject: VAEs
  topic: keywords
creator: Sharat Priya
rights: Copyright (c) 2026 Sharat Priya (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 67
ending page: 75
doi: 10.5281/zenodo.18439788

Synthetic Defect Data Generation Using Deep Learning Architecture for Improved Wafer Inspection Performance
type: item
description: Wafer defects have become smaller and more complex, increasing the demand for accurate and real-time quality monitoring and control. Wafer surface flaws can be properly inspected to detect defects in the production process faster. Hence, it is essential to have defect checking in the fabrication of the wafer to foster high productivity, cost effectiveness, and ideal performance. This paper provides an efficient wafer defect inspection model based on a Graph Neural Network (GNN) on the Mixed-type Wafer Defect Dataset of Kaggle that comprises some 38,000 wafer maps in 38 different, normal, single-defect, and mixed-defect classes. The wafer maps in the form of 52 ×52 grids were first processed into matrix normalization, handled labels with label encoder, reshaping, and graph-based representation to maintain the spatial relationships between dies. Stratified sampling was used to divide the dataset into training, validation and testing sets, and data augmentation was used, which included rotation, flipping and cropping to improve robustness and generalization. The suggested GNN has used message-passing and global pooling, which captures complex spatial and relationship defects that are difficult to understand by normal CNN and machine learning methods. The evaluation of the performance was conducted based on accuracy, precision, recall, F1-score, and cross-entropy loss. The experimental results show that the proposed model offers a high classification accuracy of 97.25, a high level of precision (96.70%), recall (96.17%), and F1-score (96.44%). Comparative analysis reveals that the GNN is superior to the MobileNetV1, ResNet50 and SVM models. In general, the findings indicate the strength, consistency, and appropriateness of GNNs in complex multi-defect wafer inspection of semiconductor manufacturing.
subject: Research Paper
  topic: section
subject: Wafer defect inspection
  topic: keywords
subject: Semiconductor manufacturing
  topic: keywords
subject: Mixed-type defects
  topic: keywords
subject: Wafer map analysis
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Quality control
  topic: keywords
subject: Defect classification
  topic: keywords
creator: Roopesh Kumar
rights: Copyright (c) 2026 Roopesh Kumar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-03
publication date: 2026-02-03
starting page: 1
ending page: 7
doi: 10.5281/zenodo.18439502

Char Dham Care App a Technological intervention for Holy Pilgrimage with reference to Uttarakhand Char Dham Yatra
type: item
description: Mobile technologies are revolutionizing pilgrimage management and visitor experiences, especially in high risk, high volume, and high scrutiny religious circuits such as Char Dham Yatra in Uttarakhand, India. This chapter offers an applied case study of mobile app design and use to enhance safety, logistics, and experience quality for millions of pilgrims. It provides a review of digital pilgrimage innovations and an integrated mobile application design and implementation model Char Dham Care for Uttarakhand, based on geography, culture, and environment. Using a mixed methods approach, the case study describes existing government initiatives (Tourist Care app; Aadhaar-based eKYC registration), field issues (connectivity challenges, privacy, inclusion and access barriers, hazardous conditions), user needs (accessibility, multilingual user interface, offline capabilities), and the research process and findings. A ‘one stop shop’ solution integrates hazard alerts, registration, offline mapping, and SOS functionality as a single privacy-first ecosystem. The case study proposes practical recommendations for inclusive design, ethical governance, and sustainable functioning of mobile innovations in sensitive mountain ecosystems. In short, the contributions of this case study for both the academic and practical debates taking place around the digitalization of sacred spaces is valuable, and of direct relevance to similar initiatives in other places.
subject: Research Paper
  topic: section
subject: Char Dham Yatra
  topic: keywords
subject: Pilgrimage
  topic: keywords
subject: Mobile Application
  topic: keywords
subject: Smart Tourism
  topic: keywords
subject: Hazard Management
  topic: keywords
subject: eKYC
  topic: keywords
subject: Digital Governance
  topic: keywords
subject: Sustainable Tourism
  topic: keywords
creator: Parth Gautam
creator: Aditi Saxena
rights: Copyright (c) 2026 Parth Gautam, Aditi Saxena (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-19
publication date: 2026-01-19
starting page: 62
ending page: 66
doi: 10.5281/zenodo.18439669

Enhanced Sentiment Analysis on Online Amazon Reviews Using RoBERTa with PSO-Based Hyperparameter Tuning
type: item
description: Sentiment analysis stands vital for businesses that want to deliver superior customer satisfaction by optimizing their strategies. Rising dependence on digital platforms along with e-commerce leads consumers to use online reviews for their purchase choices. This work focuses on the customer product reviews and incorporates classical ML approaches, as well as DL models using the transformer coupling with optimization methods. The study creates a better sentiment analysis algorithm that uses RoBERTa to read product reviews on Amazon in Kaggle which has 34,000 records with rating information and user comments. The strategy to carry out this project necessitates an ample data preparation cycle as well as pre-lemmatization prior to the TF-IDF features operations. The dataset is organized into a training and testing partition approach and RoBERTa performs classification since it is a powerful self-attention mechanism and is pretrained using masked language modeling. Hyperparameter tuning that is implemented with Particle Swarm Optimization (PSO) is an advantage of the model optimization. Both confusion matrix and epoch-based performance plots display 85.31% accuracy and 92.26% precision and 85.31% recall and F-1 values. The results show that whereas RF, LR, Naive Bayes, and DT all perform poorly, RoBERTa beats the other fundamental models in terms of sentiment classification accuracy and F1 Score value. The evaluation finds that RoBERTa excels at generalizing and predicting sentiment which positions it as an effective model for sentiment analysis applications in practical use.
subject: Research Paper
  topic: section
subject: NLP
  topic: keywords
subject: Sentiment analysis
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: Particle Swarm Optimization (PSO
  topic: keywords
subject: Amazon Product Reviews
  topic: keywords
subject: deep learning
  topic: keywords
creator: Saket Kotpalliwar
creator: Priya Vij
rights: Copyright (c) 2026 Saket Kotpalliwar, Dr. Priya Vij (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-17
publication date: 2026-01-17
starting page: 54
ending page: 61
doi: 10.5281/zenodo.18439563

Automation in Smart Factories: Trends in Robotics, IoT, and Cyber-Physical Systems in Manufacturing
type: item
description: The use of Industry 4.0 technologies is reshaping conventional production, and automation in smart factories is a major contributor to this transformation. Automation enables decision-making and production systems that are up to the point thanks to the integration of Robotics, the Internet of Things (IoT), and Cyber-Physical Systems (CPS). The paper under review is dedicated to identifying the present-day trends and breakthroughs in industrial automation that relate, for instance, to introducing autonomous mobile robots, robotic process automation (RPA), digital twins, and edge-cloud computing, among others. Besides, it tackles given the fact that IoT and IIoT are the techs that make seamless connectivity and communication between the digital and physical systems possible. Moreover, the paper mentions the big issues, data privacy, interoperability, and protocol standardization, which have been around for some time. The current study not only gathers but also discusses the recent research and technological advancements, thus, providing a glimpse into the future of smart manufacturing and the technification of interlinked, intelligent industrial ecosystems.
subject: Research Paper
  topic: section
subject: Automation
  topic: keywords
subject: Smart Factories
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Robotics, IoT
  topic: keywords
subject: Cyber-Physical Systems (CPS)
  topic: keywords
subject: IIoT
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Intelligent Manufacturing
  topic: keywords
creator: Bal Krishna Sharma
rights: Copyright (c) 2025 Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-22
publication date: 2025-12-22
starting page: 70
ending page: 75
doi: 10.5281/zenodo.18347744

The Computational Marketer: A Comprehensive Review of Machine Learning Applications in Digital Strategy
type: item
description: The integration of the so-called Machine Learning (ML) and Artificial Intelligence (AI) has fundamentally restructured the industry of digital marketing, turning it into a science of prescriptive and real-time optimization instead of the science of descriptive analytics. 2022-2025 studies have highlighted the maturation of AI applications in the marketing spectrum between the developed predictive customer intelligence, the algorithmic creative optimization, and the advanced programmatic media buying. Empirically, organizations have reported selling improvement in ML sales, Return on Investment (ROI) of 10-20%, with high levels of operational returns of up to 25% churn of customers. However, ethical governance is necessary in the pursuit of performance. It is the complex algorithms that result in high returns that need to be put on the focus of Explainable AI (XAI) to contain the imminent biases and ensure privacy of the data. The review compiles the available academic sources, defining the specific algorithms (i.e., XGBoost, Reinforcement Learning, Convolutional Neural Networks) that allow gaining competitive advantages and points out the most significant finding that the effectiveness of technology is intrinsically linked with the perceived transparency and trust and makes the ethical compliance not only a regulatory consideration but a competitive condition of high consumer acceptance and financial sustainability.
subject: Research Paper
  topic: section
subject: Machine Learning (ML)
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Convolutional Neural Networks (CNNs)
  topic: keywords
subject: Creative Optimization
  topic: keywords
subject: Customer Intelligence
  topic: keywords
subject: Real-time Optimization
  topic: keywords
subject: Bias Mitigation
  topic: keywords
subject: Descriptive Analytics
  topic: keywords
creator: Mahadeo Prasad
creator: Jayadeep Patil
rights: Copyright (c) 2026 Mahadeo Prasad , Jayadeep Patil (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-15
publication date: 2026-01-15
starting page: 40
ending page: 46
doi: 10.5281/zenodo.18337735

Cybersecurity Challenges and Automation Frameworks in Modern Digital Transformation Initiatives
type: item
description: The new digital transformation trends are now centered on automation frameworks and cybersecurity concerns as organizations are adopting cloud computing, DevSecOps pipelines, IoT systems, and software-defined infrastructures. Even though the technologies enhance agility, scalability, and operational efficiency, it also adds additional agile attack surfaces and dynamic and challenging security threats. The level, pace and scope of modern cyber threats including zero-day and pseudo-zero-day attacks have made the rule-based and manual protection systems inadequate. This paper will present a summary of automated cybersecurity systems that focus on the application of AI and ML to be more active, adaptive, and effective in cyber defense. Some of the areas that have been discussed are security automation and orchestration, AI threat intelligence, predictive analytics, incident response automation, self-healing systems, and architecture, such as Zero Trust architecture and defense-in-depth. Issues in the paper concern also the privacy of the data, compliance with regulations, and adversarial attacks, as well as gaps in the competencies of the working staff, which requires intelligent, scalable, and reliable security measures.
subject: Research Paper
  topic: section
subject: Machine Learning
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Cybersecurity Automation
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: SOAR
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
creator: Himanshu Barhaiya
rights: Copyright (c) 2026 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-16
publication date: 2026-01-16
starting page: 47
ending page: 53
doi: 10.5281/zenodo.18335009

Cybersecurity in Financial Institutions: A Survey of Threat Detection, Risk Assessment, and Prevention Strategies
type: item
description: The digital revolution that has occurred fast in the financial sector, especially the banking sector has broadened the cybersecurity threat significantly. Since financial institutions are becoming increasingly dependent on digital technologies, online banking, APIs, cloud services, and fintech solutions, these institutions become more vulnerable to cyber threats, such as data breaches, ransomware, identity theft, and fraud. This article gives a detailed overview of cybersecurity background, threat detection systems, and risk evaluation strategies specific to the financial services. It identifies customer, transaction systems, digital identities, and interbank networks as major digital assets at risk. The focus is made on the traditional and AI-based intrusion detection systems such as the supervised, unsupervised, and hybrid learning models to identify threats effectively. In addition, the paper examines how Zero Trust Architecture and third-party governance can be implemented to enhance the resilience of an organization. The paper establishes gaps in the prevailing standards of cloud security, legal responsibility as well as proactive measures to counter, through a systematic literature review. It emphasizes the need to embrace dynamic cybersecurity models and strategic risk management to guarantee the integrity of data, compliance with regulations, and confidence in financial systems. This article is useful to the researchers, policymakers, and practitioners who aim at protecting financial institutions in a more digitalized economy.
subject: Review Article
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Financial Institutions
  topic: keywords
subject: Threat Detection
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Digital Banking Security
  topic: keywords
creator: Manish Jain
rights: Copyright (c) 2025 Dr. Manish Jain (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 62
ending page: 69
doi: 10.5281/zenodo.18311279

Survey on Data-Driven Decision Support Frameworks in Industrial IoT (IIoT)-Based Manufacturing
type: item
description: Industrial Internet of Things (IIoT) has become a revolution in the smart manufacturing paradigm as it provides pervasive connectivity, real-time data collection, and intelligent automation of industrial systems. In IIoT-enabled manufacturing, large volumes of heterogeneous data are constantly generated by sensors, machines, and enterprise systems, which must be used effectively to implement the data-driven decision support frameworks. This decision-making process relies on machine learning, artificial intelligence, edge-to-cloud computing, and optimization of processes, quality control, and real-time operational decisions. Using a manufacturing example, examine the following decision support system paradigms: data-driven, model-driven, knowledge-driven, document-driven, and communication-driven. Moreover, the key issues like data heterogeneity, complexity of integration, scalability, security, privacy, and interpretability of the model are also discussed, which can be used to consider creating robust, scalable, and intelligent IIoT-based data-driven decision support systems to be used in next-generation smart manufacturing systems
subject: Review Article
  topic: section
subject: Industrial Internet of Things (IIoT)
  topic: keywords
subject: Data-Driven Decision Support Systems
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: IoT, smart manufacturing, Industry 4.0, predictive maintenance, cloud computing, edge computing, cybersecurity, interoperability, AI integration.
  topic: keywords
creator: Manish Jain
rights: Copyright (c) 2026 Dr. Manish Jain (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-12
publication date: 2026-01-12
starting page: 33
ending page: 39
doi: 10.5281/zenodo.18267065

Predictive Analytics For Sales Using Historical Transaction Data And Seasonal Trends
type: item
description: The unpredictability of promotional and economic factors makes it imperative to have strong predictive models that would enable sound retail sales forecasting. The accurate sales forecasting in retailing is essential in managing inventory, minimizing operation cost, and maximizing customer satisfaction. To enhance the accuracy of Walmart sales forecasting, the present research utilize a wide range of machine learning (ML) approaches supported by a large amount of data acquired in Kaggle. The methodology includes extensive data pre-processing, such as outlier removal, time-based feature engineering with the assistance of Exploratory Data Analysis (EDA). This is the reason an XGBoost regression model is developed because it has a high possibility to capture nonlinear relationships and can be used with large-scale data. High predictive accuracy is reflected in the results of the experiment with the best model performance as the R 2 of 0.99, Mean Absolute Error (MAE) of 1226.47 and the root mean squared error (RMSE) of 1700.98. A comparative study also proves that XGBoost is better than the classical models such as the Gradient Boosting, Decision Trees and the Random Forests. The results prove the efficiency of the suggested method and indicate its usefulness in practice when applied to retail forecasting.
subject: Research Paper
  topic: section
subject: Sales Forecasting
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Retail Sales Data
  topic: keywords
subject: Seasonal Trends
  topic: keywords
subject: Demand Forecasting
  topic: keywords
subject: Machine Learning,
  topic: keywords
subject: Walmart data
  topic: keywords
creator: Ram Pratap Singh
rights: Copyright (c) 2026 Mr. Ram Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-05
publication date: 2026-01-05
starting page: 8
ending page: 13
doi: 10.5281/zenodo.18265989

A Robust Machine Learning Method for Intelligent Ransomware Identify in Industrial Control Networks
type: item
description: Malicious software known as Malware in the form of viruses, ransomware, and spyware has turned into a global epidemic, and research shows that the impact is intensifying. Numerous ways have been introduced to date to deal with these hazards. To handle this increasing problem, this paper proposes an effective Deep Neural Network (DNN) model that can be used to detect ransomware precisely. The model proves to be very effective in the separation of malicious and benign samples, with an accuracy, precision, recall, and F1-score of 99.76, and an AUC value of 0.98, which indicates the close to perfection of the classification. The findings reveal the high learning stability and generalization without overfitting that is reinforced by the stable training and validation. Compared to the current methods, including KNN (83.9%), VGG-16 (90.5%), XGBoost (94.1%), and Logistic Regression (96%), the DNN-based model was better in its performance. On the whole, this paper highlights how deep learning can be used to reinforce cybersecurity protection and offer a scalable and intelligent method to counter the ransomware attacks in the present digital environment.
subject: Research Paper
  topic: section
subject: Ransomware Detection
  topic: keywords
subject: Industrial Control Networks (ICNs)
  topic: keywords
subject: Machine Learning (ML
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Intelligent Systems
  topic: keywords
subject: SCADA Security
  topic: keywords
creator: Deepak Mehta
rights: Copyright (c) 2026 Mr. Deepak Mehta (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-10
publication date: 2026-01-10
starting page: 27
ending page: 32
doi: 10.5281/zenodo.18265957

A Review of AI-Assisted Stress Prediction Models in Mechanical Design
type: item
description: Stress analysis constitutes an essential part of mechanical design, by which the safety, reliability, and performance of the components engineered to operate under complex loadings and environmental conditions can be ascertained. Conventional methods for stress prediction, such as analytical approaches, finite element analysis (FEA), and experimental testing, have been implemented extensively in applications like piping systems, pressure vessels, and rotating machinery. these methods are frequently associated with significant computational costs, prolonged solution times, and extensive modeling efforts, thereby hindering their effectiveness in rapid design iterations and real, time applications. In a landmark departure from traditional methods, the advent of artificial intelligence (AI) and machine learning (ML) has spawned data, driven alternatives that can efficiently predict stress responses by learning the nonlinear relationships between geometrical features, materials, and loading conditions. AI, driven stress prediction models, which encompass machine learning and deep learning methods, are capable of quick and precise stress estimations, thus facilitating early, stage design decisions and diminishing the need for a multitude of simulation runs a comprehensive review of AI, assisted stress prediction models in mechanical design, their interaction with traditional stress analysis methods, the main advantages, and the limitations intrinsic to them regarding computational efficiency, predictive accuracy, and obstacles stemming from data quality, generalization, and physical interpretability, thereby sketching the next steps towards intelligent stress analysis frameworks in engineering practice.
subject: Review Article
  topic: section
subject: stress piping analysis
  topic: keywords
subject: Stress prediction
  topic: keywords
subject: Mechanical Design Optimization
  topic: keywords
subject: Machine Learning in Stress Analysis
  topic: keywords
subject: Finite Element
  topic: keywords
subject: Deep Learning Techniques
  topic: keywords
creator: Prachi Rajput
rights: Copyright (c) 2026 Prachi Rajput (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-03
publication date: 2026-01-03
starting page: 1
ending page: 7
doi: 10.5281/zenodo.18266110

Machine Learning Approaches for Threat Detection in Android Mobile Networks
type: item
description: The development of mobile technologies and the high number of applications based on Android have considerably increased the number of targets of attack by cybercriminals. Android, which is the most popular mobile platform, is becoming the target of more and more malware and adware that aim to steal the privacy, financial data, and integrity of the system of its users. The paper suggests a complex Android malware detection system based on an Artificial Neural Network (ANN) that can be used to improve the classification of malicious and innocuous applications. The study is based on a sample of 4,464 Android instances and includes necessary preprocessing steps, including handling missing values and outliers, min-max normalization, and feature selection using PCA. Random under-sampling was used to overcome the imbalance in classes prior to training of ANN model. The offered ANN has better results, with the accuracy of 99.38%, precision of 99.24%, recall of 99.42%, and F1-score of 99.61%. Accuracy loss curves, confusion matrix, and ROC analysis evaluation have verified the high generalization of the model, learning stability, and good discriminative properties. Comparative findings also indicate that the ANN performs more successfully than traditional models which include, LSTM (93.9%), SVM (94%) and CNN (97.8%). All in all, the research offers a viable deep learning-driven source of strong Android threat detection, which enhances the safety and well-being of the mobile context
subject: Research Paper
  topic: section
subject: Android
  topic: keywords
subject: Network,
  topic: keywords
subject: Android Security
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Cyber-Attacks
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Vulnerabilities.
  topic: keywords
subject: Cyber Security
  topic: keywords
subject: Threats
  topic: keywords
subject: Malware
  topic: keywords
creator: Sachin Manekar
rights: Copyright (c) 2026 Mr. Sachin Manekar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-12
publication date: 2026-01-12
starting page: 14
ending page: 19
doi: 10.5281/zenodo.18265828

Trends, Challenges, and Future Directions in Machine Learning-Based Housing Price Prediction
type: item
description: Housing price prediction is a field that is of growing importance in research, given the economic implications of the field as well as the intrinsic complexity of the real estate markets. Conventional statistical and econometric approaches, e.g., linear regression and time series analysis, usually find it hard to reflect the nonlinear, multifactorial relationship that affects property values. With the rise of machine learning (ML) methods, predictive modeling has changed as now flexible methods that are data-oriented and can work with large and diverse datasets are possible. Decision trees and random forests, gradient boosting (XGBoost), and neural networks have proven to be more accurate predictors when compared to traditional models. The latest trends focus on ensemble and hybrid modeling, the incorporation of alternative sources of data, such as satellite imagery and socioeconomic factors, and the implementation of explainable AI (XAI) to promote transparency. Regardless of these developments, various challenges do exist, such as data quality, interpretation of the model, and dynamism of the housing markets. This paper presents an extensive literature analysis of the field of ML-based housing price prediction, including the attributes of properties that cause the greatest impact on their price, the methodological strategies that are pertinent, the current trends, and the principal limitations that limit the predictive accuracy, to provide an idea of the future studies and possible applications.
subject: Review Article
  topic: section
subject: Housing Price Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Ensemble Models
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Real Estate Analytics
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Deepak Mehta
rights: Copyright (c) 2025 Mr. Deepak Mehta (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-18
publication date: 2025-12-18
starting page: 49
ending page: 54
doi: 10.5281/zenodo.18265771

A Machine Learning Based to the Enhancement of Electrical Fault Detection and Classification
type: item
description: The electricity transport system relies on transmission lines, which remain vulnerable to faults that can cut off operations and cost the system massive monetary losses. Transmission lines are very crucial over long distances in delivering electricity, but they also have reliability problems. Images of defects that might disrupt the power supply and put people in risk accompany reliability. Therefore, this study uses machine learning on the Electrical Fault Detection and Classification dataset from Kaggle, which comprises voltage and current observations from an 11 kV transmission system. Two models, K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM), were constructed following data preparation, which included label encoding and Minmax normalization. The models' performance was then evaluated using metrics such as confusion matrices, accuracy (Acc), precision (Prec), recall (Rec), and F1-score. KNN offered the highest Acc of 99.72, high Prec of 99.99, high Rec of 99.55, and a high F1-score of 99.77, and hence it beats LSTM and the earlier Random Forest and Decision Tree tricks. In addition, the LSTM model demonstrated a high performance also since the training and validation loss were both stable and convergent, which indicates that learning was effective. The above findings indicate that machine learning, including multi-feature fusion, is significant to enhance the accuracy of electrical fault detection; thus, a very precise and reliable solution can be offered to minimize the loss of time and improve the safety of power transmission networks.
subject: Research Paper
  topic: section
subject: Electrical Fault Detection
  topic: keywords
subject: Fault Classification
  topic: keywords
subject: Power System Reliability
  topic: keywords
subject: Transmission Lines
  topic: keywords
subject: Power Plant
  topic: keywords
creator: Parth Gautam
rights: Copyright (c) 2025 Dr. Parth Gautam (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 43
ending page: 48
doi: 10.5281/zenodo.18255304

AI-Powered Tools in Software Engineering Applications in Code Generation and Quality Assurance
type: item
description: Modern software engineering is being red financed by tools based on Artificial Intelligence (AI) and specifically in the realms of the code generation, quality assurance, and continuous integration and deployment (CI/CD). Machine learning, deep learning, natural language processing, and large language model and practice have made intelligent systems shift of traditional rule-based automation to collaborative development support. GitHub Copilot and transformer-based models introduced by Open AI are examples of tools which have shown significant advances in developer productivity, code quality and automated error detection through presented code suggestions and intelligent refactoring options. In quality assurance, AI-based methods enable automated test generation, defect prediction, and anomaly detection, which help to make the process of manual testing much easier and enhance the reliability of the software. In a similar fashion, AI-based CI/CD pipelines make use of predictive analytics and real-time performance inspection to optimize build phases, identify deployment anomalies, and improve system stability. Nonetheless, the implementation of AI in software engineering also brings about issues of data quality, interpretability of model, trust and ethics issues that should be addressed to ensure responsible deployment the increase in the significance of human AI cooperation and the attainment of scalable, dependable, and ethically appropriate inclusion of AI into modern software engineering practice.
subject: Review Article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Software Engineering
  topic: keywords
subject: Code Generation
  topic: keywords
subject: Quality Assurance
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Pradeep Laxkar
rights: Copyright (c) 2025 Dr. Pradeep Laxkar (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-19
publication date: 2025-12-19
starting page: 55
ending page: 61
doi: 10.5281/zenodo.18265714

A Comprehensive Survey on Blockchain-Based Digital Payment Systems and Their Impact on Financial Inclusion
type: item
description: The increasing speed at which digital payment systems are evolving coupled with the rate at which the blockchain technology is being integrated is restructuring financial systems in the world and redefining financial inclusion. Traditional payment systems might be problematic including excessive cost of transaction, lack of transparency, security risk, and access by underbanked communities. The opportunities to efficiently manage this issue can be offered by the decentralized and transparent architecture of the blockchain technology which increases the efficiency of transactions and removes the middlemen as well as adds the trust to a peer-to-peer platform. This paper has presented a detailed analysis of blockchain-based payment system in digital format in order to identify how they impact financial inclusion. It described the basics of blockchain, the various methods of making a digital payment and how it is possible to compare traditional systems and blockchain system systems. The results reveal the use of blockchain in promoting security, transparency, and financial service provision particularly in the developing nations. It was also noted in the review how blockchain has the potential to empower the unbanked and underserved, in particular, when it comes to the development of inclusive and efficient financial ecosystems. Finally, the study identifies the enduring issues, such as scalability, regulation, and interoperability, and provides some recommendations to attain sustainable and equal digital financial inclusion by innovating blockchain.
subject: Review Article
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: Financial Inclusion
  topic: keywords
subject: Decentralized Finance (DeFi)
  topic: keywords
subject: Financial Ecosystem
  topic: keywords
subject: Digital Payment Systems
  topic: keywords
creator: Sandeep Gupta
rights: Copyright (c) 2025 Sandeep Gupta (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 36
ending page: 42
doi: 10.5281/zenodo.18255601

A Systematic Survey of Machine Learning Methodsfor Fraud Identification in Credit Cards
type: item
description: Credit card fraud is still a huge issue for the financial industry, and over the years, it has caused the industry to lose billions of dollars across the globe. The once-efficient traditional rule-based systems are now almost entirely obsolete. The primary focus of this survey is the shift from conventional rule-based systems to astute, data-driven solutions for credit card fraud detection. It even goes so far as to thoroughly examine ML-based credit card fraud detection (CCFD). Along with the difficulties of class imbalance, idea drift, and verification delay, the study offers a comprehensive analysis of the concept and different kinds of credit card transactions. It also discloses publicly available datasets, including Kaggle, IEEE-CIS, and PaySim. This research highlights the contributions of preprocessing methods, such as data cleaning, normalization, and PCA, to enhancing data quality and also discusses ethical dilemmas related to transparency, bias reduction, and informed consent. Besides that, the paper discusses various supervised learning methods (Logistic Regression, Decision Tree, Naïve Bayes) as well as different unsupervised learning approaches (K-means, DBSCAN, Autoencoders, One-Class SVM) that can be applied to identify fraudulent transactions. The survey reveals that ML models are responsible for improving the accuracy, flexibility, and speed of detection, which, in turn, can lead to the establishment of safer, more trustworthy financial systems.
subject: Review Article
  topic: section
subject: Credit Card Fraud Detection (CCFD)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Fraud Analytics
  topic: keywords
subject: Supervised Learning
  topic: keywords
subject: Financial Security
  topic: keywords
subject: Unsupervised Learning
  topic: keywords
creator: Prachi Rajput
rights: Copyright (c) 2025 Prachi Rajput (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 28
ending page: 35
doi: 10.5281/zenodo.18265654

Latest Advances in PCMs (Phase Change Materials) for Thermal Regulation Applications: A Survey
type: item
description: Phase change materials have a wide range of possible uses, including thermal energy storage and temperature adjustment. This research classifies PCMs into three distinct types: organic, inorganic, and eutectic. It also details their usage, side effects, and advantages. Researchers examine PCMs' latent heat, thermal conductivity, density, and melting point, among other thermophysical characteristics, with a focus on ways to enhance them, such as by including metal foams for enhanced heat transmission. Furthermore, macro, micro, and nano encapsulation methods are presented as the main techniques to make the PHM migration impossible, raise the PHM stability, and make the integration into the real-world system easier. The combination of numerical modeling advancements, phase transition monitoring, and experimental methods has demonstrated that PCMs could be widely applied in the insulation of buildings, thermal energy storage, and even the cooling of high-tech equipment. This review has indicated the need for future research directed toward the scalability, durability, and smart integration of PCMs to be able to exploit their full potential in being part of the sustainable energy solutions.
subject: Review Article
  topic: section
subject: Thermal Conductivity
  topic: keywords
subject: Phase Change Materials (PCMs)
  topic: keywords
subject: Thermal Energy Storage
  topic: keywords
subject: Renewable Energy Systems,
  topic: keywords
subject: Sustainable Energy Solutions
  topic: keywords
creator: Dr. Roopesh Kumar
rights: Copyright (c) 2026 Dr. Roopesh Kumar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-12
publication date: 2026-01-12
starting page: 20
ending page: 26
doi: 10.5281/zenodo.18266050