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.
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.
Copyright (c) 2026 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2026 Dr. Manish Jain (Author) https://creativecommons.org/licenses/by/4.0
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
Machine Learning Approaches for Threat Detection in Android Mobile Networks
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
Copyright (c) 2026 Mr. Sachin Manekar (Author) https://creativecommons.org/licenses/by/4.0
Trends, Challenges, and Future Directions in Machine Learning-Based Housing Price Prediction
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.
Copyright (c) 2025 Mr. Deepak Mehta (Author) https://creativecommons.org/licenses/by/4.0
A Machine Learning Based to the Enhancement of Electrical Fault Detection and Classification
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.
Copyright (c) 2025 Dr. Parth Gautam (Author) https://creativecommons.org/licenses/by/4.0
AI-Powered Tools in Software Engineering Applications in Code Generation and Quality Assurance
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.
Copyright (c) 2025 Dr. Pradeep Laxkar (Author) https://creativecommons.org/licenses/by/4.0
A Comprehensive Survey on Blockchain-Based Digital Payment Systems and Their Impact on Financial Inclusion
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.
Copyright (c) 2025 Sandeep Gupta (Author) https://creativecommons.org/licenses/by/4.0
A Systematic Survey of Machine Learning Methodsfor Fraud Identification in Credit Cards
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.
Copyright (c) 2025 Prachi Rajput (Author) https://creativecommons.org/licenses/by/4.0
Latest Advances in PCMs (Phase Change Materials) for Thermal Regulation Applications: A Survey
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.
Copyright (c) 2026 Dr. Roopesh Kumar (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Energy Usage in Data Centers Using AI Techniques
Data center energy optimization has come to be a very pressing research problem due to the active evolution of the digital infrastructure and the increasing environmental issues. The existing data centers are highly computing-intensive, with cooling and power distribution thus high operational costs and carbon emissions. The given paper examines the most critical energy consumption characteristics of data centres and discusses more advanced approaches to AI to improve energy efficiency. It discusses AI-based models, such as deep learning, reinforcement learning, and hybrid optimization methods, in predictive workloads, cooling strategies optimization, and better power management. The hybridization of AI with the energy management system, edge-cloud architecture, and smart monitoring systems is also discussed. It puts emphasis on performance measures such as the PUE, CUE, and system reliability. Based on the findings, AI-based optimization of the next-generation data centers can be made much more energy efficient, robust, and sustainable.
Copyright (c) 2025 Ms. Anamika Soni (Author) https://creativecommons.org/licenses/by/4.0
A Review of Thermal and Mechanical Design Challenges in Modern Data Centers
Thermal and mechanical design concerns in modern data centers are getting the highest priority due to increasing density and intensity of the IT work. The three-level network topology that was in existence in the past would not accommodate the modern east-west traffic, and this has led to the use of spine-leaf (Clos) topologies that offer low-latency, high-bandwidth, and scalability. The lack of uniform time-varying loads of high-density IT equipment makes thermal management even more difficult and needs advanced cooling solutions such as (but not confined to) airflow optimization, liquid cooling, and modular designs. The mechanical design must be in such a way that it provides structural integrity, use space efficiently, reduces vibration and also reduces noise as well as scalable and reliable cooling infrastructure. Efficiency, maintainability, and adaptability are the three metrics used to compare room, row, and rack-based cooling solutions. Combining the multidisciplinary thermal and mechanical aspects with the scalability of network architecture is also required to make the aspects of component reliability, operational efficiency, and energy performance sustainable in the current data centers. It is an integrated design of high-performance, durable and economical facilities.
Copyright (c) 2025 Mr. Anoop Sunarty (Author) https://creativecommons.org/licenses/by/4.0
A Review of Fault Simulation Techniques Focused on Stuck-At Models in Digital IC Testing
Stuck-at fault models are also used to determine the test and reliability of digital integrated circuits (ICs), especially where manufacturing defects and failures due to aging can occur. This review paper has addressed the ATPG flows and fault simulation techniques associated with both stuck-at fault (SAF) and transition delay fault (TDF) models in order to reveal their importance for catching small delay defects (SDDs) which otherwise pass undetected by the usual testing. A comprehensive analysis of various ATPG flows like incremental, merged, and split-model methods shows the important role played by different fault models (SAF, TDF, static and dynamic crosstalk-aware faults) in the defect coverage of state-of-the-art semiconductor technology. The review also deals with the shortcomings of the conventional delay testing and suggests the use of faster-than-at-speed testing (FAST) and WeSPer as a metric for assessing test quality under varying clock frequencies to the end of motivating the use of those methods. Fault simulation techniques such as serial, parallel, deductive, differential, and concurrent testing are analyzed to get an insight about their efficiency and applicability in the current VLSI testing. After that, the paper dwells on the impact of digital IC testing over various areas spanning VLSI design validation, built-in self-test (BIST), and semiconductor manufacturing, hence illustrating the connection between improved fault modeling and simulation and the three factors of higher yield, reliability, and robust test strategies in nanoscale technologies.
Copyright (c) 2025 Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
A Deep Review of Hardware Trojan Identification Methods in System-on-Chip Architecture
Hardware Trojans (HTs) have become one of the most important dangers to the modern integrated circuits, particularly as System-on-Chip (SoC) designs are becoming both larger and more complex and dependent on globalized supply chains. Such malicious modifications may undermine functionality, cause sensitive information to get stolen, or provide backdoors that may remain unnoticed, and that means that effective detection methods are necessary to ensure that modern hardware platforms are secure. The higher diversification of chip architectures such as digital, analog or mixed-signal, RF as well as biochips also contributes to increased pressure on the security mechanisms that are robust and adaptive. A broad overview of hardware Trojan detection methods is presented in this work, especially paying attention to machine learning (ML) and deep learning (DL) development. Look at the pre-silicon (static and dynamic) verification schemes, post-silicon (side channels) and the new smart detection schemes that make use of the power, electromagnetic, timing, and PUF-based signals. Other issues that were identified in the survey are the computational overhead, data limitations and the cross-platform generalization problems. Overall, the work provides the existing advancement and trace the way to advance further research to enhance trusted SoC design and the security of hardware.
Copyright (c) 2025 Dr. Sunita Dixit (Author) https://creativecommons.org/licenses/by/4.0
The Role of Machine Learning in Loan Default Prediction Trends, Challenges, and Future Directions
Predicting loan default has become one of the most important areas of study in the financial world because of the need to lower credit risk and find long-term ways to lend money. While credit scoring systems and logistic regression have provided some minimal answers, these traditional statistical models do not succeed of capturing the complicated borrowers' nonlinear patterns and behaviors. With machine learning, novel predictive models have been presented that are more sophisticated in predicting risk, such as supervised, ensemble, deep learning and hybrid models, which greatly add to the precision and effectiveness of credit risk forecasting. This paper is a complete overview of the use of machine learning in predicting loan defaults by presenting the underlying methodology, data, and feature engineering approaches. Moreover, their use in banking and fintech, the use of big data and other sources of information, and the increased relevance of explainable and interpretable AI are also considered. Some of the most important challenges, like the imbalance of data, model transparency, and regulatory compliance, are outlined, and the new research directions of developing more precise, scalable, and understandable systems to guide financial decisions are discussed.
Copyright (c) 2025 Mr. Sachin Manekar (Author) https://creativecommons.org/licenses/by/4.0
Human Resource Analytics for Enhancing Employee Performance through Machine Learning Techniques
Human Resources (HR) onboarding is a critical step in integrating new employees into an organization. Effective onboarding can improve employee satisfaction, reduce turnover, and accelerate productivity. However, traditional methods for onboarding and predicting employee success are often limited to individual-level attributes, neglecting the complex network of relationships and team dynamics that influence an employee's experience. This study focuses on predicting employee performance using machine learning and deep learning techniques, with a particular emphasis on effective feature selection and preprocessing. The Employee Dataset, consisting of diverse demographic and organizational attributes, was preprocessed through normalization, outlier removal, and dimensionality reduction using PCA to ensure improved data quality and reduced redundancy. Deep Neural Network (DNN) model, was developed and evaluated. The results demonstrate that the proposed DNN model achieved a high accuracy of 96.25%, along with a precision of 98%, a recall of 97%, and an F1-score of 97%. These findings highlight the superior capability of the DNN in capturing complex patterns within employee data, making it a highly effective approach for enhancing prediction accuracy and supporting data-driven decision-making in employee performance management.
Copyright (c) 2025 Dr. Amit Jain (Author) https://creativecommons.org/licenses/by/4.0
A Survey on Property Insurance Claims Using Machine Learning Models in Finance Sector
The complexity of property insurance claims and the rising number of risk factors involved in financial risk prediction are factors that have made proper early claim prediction an essential requirement among insurers. Conventional means of assessment cannot process heterogeneous data, and increases of fraud attempts and the changing conditions of the market, which demonstrates the need to use more efficient and data-focused approaches. This paper considers the property insurance terrain within the finance industry and investigates how supervised and unsupervised machine learning methodologies can be used to enhance the accuracy of claim prediction and the operational decision-making process. The survey summarizes the important considerations in claims, key important ML approaches applied in risk assessment, and issues in early prediction, such as data imbalance, scarcity of early information, and large-scale heterogeneous data. Combining the results of recent research, this paper enables systematic knowledge on the effects of ML in improving claim assessments, fraud detection, and risk modeling in property insurance. The results also bring out the existing constraints that should be overcome in order to attain credible and scalable early claim prediction.
Copyright (c) 2025 Prof. (Dr.) Abid Hussain (Author) https://creativecommons.org/licenses/by/4.0
Thermodynamic Analysis of Hybrid Energy Systems: A Review of Modeling and Optimization Strategies
The rising demand of energy and the rising concerns of the environment have provided a great demand of efficient, reliable and sustainable hybrid energy solutions. Conventional single-source energy systems tend to be very difficult on intermittency, conversion loss, and sub-optimal performance. To gain insight into the role of energy and exergy techniques in enhancing the operation of multi-source arrangements, in this paper, an overview of thermodynamic analysis, optimization strategies and recent advances in Hybrid Energy Systems (HES) is provided. According to the review, the following factors play a role in increasing the reliability and operational efficiency because of energy flow modeling, subsystem interaction and load-matching strategies: Moreover, more intricate exergy-based assessment can be used to detect irreversibilities, which allows designing a better system. Recent works concerning the hybrid storage integration, dynamics of PV winds, fuel-cell assisted systems, and microgrid control structures are also reviewed. The results suggest the increased applicability of optimization techniques, such as multi-objective and intelligent control algorithms, to the issues of efficiency, cost, and environmental constraints. In general, the research paper offers a systematic insight into the thermodynamic-based enhancements that are imperative in the design of new, high-performance HES architectures.
Copyright (c) 2025 Ram Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Quality Across Manufacturing and Storage Systems Strategies for Efficiency and Integrity
The manufacturing and energy industry are typical complex large systems which cover a long cycle such as design, production chain, production or operation, after-sales, etc. This review is a thorough analysis of the current quality management practices in manufacturing and warehousing, which have undergone changes due to Industry 4.0. It combines articles about classical quality control models, conditions of utilization of statistical methods, and cutting-edge artificial intelligence-software-powered optimization tools. The evaluation shows that these technologies such as machine learning, deep learning, reinforcement learning, and digital twins will contribute to the processes becoming more stable, defects' catching being improved, and predictive maintenance being the most efficient. Other topics discussed include the impact of cloud and edge computing on the capacity of real-time decision-making to be enlarged and the decision-making to be more efficient. Besides, in warehousing, the review points out smart systems, RFID, blockchain, temperature–humidity monitoring, and automation as the main factors that contribute to operational excellence and sustainability. All in all, it is a matter of how integrated, data-driven, and environmentally friendly practices that are able to handle the quality, reliability, and resilience concerns of modern industrial systems.
Copyright (c) 2025 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by/4.0
Optimized Histopathological Image Classification for Breast Cancer Using Deep Learning Model
A significant cause of death very early among women is due to breast cancer. Since invasive ductal carcinoma (IDC) and breast cancer in general continue to be among the most common and fatal illnesses affecting women, prompt identification is crucial. Convolutional neural networks (CNNs), in particular, are particularly notable in automating image processing of breast cancer to the point that the images do not need human interpretation. The proposed project enhances the detection of IDC by creating a reliable diagnostic algorithm, in which deep learning and histopathology image analysis are used. On a large-scale dataset of IDCs containing more than 277,000 image patches, it applied a full preprocessing pipeline, including Otsu thresholding, tissue masking, Hematoxylin channel extraction, CLAHE enhancement, Gaussian denoising and gamma correction to improve image quality. Images with low tissue content were eliminated and SMOTE has been utilized to work on the imbalance of classes. This was a fully fine-tuned ResNet50V2 that was pretrained on ImageNet, and then combined with own dense layers and trained based on Adam optimizer and binary classification. Accuracy (acc) of the model was 88.52%, and precision (pre), recall (rec), and F1-score (F1) were greater than 88%. The effect of various architectural and training setups was studied using ablation and confirmed the efficiency of the chosen model. The comparative analysis yielded better performance in comparison to the existing CNN, CNN-GRU, and DenseNet-based models. The results indicate the possibility of AI-based breast cancer detection to be used in clinical practice, decrease errors in diagnosis, and increase the rate of early diagnoses. Combined application of the state-of-the-art image enhancement, balanced data depiction, and comprehensive study of the ablation using pre-trained CNN models is novel. This adds a high-performing and interpretable, yet clinically relevant, solution to the early diagnosis of breast cancer by using histopathology.
Copyright (c) 2025 Anjali Choudhary, Prof Rajkumar Sharma (Author) https://creativecommons.org/licenses/by/4.0
Applications of Deep Learning Approaches for Defect Identification: Trends, Challenges, and Future Directions
Quality assurance in many fields relies on the ability to detect defects. This is especially true in the manufacturing, construction, electronics, and medical diagnostics sectors. As deep learning becomes more prevalent in industrial inspection systems, its revolutionary effect on defect identification is being felt in many different fields. This article summaries current methods for detecting defects in manufacturing, infrastructure, and biomedical imaging using deep learning. It examines methods such as CNNs, generative models, attention mechanisms, and emerging transformer and diffusion frameworks, focusing on their effectiveness in surface anomaly detection. The paper categorizes approaches into supervised, unsupervised, and semi-supervised models, examining their suitability under different data conditions and deployment scenarios. It also highlights key implementation challenges, including data imbalance, annotation complexity, dataset variability, and generalization across domains. There are also important items like the quality of the dataset, model interpretability, scalability, and real-time performance that are mentioned to ensure the successful implementation of AI in real-world scenarios. Future directions include the emergent technologies of domain-adaptive learning, explainable AI and the deployment of AI at the edge, where it could be applied to real-time inspection. The review summarizes the recent advances and highlights the methodologies to enhance the transparency and reliability of deep learning-based defect detection and the emergence of intelligent and high-performance adaptive inspection devices in industries.
An Intelligent System for Identifying Fraud Phone Calls Using Machine Learning Algorithms
Phone fraud, also known as spam and unwanted calls, is a major problem in the telecom industry, costing millions of dollars annually all around the globe even as technology goes further and further ahead. The rise of phone scams, also known as spam using a mobile phone or a telephone, has become a common security risk for businesses and individuals alike. Machine learning and artificial intelligence have shown promising results in analyzing and detecting fraudulent or harmful phone calls. This paper gives an efficient method to forecast fraud calls through Call Detail Records (CDR) based on a selection of different machine learning and clustering algorithms. Data cleaning, normalization, feature selection, and data balancing based on SMOTE were deemed applicable to the CDR dataset that includes fundamental features, including caller ID, called ID, duration, cost, destination, and call type. The various models that have been used include DB scan, SVM, GCN, and XGBoost which revealed patterns of fraudulent behavior. The proposed XGBoost model had the best accuracy score of 96.7 %, illustrating that it could better recognize fraud than the others.
Redundancy of Collisions and Data Repetition A State-of-the-Art AES Algorithms with SHA-512 to Support a High-Integrity IoT Storage
The secure data transmission through the internet is a growing field of work that is vital in streamlining service delivery in modern infrastructure. The study destroys the age-old dilemma of optimization of redundancy in the data security across storage servers that serve data to multiple users and ownership systems. Older hashing algorithms such as MD5 that is prone to collision issues and lack of enough key lengths and vulnerability in sharing keys are often found in conventional methodologies. In order to eliminate these constraints, a new and effective algorithmic method is suggested. This model applies an Advanced AES-based encryption system (which is mentioned in the sources as an Asymmetric Encryption System) to guarantee high-level privacy and security of data. At the same time, SHA-512 is built in to calculate hash, allowing secure detection of files at a faster rate than the older system and redundancy detection through chunking of files on a file-level. The proposed methodology was simulated using MATLAB, and it was shown to work better than the old encryption methods in terms of such parameters as block size, hash output security, and time complexity.
Copyright (c) 2025 Chandralok Kumar, Dr Arvind Kourav, Nehul Mathur (Author) https://creativecommons.org/licenses/by/4.0
Lightweight Machine Learning Techniques forHardware Trojan Detection in IoT Devices
Hardware trojans are malicious pieces of software that attempt to prevent the normal operation of a chip, and are carefully engineered not to be detected during the silicon design and verification phase before it is actually sold to a consumer. The military, business, and academics are all looking into this new threat. Consequently, as a defense during chip deployment, run-time hardware Trojan identification is vitally needed. This work focuses on hardware Trojans that affect processor performance. This study presents a machine learning-based approach to detecting hardware Trojans in IoT devices by exploiting the Hardware Trojan Dataset. To preserve the most relevant features, the dataset was subjected to a thorough preparation procedure that included data cleaning, augmentation, label encoding, normalization, and feature selection using PCA. A number of models were evaluated, including Logistic Regression, ResNet, Decision Tree, and Long Short-Term Memory (LSTM). With the highest accuracy, precision, recall, and F1-score of 95.25%, 95.25%, 95.27%, and 95.25%, respectively, the LSTM model fared better than the others. The outcomes demonstrate how well feature selection and sequential deep learning architectures work together to capture temporal relationships in power trace data. Overall, the suggested approach shows a strong and trustworthy foundation for improving IoT hardware security against Trojan assaults.
Copyright (c) 2025 Dr. Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by/4.0