Journal of Global Research in Electronics and Communications(JGREC)
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.
Artificial Intelligence-Driven Sentiment Analysis for Product Reviews in Online Retailing Platforms
Machine learning and large language models (LLMs) are two examples of AI methods used for sentiment analysis that can efficiently categories texts as positive, negative, or neutral. Its capacity to automatically detect emotions in texts is utilized by numerous campaigns related to marketing, social media, product reviews, and hate speech. In order to categories the tone of product reviews taken from the Amazon Customer Review dataset, this study suggests a deep learning model. A combination of CNNs and LSTMs (Long Short-Term Memory) networks makes up the model. Tokenization, normalization, stop word deletion, and sequence padding are all part of the suggested systematic approach to data preprocessing in the given method. For feature extraction, it recommends the Bag-of-Words model. It may divide the data for training and testing 80/20 with this dataset, which contains 400,000 ratings across five product categories. The LSTM layer use long-term dependency modelling to enhance classification accuracy, whereas the CNN module seeks out local textual patterns. Testing shows that the CNN-LSTM model beats more traditional models like Random Forest, Naive Bayes, and independent CNN in terms of accuracy (98%), precision (98.52%), recall (98.49), and F1-score (98.49). The findings demonstrate that deep hybrid architecture is effective for sentiment analysis of large e-commerce platforms.
Copyright (c) 2026 Dr. Manish Jain (Author) https://creativecommons.org/licenses/by/4.0
Predictive Analytics for Employee Retention UsingWorkday HCM Data
The retention of employees is an important aspect of organizational stability and performance because high rates of employee attrition mean that the organization incurs financial losses, decreased productivity, and institutional knowledge loss. Predictive analytics is a proactive way of comprehending and predicting employee turnover in using demographic, behavioral, and work-related information. A practical model for predicting employee retention is the goal of this research, which applies machine learning techniques to the 1,470-item Employee Attrition and Performance dataset from IBM HR Analytics. The dataset contains 35 different qualities. Data preprocessing such as the processing of missing values, outlier processing, label encoding, normalization, and feature selection were done comprehensively to guarantee data quality. The problem of the imbalance of classes was addressed by creating balanced training data with the SMOTE technique. The postulated model employs the Extra Trees Classifier (ETC) that is an ensemble learning algorithm that constructs numerous randomized decision trees to improve the predictive capability and strength. The ETC model was doing exceptionally well with an accuracy (ACC) of 99.1%, precision (PRE) of 98.6%, recall (REC) of 99.8% and F1-score(F1) of 99.2%. These results affirm the applicability and relevance of the model in employee retention prediction as it provides the organization with practical data of how to optimize HR practices and support businesses to make informed judgments concerning workforce management.
Copyright (c) 2026 Dr. Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by/4.0
Database Administration Across SQL and NoSQL Systems: Trends, Techniques, and Challenges
In the age of data-driven computing, data management has become a significant concern in managing highly scaled, heterogeneous and fast-varying loads of data. The paper also provides a comprehensive analysis of the management of databases in both SQL and NoSQL databases based on their architecture, nature of operations, trends in their performance, security issues and future trends of technology. The analysis indicates that relational (SQL) databases are very consistent and structured, and accommodate ACID-compliant transactions, which are very well suited to enterprise and transactional systems. On the other hand, NoSQL systems are highly scalable, schema-agnostic and effective with unstructured and distributed data, which is advantageous for contemporary applications, including big data analytics, cloud computing and real-time systems. Other developments covered in the paper include cloud-native databases, Data-as-a-Service (DBaaS), serverless computing, AI-based automation and distributed database models. Comparative analysis shows that there is no single best database model, and suitability of a model can be determined by the requirements of a given application and the characteristics of the workload. The conclusion of the paper is that database systems in the next-generation computing environments will increasingly embrace hybrid architecture and smart automation to enhance their scalability, security and performance.
Copyright (c) 2026 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by/4.0
Leveraging Ensemble Learning Methods to Improve Credit Scoring Model Accuracy and Robustness
Credit scoring is the term used to describe a statistical analysis that can be applied in finance institutions and banks to determine the creditworthiness of an individual. The bestowers normally manipulate it to decide whether to expand or withdraw credit. The score is also a major factor in evaluating an individual's creditworthiness and determining whether he/she approved for a loan. The proposed study uses an ensemble learning model with the XGBoost package to optimize credit scoring using the Credit Scoring Kaggle dataset, which consists of 31,219 customer records from 2023. The data preprocessing pipeline included handling missing data, outliers, and noise; label encoding; z-score normalization; and data balancing using the Synthetic Minority Oversampling Technique (SMOTE). Stratified sampling (80:20) was also employed to maintain the proportions of the different classes. The suggested XGBoost model was assessed using standard performance measures, including accuracy, precision, recall, F1-score, and ROC-AUC. The experimental findings showed increased prediction performance, with an F1-score of 95.9%, recall of 99.5%, precision of 92.6%, and an AUC of 0.99, which denotes practically perfect classification. Compared with more conventional models such as Decision Trees, Random Forests, and Gradient Boosting Machines, the XGBoost model proved more robust, efficient, and capable of strong generalization. The results underscore that the model is interpretable in addition to improving credit risk prediction accuracy and dependability, as demonstrated by the analysis of feature importance, making it an effective decision-support tool in current financial risk management.
Copyright (c) 2026 Mr. Kapil Ahir (Author) https://creativecommons.org/licenses/by/4.0
A Review on Automated Tomato Leaf Disease Detection Using Deep Learning in Smart Farming Systems
Tomato cultivation is an important agricultural practice across the world, but it confronts many obstacles because of many diseases that negatively affect crop quality and production. This article discusses deep learning-based automated tomato leaf disease sensors for smart farming systems. It discusses publicly accessible datasets such as PlantVillage and other large repositories, as well as preprocessing and data augmentation techniques to improve model performance. Different DL networks, such as Convolutional Neural Network (CNNs), MobileNet, VGG, ResNet, Inception and YOLO, are analyzed based on their soundness in classification and detection. Besides, the review has indicated the combination of artificial intelligence and internet of things, drones and cloud-based decision support systems to provide real-time monitoring of crops. Critical issues like imbalance in the data, complexity of computation, inadequate field data and unexplainability are also discussed. Lastly, the research directions in the future, including lightweight models, edge computing and explainable AI, are discussed in order to move towards more accurate solutions of precision agriculture and sustainable smart farming.
Copyright (c) 2026 Drashi Jain (Author) https://creativecommons.org/licenses/by/4.0
Stock Price Time Series Forecasting: A Comprehensive Analysis of Traditional Machine Learning vs. Deep Learning Approach
It is very important to correctly predict BSE Sensex stock prices because of the high market volatility and the increasing monetary value of investment decisions. Conventional forecasting methods have a tendency not to account for complex, non-linear patterns in the market. This paper attempts to forecast stock price movement through machine learning (ML) and deep learning (DL), using BSE Sensex 10- Year Stock Price data available at Kaggle. The data used is the daily data from April 2014 to March 2024 with open, high, low, and close prices of the index. The preprocessing of the data was used to deal with missing data, to transform the data types, and to chronologically order the records. StandardScaler was used to apply feature scaling in order to increase the performance of the model. Two predictive models, Random Forest (RF) and Long Short-Term Memory (LSTM), were implemented for stock price time-series forecasting. The models were compared on the basis of regression measures such as R2, RMSE and MAE. The experimental findings indicate that the LSTM model outperforms the Random Forest model, with an R2 of 0.9777, suggesting it is highly predictive. The results provide insight into the usefulness of deep learning (DL) methods for understanding temporal patterns and improving the accuracy of stock market forecasts.
An Analytical Survey of Root Cause Analysis Techniques for Mechanical Failures
The failure of mechanical systems has a serious effect on safety reliability and continuity in the operations of important engineering systems such as energy, transportation, aerospace, and manufacturing. Complex interactions of material degradation, cyclic stresses, environmental exposures, design constraints, manufacturing variability, and operational practices contribute to these failures. Root Cause Analysis (RCA) has come out as a systematic and structured approach to the identification of the root technical, operational, and organizational causes of such failures. This paper survey of the RCA technique with mechanical failure investigation, in which the classification of failure mechanisms, hierarchical causation modeling, and systems engineering concepts are incorporated in the unified framework. The qualitative and quantitative instruments of analysis, such as Cause-Effect Diagram, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and reliability measures, such as MTBF, MTTR, failure rate, etc., are taken critically. Complex diagnostic methods, such as fractographic characterization, vibration signal processing, non-destructive testing (NDT), and finite element analysis (FEA), are integrated to promote evidence-based validation of failure mechanisms. The suggested analytical framework enhances methodological rigor, aids in the quantification of reliability, as well as allows proactive corrective actions in terms of sustainable mechanical systems functioning and reducing the risk.
Copyright (c) 2026 Dr. Jvalantkumar Kanaiyalal Patel (Author) https://creativecommons.org/licenses/by/4.0
AI-Powered Accessibility Features for Web Applications
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.
Copyright (c) 2026 Arun Rathore (Author) https://creativecommons.org/licenses/by/4.0
AI For Cybersecurity: Real-Time Anomaly Detection in Network Traffic Using AI
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.
Copyright (c) 2026 Suraj Panthi (Author) https://creativecommons.org/licenses/by/4.0
A Survey of Code-Based vs Numerical Approaches for Piping Stress Evaluation
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.
Copyright (c) 2026 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Fraud Detection in Insurance Claims: A Deep Learning Framework for FinTech Risk Intelligence
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.
Copyright (c) 2025 Dr. Hemant N. Patel (Author) https://creativecommons.org/licenses/by/4.0
Comprehensive Study of Machine Learning Approach for Fraudulent Identification in Real-Time Financial Banking Systems
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.
Copyright (c) 2026 Sandeep Gupta (Author) https://creativecommons.org/licenses/by/4.0
The Role of Artificial Intelligence in Automating SaaS Operations: A Systematic Review of Current Techniques
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.
Copyright (c) 2026 Anoop Sunarthy (Author) https://creativecommons.org/licenses/by/4.0
Forecasting Cyber Attacks in Banking and FinTech Platforms: A Review of Data-Driven Approaches
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.
Copyright (c) 2026 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
High-Accuracy based on Deep Learning Techniques for Pattern Recognition of Wafer Defect
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.
Copyright (c) 2026 Dr. Dinesh Yadav (Author) https://creativecommons.org/licenses/by/4.0
The Role of Social Media in Public Health Intelligence: Trends, Techniques, and Challenges
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.
Copyright (c) 2026 Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by/4.0
A Data-Driven Advanced Deep Learning Model For False News Classification and Identification
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.
Copyright (c) 2026 Parth Gautam (Author) https://creativecommons.org/licenses/by/4.0
Deep-Learning-Based Crowd Density Estimation: A Comprehensive Survey
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.
Copyright (c) 2026 Shrihari Pathak (Author) https://creativecommons.org/licenses/by/4.0
A Review Of Generative Ai Applications In Financial Advisory Services: Current Developments, Limitations, And Opportunities
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.
Generative Artificial Intelligence (GAI)
Copyright (c) 2026 Sharat Priya (Author) https://creativecommons.org/licenses/by/4.0
Synthetic Defect Data Generation Using Deep Learning Architecture for Improved Wafer Inspection Performance
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.
Copyright (c) 2026 Roopesh Kumar (Author) https://creativecommons.org/licenses/by/4.0
Char Dham Care App a Technological intervention for Holy Pilgrimage with reference to Uttarakhand Char Dham Yatra
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.
Copyright (c) 2026 Parth Gautam, Aditi Saxena (Author) https://creativecommons.org/licenses/by/4.0
Enhanced Sentiment Analysis on Online Amazon Reviews Using RoBERTa with PSO-Based Hyperparameter Tuning
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.
Copyright (c) 2026 Saket Kotpalliwar, Dr. Priya Vij (Author) https://creativecommons.org/licenses/by/4.0
Automation in Smart Factories: Trends in Robotics, IoT, and Cyber-Physical Systems in Manufacturing
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.
Copyright (c) 2025 Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by/4.0
The Computational Marketer: A Comprehensive Review of Machine Learning Applications in Digital Strategy
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.
Copyright (c) 2026 Mahadeo Prasad , Jayadeep Patil (Author) https://creativecommons.org/licenses/by/4.0
Cybersecurity Challenges and Automation Frameworks in Modern Digital Transformation Initiatives
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.
Cybersecurity in Financial Institutions: A Survey of Threat Detection, Risk Assessment, and Prevention Strategies
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.
Copyright (c) 2025 Dr. Manish Jain (Author) https://creativecommons.org/licenses/by/4.0
Survey on Data-Driven Decision Support Frameworks in Industrial IoT (IIoT)-Based Manufacturing
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
IoT, smart manufacturing, Industry 4.0, predictive maintenance, cloud computing, edge computing, cybersecurity, interoperability, AI integration.
Predictive Analytics For Sales Using Historical Transaction Data And Seasonal Trends
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.
Copyright (c) 2026 Mr. Ram Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0
A Robust Machine Learning Method for Intelligent Ransomware Identify in Industrial Control Networks
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.
Copyright (c) 2026 Mr. Deepak Mehta (Author) https://creativecommons.org/licenses/by/4.0
A Review of AI-Assisted Stress Prediction Models in Mechanical Design
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.
Copyright (c) 2026 Prachi Rajput (Author) https://creativecommons.org/licenses/by/4.0