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, open-access scholarly journal dedicated to publishing high-quality original research that advances the fields of electronics, communication engineering, computer science, and emerging digital technologies. The journal provides a global platform for researchers, academicians, industry professionals, and practitioners to disseminate innovative research findings, foster interdisciplinary collaboration, and promote technological advancement. The journal serves researchers, academics, scientists, engineers, industry professionals, policymakers, and postgraduate students working in electronics, communications, computing, and related interdisciplinary fields. JGREC adheres to internationally accepted publication ethics and editorial best practices to ensure the integrity, transparency, and quality of the scholarly record. Scope JGREC welcomes original and interdisciplinary contributions in, but not limited to, the following areas: Electronics & Communication Engineering Wireless & Mobile Communications (5G, 6G, IoT, MIMO, Software-Defined Radio) Signal Processing & AI in Communications VLSI Design & Embedded Systems Microwave, RF & Antenna Technologies Optical & Satellite Communication Quantum & Nanoelectronics Cybersecurity & Cryptographic Systems Automation & IoT Applications in Electronics Smart Grid & Energy-efficient Communications Computer Science & Engineering Artificial Intelligence & Machine Learning Cloud Computing & Edge Computing Cybersecurity & Blockchain Big Data Analytics Computer Vision & Image Processing Internet of Things (IoT) & Smart Systems Quantum Computing & High-Performance Computing Human-Computer Interaction & UX/UI Research Software Engineering & DevOps Publication Model JGREC follows a continuous open-access publishing model with monthly issues. All submissions undergo an initial editorial assessment followed by a rigorous double-blind peer-review process conducted by independent experts. Accepted articles are published online immediately after completion of the editorial and production process.
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A Review on AI-Driven Approache for Diabetes Prediction in Female Populations via Risk Factor Analysis
Diabetes is fast emerging as a serious health challenge in the world, with countries around the globe spending huge resources on its management. At the same time it impacts the quality of life, especially among the female population in view of the hormonal, reproductive, metabolic and lifestyle related risk factors. Early diagnosis and timely treatment are essential to minimize complications and enhance preventive healthcare. The present review paper presents the different artificial intelligence (AI) based strategies for the prediction of diabetes based on risk factors. The study focuses on the female specific risk factors like gestational diabetes mellitus (GDM), polycystic ovary syndrome (PCOS), hormonal imbalance, obesity, genetic predispositions and socioeconomic factors related to the progression of diabetes. Furthermore, the survey examines how predictive accuracy can be improved by using machine learning (ML), deep learning (DL), federated learning, and cloud-based healthcare systems and how they can be used to personalize healthcare and facilitate early healthcare diagnosis. The results show that AI-based models, such as multimodal and DL models, outperform the traditional models in terms of prediction accuracy. However, there are a number of challenges that are still important research challenges for AI predictive systems for diabetes, such as limited data interpretability, privacy concerns, imbalanced data, and lack of female-specific data.
Copyright (c) 2026 Sunali Singh , Ram kumar Sahu , Kamlesh Raghuvanshi (Author) https://creativecommons.org/licenses/by/4.0
Real-Time Monitoring and Adaptive Control in 3D Printing: Trends, Challenges, and Opportunities
Additive manufacturing (3D printing) has emerged as a transformative technology for producing complex and customized components across aerospace, biomedical automotive, energy, and other industrial sectors. Despite its advantages, maintaining consistent print quality remains a critical challenge due to process instability, material variability, and defect formation during fabrication. This paper presents a comprehensive review of 3D printing fundamentals, real-time monitoring techniques, adaptive control strategies, and recent advancements in intelligent manufacturing systems. It systematically analyzes vision-based, sensor-driven, and multimodal monitoring approaches, along with emerging technologies for process optimization. Furthermore, current trends in adaptive and autonomous control systems are discussed, highlighting their role in improving accuracy, stability, and defect reduction. A comparative analysis of recent studies is also presented to identify key advantages, limitations, and research gaps. The findings reveal that existing approaches are limited by scalability issues, computational complexity, and lack of real-world validation. Finally, the paper outlines future opportunities toward fully autonomous, real-time, and self correcting 3D printing systems enabled by AI-driven closed loop control and multimodal sensor fusion.
Copyright (c) 2026 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
Review of Artificial Intelligence Based Cloud Cost and Security Optimization Techniques Systems
Cloud computing has gained immense importance as a critical technological infrastructure for present scalable, flexible, and cost-efficient computing services to businesses across the globe. But achieving efficient operations cost-wise along with providing high-level security continues to pose major challenges owing to the growing complexity of cloud environments. Artificial Intelligence (AI) has proven to be a potent tool that helps with intelligent resource management, prediction, automated scaling, and enhanced security. The current paper proposals an extensive review of AI-powered cloud cost and security optimization strategies. The paper reviews the main AI technologies, such as Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning, and analyzes their application in resource prediction, intelligent workload scheduling, auto-scaling, energy-efficient resource management, threat detection, intrusion detection, anomaly detection, and access control. It has been found that AI-based strategies result in better utilization of resources, cost efficiency, scalability, and enhanced security of cloud systems against emerging cybersecurity threats. The paper highlights the limitations of AI technologies and the existing challenges, including lack of explain ability, data privacy, adversarial attacks, high computational cost, and scalability issues. The overall study has shown the capabilities that can be achieved by using artificial intelligence to develop intelligent, secure, and efficient cloud computing systems.
Copyright (c) 2026 Mr. Mohit Sahu (Author) https://creativecommons.org/licenses/by/4.0
Efficient LLM Serving Systems A Survey of Model Placement, Batching, and Resource Optimization Techniques
Large Language Models (LLMs) have become a fundamental component of modern artificial intelligence applications due to their remarkable capabilities in natural language understanding, generation, reasoning, and decision support. However, the increasing scale and complexity of thesemodels have introduced significant challenges related to inference efficiency, memory consumption, lat ency, and resource utilization. This paper presents a comprehensive survey of efficient LLM serving systems, focusing on model placement strategies, batching techniques, and resource optimization approaches. The study reviews key deployment methods, including distributed serving, model parallelism, edge–cloud architectures, dynamic and continuous batching, KV-cache management, quantization, and intelligent scheduling. In addition, major challenges associated with scalability, energy consumption, load balancing, and memory management are examined. The analysis of recent literature demonstrates that the integration of system-level and algorithmic optimizations can substantially improve throughput, reduce latency, enhance resource utilization, and lower operational costs. The findings highlight that efficient memory management, adaptive scheduling, and resource-aware deployment strategies are essential for building scalable, reliable, and high-performance infrastructures for large-scale LLM serving.
Copyright (c) 2026 Prachi Rajput (Author) https://creativecommons.org/licenses/by/4.0
A Survey on the Role of Digital Twin Technology in Software Engineering and Development
Software engineering is a crucial field for the creation of software systems that are reliable, efficient, and maintainable. But complexities, changing needs, tests, and maintenance issues remain as hurdles along the software development journey. The development of digital twins (DT) has allowed for the creation of a solution that can be utilised for optimisation, analysis, simulation, and real-time monitoring of software or physical systems. This survey paper introduces some software engineering concepts and the concept of DT in the modern software development. The study elaborates on software development processes, major engineering challenges, Digital Twin types, enabling technologies and DT integration levels. Additionally, it looks at the use of DT throughout the Software Development Life Cycle, such as system monitoring, security management, process modeling, and systems engineering. Also taken up in the paper is a review of recent research and industrial applications of DT technology. The results indicate that DT can contribute to better software quality, decision-making, predictive maintenance and continuous optimization of the system. DT present a great opportunity for improving software engineering practices and software lifecycle management as software systems become more complex
Copyright (c) 2026 Mr. Deepak Mehta (Author) https://creativecommons.org/licenses/by/4.0
AI-Based Tuberculosis Detection Using Chest Radiographs and Transfer Learning
Early and accurate diagnosis of tuberculosis (TB) is essential to stop its transmission, especially in underdeveloped countries. A limitation of traditional diagnostic tests is that they typically require hours of assessment; they are also prone to human error in interpretation and cannot be scaled up in limited resource settings. This research proposes a machine learning-based (ML) system for automatic and efficient diagnosis of TB in CXRs, addressing these limitations. The proposed technique enhances the image quality when applied to the TBX11K database and the pre-processing consists of conversion to greyscale, picture scaling, Contrast Limited Adaptive Histogram Equalization (CLAHE) and normalization. Supplementing data with synthetic minority In order to rectify class imbalance and enhance dataset variety, SMOTE is utilized. After processing, images were categorized by Extreme Gradient Boosting (XGBoost) method. The data is divided into three sets: training, validation, and testing sets, so as to develop a complete model and test the effectiveness of the model. The suggested framework is proven effective in the experiments, which achieved an F1-score of 99.60%, an ACC of 99.71%, a PRE of 99.70%, and a REC of 99.74%. Analyses comparing the suggested method to preexisting ML and DL models have proven its higher classification performance. Automatic TB screening and early detection utilizing chest radiographs is now possible with the help of the created framework, according to the results. It's dependable, efficient, and accurate.
Contrast Limited Adaptive Histogram Equalization (CLAHE)
Copyright (c) 2026 Dr. Manish Jain (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning Techniques for Natural Gas Exploration, Production and Distribution: A Review
Machine learning (ML) technology is increasingly becoming an important tool in the natural gas industry, providing advancements in areas such as exploration, production, reservoir characterization, and pipeline transportation. Unfortunately, the classical methods have certain shortcomings, which include the employment of complicated non-linear models and high computational cost as well as large-scale reservoir simulation problem. To address the aforementioned challenges, various ML models, including but not limited to support vector machines (SVMs), random forests (RF), linear regression (LR), extreme gradient boosting (XGBoost), artificial neural networks (ANNs), and other types of deep learning techniques, have been applied in forecasting, prediction, fault diagnosis, and optimization applications. The review paper will give an insight into the application of machine learning techniques in natural gas exploration and production process and pipeline transport. It will highlight the capabilities of ML techniques to analyze and deal with big data, improve prediction accuracy, facilitate intelligence-based decision making and increase operational efficiency in the natural gas industry. Furthermore, it will discuss some limitations of ML approaches in the field. Finally, future research directions are presented highlighting the need for ML in creating intelligent, reliable, and automated natural gas systems for sustainable industrial operations.
Copyright (c) 2026 Dr. Parth Gautam (Author) https://creativecommons.org/licenses/by/4.0
Multi-Agent Reinforcement Learning for Autonomous Decision-Making Systems: A Survey
The autonomous systems are emerging in complex and dynamic contexts in which several intelligent agents have to coordinate, adapt and make decisions in the face of uncertainty. Single-agent reinforcement learning typically performs poorly in such environments due to issues with agent interactions, decentralized information, and non-stationary learning conditions. One promising framework that could enable multiple agents to learn cooperative, competitive, or mixed strategies, while continuously interacting with their environment and with each other, is Multi-Agent Reinforcement Learning (MARL). In this paper, a wide survey of MARL for autonomous decision-making systems is presented. It examines the core principles and models of MARL, such as cooperative and competitive learning paradigms, centralized training and decentralized execution (CTDE), and key algorithmic categories like value-based, policy-based, and actor–critic. In addition, the survey analyzes the use of MARL in autonomous decision-making architectures. It identifies its use in autonomous vehicles, unmanned aerial vehicle (UAV) swarms, multi-robot systems, smart grids, and industrial automation. Some of the main difficulties, such as scalability, coordination, credit assignment, safety, reliability, communication limitations, and partial observability, are also examined. Finally, the paper discusses current research trends and future directions to improve the scalability, robustness, explain ability, and real-world deployment of MARL-enabled autonomous systems. The survey provides an organized view of today's progress and challenges in this ever-changing field.
Multi-Agent Reinforcement Learning (MARL)
Copyright (c) 2026 Dr. Pradeep Laxkar (Author) https://creativecommons.org/licenses/by/4.0
Automated Skin Lesion and Cancer Classification Using AI-Driven-Deep Convolutional Networks
Skin cancer is one of the most common cancers worldwide, and the longer it remains untreated, the greater the likelihood of serious consequences or even death. Traditional diagnostic techniques depend on the expert's visual evaluation and are thus subjective, laborious, and very variable. As a result, getting a proper diagnostic done quickly is crucial. To enhance the accuracy and efficacy of skin lesion diagnosis, this effort seeks to develop an automated classification system using dermoscopy images. The authors of this study propose building and testing a skin lesion classification AI system that makes use of various ML and DL techniques, including Xception, ResNet, CNN, ANN, and LSTM models. The proposed convolutional neural network (CNN) model achieved the highest reliability and accuracy in skin cancer diagnosis (98.49 F1-score, 98.58% ACC, 98.57% PRE, and 98.59% REC) compared to all other models. This method can help dermatologists identify skin disorders earlier and more accurately, which in turn improves clinical decision-making and decreases the incidence of misdiagnosis.
Copyright (c) 2026 Dr. Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by/4.0
Minimum Response Time Optimization in Cloud-Based IoT Systems
Cloud computing and IoT technologies are evolving quickly, leading to a growing need for intelligent systems that can process the dynamic workloads with minimal latency. However, effective response time and resource utilization are still huge challenges in cloud-IoT environments. This study suggests a minimum response time optimization framework for cloud-based IoT systems to achieve latency-aware resource scheduling and adaptive optimization under dynamic workloads. Data preprocessing, feature engineering, exploratory data analysis, and normalization follow the utilization of the Multi-Tier IoT Resource Allocation Dataset from Kaggle. The metrics MAE, MSE, RMSE, and R² are used for training and evaluation of Random Forest (RF), Gradient Boosting (GB), Stochastic Gradient Descent (SGD), and Deep Neural Network (DNN). Experimental outcomes show that the DNN model outperforms the base models including RF, GB, and SGD with an MAE of 5.9605 ms and an R² score of 0.9830. Results show that the suggested strategy is more efficient and performs better overall in terms of response time minimization, latency optimization, throughput improvement, energy efficiency, and SLA-aware QoS enhancement. The novelty of this work is in combining the three elements of response time prediction, adaptive resource allocation, and optimization analysis in a single framework, which can effectively enhance the performance and reliability of cloud-based IoT systems.
Copyright (c) 2026 Hitesh Kumar Sharma, Dr Samta Jain Goyal, Dr. Sumit Kumar (Author) https://creativecommons.org/licenses/by/4.0
Recent Advances in Deep Learning-Based Disease Detection in Garlic Plants in Agriculture: A Review
AI and DL technologies have rapidly evolved and revolutionized modern farming practices, especially in automated plant disease detection and precision farming. This crop is economically and nutritionally significant and highly susceptible to diseases that affect yield, quality of bulbs and market value, including white rot, basal rot, purple blotch, downy mildew, rust and viral infections. Diagnosing disease promptly and correctly is very important for production losses and sustainable garlic production. Diagnosing diseases using conventional approaches that rely on human eye observation and experience can be challenging for large-scale agriculture due to their potential drawbacks, such as being costly, subjective, labor-intensive, and time-consuming. Automatic disease identification by image analysis using DL approaches has been more popular in the past few years. Finding diseases in garlic using Deep Learning (DL) techniques including YOLO, CNNs, Vision Transformers (ViT), hybrid CNN-ViT, and Generative Adversarial Networks (GAN) is the focus of this publication. Furthermore, the study discusses current research progress, key challenges, limitations, and future opportunities for developing accurate, scalable, and intelligent garlic disease detection systems for precision agriculture.
Copyright (c) 2026 Dinesh Kumar Salitra, Dr Prabhat Singh (Author) https://creativecommons.org/licenses/by/4.0
Design and Implementation of a Zero Trust Security Framework for Cloud-Based SaaS Applications
Perimeter-based security solutions are becoming less and less effective due to the growing complexity of contemporary cyber threats and the collapse of old network boundaries. As a result, Zero Trust Architecture (ZTA) has become a revolutionary paradigm that redefines how cloud and hybrid IT infrastructures build, maintain, and revoke trust. This paper presents an intelligent Zero Trust Security Framework based on attention-based deep learning models to detect intrusions in real-time and dynamically enforce policies within SaaS applications. The framework combines the DNN + Attention and RNN + Attention models to continuously observe the network traffic and dynamically determine if the requests are legitimate or malicious and make ALLOW or DENY/QUARANTINE decisions. The CICIDS2017 dataset is used for experiment, followed by the preprocessing, feature analysis, data balancing using SMOTE and model training. Experimental results show that DNN + Attention model has the highest accuracy, F1 score and AUC-ROC of 97.00%, 92.75% and 99.63% respectively with less latency and faster prediction time compared to RNN + Attention model. The proposed framework is validated and demonstrated as effective in an intelligent intrusion detection, real-time threat containment, and secure SaaS application monitoring for current Zero Trust environments by comparing to existing approaches.
Copyright (c) 2026 Dr. Neetu Sikarwar (Author) https://creativecommons.org/licenses/by/4.0
Asset Integrity Management Frameworks for Industrial Facilities: A Survey
To optimize assets and the environment, asset integrity is a continuous process of applying knowledge and skills to control risk from facility design, installation, and operation across the course of the asset's existence. The Industrial Facilities are concerned with the safe, dependable, and affordable operation of critical assets throughout their lifecycle. The paper provides a broad overview of the Asset Integrity Management (AIM) by incorporating the lifecycle management principles, risk-based approaches, and data-driven technologies. It addresses the theoretical foundations of AIM, including design and material selection, maintenance planning, inspection and monitoring, and risk assessment and life-extension strategies. An approach based on the lifecycle is highlighted to organize availability, maintainability, and safety requirements with the functional and operational requirements between the conceptual design phase and decommissioning. The unified framework assesses criticality and risk categorization with the help of the RBI, FMEA and probabilistic risk assessment. The electrical, mechanical, and electronic integrity of assets is discussed. Finally, the paper has also shown that data-driven prognostics, machine learning, sensor arrays based on IoT, and advanced signal processing are crucial in predictive maintenance and health-based monitoring. The proposed framework will contribute to the improvement of reliability, regulatory compliance and sustainable industrial activities.
Copyright (c) 2026 Aravindh Balan (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning- Based Condition Monitoring of Wind Turbines Using SCADA Dataset for Early Fault Detection
There has been a steady increase in the amount of wind power installed worldwide as a result of global efforts to replace fossil fuels and lower the average global temperature. The LCO of wind energy includes, among other things, the costs of operating and maintaining wind farms. Using machine learning and SCADA data, this research proposes a framework for early defect identification in wind turbines. The approach is designed for use with wind turbines. The proposed methodology contains data preprocessing methods like data cleaning, data normalization, feature selection, label encoding and SMOTE for the class imbalance issue. Processed data is then classified into fault and non-fault conditions using XGBoost and LightGBM (LGBM) models to effectively classify the data. The experimental outcomes showed that the proposed models have high fault detection performance, with accuracy of 95.2% for XGBoost, and the highest accuracy of 95.6% for LGBM, which has better values of precision, recall, and F1 score. The performance of the proposed LGBM is related to other machine learning models like NB, KNN and SVM by obtaining better classification performance with low misclassification rates. This framework could greatly aid predictive maintenance, keep turbines up and running, cut down on maintenance expenses, and improve the operational stability of wind power systems.
Few-Shot Question Answering in Low-Resource Languages using Model-Agnostic Meta-Learning (MAML)
Question Thanks to massive datasets like SQuAD, Question Answering (QA) systems have made tremendous strides in languages with abundant resources, such as English. Unfortunately, model performance is severely constrained in low-resource languages due to the lack of annotated data. The Model-Agnostic Meta-Learning (MAML) framework is suggested in this study as a means of few-shot quality assurance in languages with limited resources. With only a small number of annotated question-answer pairs, the method allows for quick domain or language adaptation. With an emphasis on low-resource Indian languages like Telugu, Bengali, and Hindi, we assess the framework using the multilingual QA standards TyDiQA and XQuAD. Our MAML-based technique achieves an 8.5% increase in F1 score and an 8.2% improvement in Exact Match (EM) over the best fine-tuned baselines, according to experimental data. This method considerably outperforms standard fine-tuning and transfer learning approaches in few-shot circumstances. This study demonstrates how meta-learning may be used to create flexible and scalable quality assurance systems for languages that aren't widely used
Copyright (c) 2025 Md Tahseen Equbal, Md Irshad Anwar, Wasim Ahmad Sheikh, Arif Rasul, Md Wasim Nehal, Md Ashad Iqbal (Author) https://creativecommons.org/licenses/by/4.0
Leveraging AI-Powered Business Intelligence for Data-Driven Decision Making in the Retail Industry
The constant innovations in artificial intelligence which have acted as a deciding factor in the fast-paced and competitive retail environment, machine learning (ML) and deep learning (DL) applications are gaining relevance in the industry. The demand for and the scarcity of data analytics experts, however, remain. So, there is an urgent need for more efficient and accessible intelligent forecasting systems to meet the demand. To overcome this problem, this work suggests an Ensemble Random Forest (RF) and Gated Recurrent Unit (Ensemble RF+GRU) model for accurate retail sales forecasting and data-driven decision making. The proposed model is a fusion of the advantage of deriving features from a RF with the advantage of sequential learning of a GRU, which improves the accuracy (ACC) of forecasting and the efficiency of prediction. The Retail Sales Forecasting Dataset provided by Kaggle was used for experimentation and data was preprocessed to enhance the quality. The results of experiments showed that the proposed Ensemble RF+GRU model had an R2 value of 94.5%, MSE of 79564.47, RMSE of 282.07, and MAE of 134.62, which were better than the other models such as LR, SVM, RNN, and ANN. The results proposed that the Ensemble model yields correct, reliable, and efficient forecasting performance and is suitable for intelligent decision-making in the retail industry
Copyright (c) 2026 Aniket Naresh Bachhav (Author) https://creativecommons.org/licenses/by/4.0
A Review and Performance Study of AI Techniques in Asthma Prediction
Asthma is a chronic respiratory disease that affects millions of people worldwide and continues to impose a significant burden on healthcare systems due to frequent exacerbations, hospitalizations and treatment costs. Recent advances in Artificial Intelligence (AI) have made the development of intelligent prediction models possible for early diagnosis, risk assessment and personalized disease management. This review discusses recent studies for AI-based asthma prediction and role of structured and unstructured clinical data, Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) techniques. Some of the commonly used algorithms include Random Forest, XGBoost, Support Vector Machines, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks and transformer-based NLP models. The studies reviewed show that ensemble learning and deep learning approaches tend to outperform the traditional statistical methods in terms of prediction. Moreover, intelligent healthcare systems have growing potential as manifested by AI applications such as smart inhalers, EHR-based symptom extraction and non-invasive breath analysis. However, promising results are still hindered by important challenges such as data imbalance, model interpretability, privacy, and dataset standardization. Future work should focus on developing explainable, privacy-preserving, and clinically deployable AI frameworks for the prediction of asthma.
Copyright (c) 2026 Dr Prashant Kumar Srivastava (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence- Driven Analysis for Pharmaceutical Product Pricing and Composition
Increased costs in the health sector have placed significant pressure on public budgets allocated for pharmaceutical procurement. In the context of financial crises and global health emergencies, national purchasing authorities face a critical challenge of obtaining high-quality pharmaceuticals at minimal cost. Although existing literature has explored various influencing factors using producer and reference price data, limited attention has been given to individual-level purchase data from diverse public buyers. To fill this gap, this study suggests an ensemble-based AdaBoost classification method using the DrugBank database which contains about 4900 pharmaceutical compounds. The dataset is subjected to robust preprocessing, such as handling missing values, removing duplicates, implementing One-Hot Encoding, normalizing data using the Z-score, and balancing the classes using RandomOverSampler, ensuring the quality and robustness of the data for modeling. To perform comparative analysis the following multiple machine learning models are evaluated including KNN, SVM, CNN, XGBoost and the proposed AdaBoost Classifier. Experimental results show that the proposed AdaBoost classifier has excellent predictive ability and can achieve an accuracy of 99%, a precision of 99%, a recall of 99%, and an F1 score of 99%. The results show the effectiveness of ensemble learning for achieving relationships in pharmaceutical data. The study concludes that the proposed model can be used as a solution with very high accuracy and reliability in predicting the price and composition of pharmaceutical products.
A Multi-Dimensional Design Model for Secure Multi-tenant Architecture in Cloud Computing: Synergising Hyper-Isolation, Zero Trust, and Cryptographic Integrity
The rapid proliferation of cloud computing has established multi-tenancy as the primary architectural paradigm for ensuring cost-efficiency and resource optimization. However, the sharing of physical infrastructure—including memory, CPU caches, and network bandwidth—among mutually distrusting tenants introduces profound security challenges, such as cross-tenant side-channel attacks and unauthorized data exfiltration. This review article proposes a robust design model for secure multi-tenant architecture, termed the "Deep Isolation Framework." We synthesize current research on hardware-assisted virtualization, container orchestration, and micro-VM technologies to evaluate their efficacy in multi-tenant environments. Furthermore, we investigate the integration of Zero Trust Architecture (ZTA) and Post-Quantum Cryptography (PQC) as essential components for future-proofing cloud security. This paper follows a systematic literature review methodology, analyzing 50+ high-impact research papers from the last decade. Our comparative analysis reveals a critical performance-security trade-off, where we propose a modular approach to balance isolation strictness with operational latency. The findings provide a comprehensive blueprint for cloud service providers to build resilient, secure-by-design infrastructures that mitigate the "noisy neighbor" effect and ensure absolute tenant privacy in an increasingly adversarial digital landscape.
Copyright (c) 2026 SWAPNIL JOSHI (Author) https://creativecommons.org/licenses/by/4.0
Advancing Cybersecurity with AI for Intrusion Detection Systems: A Review and Analysis of Tools, Techniques, and Challenges
The state of cybersecurity can be improved with the help of AI-based Intrusion Detection Systems (IDS) because they enable the recognition of sophisticated threats in a dynamic network environment in an intelligent, dynamic, and real-time manner. The paper goes even deeper into the design of IDS, such as host-based, network-based, and hybrid, followed by the conventional detection methods, such as signature-based, anomaly-based, and specification-based. The paper continues to discuss the more advanced AI practices, noting ML and DL models such as SVM, Random Forest, CNN, and LSTM, which enhance the identification of known and zero-day attacks. The report continues by examining some of the enabling technologies, such as cloud-based IDS, Big data streaming platforms such as Kafka and Spark, AI-based security applications, and ML frameworks. Applications, such as real-time monitoring, multi-tenant security and DevSecOps integration, are explained alongside problematic issues, which are noisy data, computational overhead, encrypted traffic, adversarial attacks and limited real-world implementation. Overall, this study emphasises the need for AI-based IDS that are scalable, efficient, and strong enough to tackle all the new cyber threats.
Copyright (c) 2026 Girish Thakre, Manish kumar Rohila, Arjun Rajput, Saurabh Karsoliya (Author) https://creativecommons.org/licenses/by/4.0
An Attention-Based Hypergraph Neural Networkfor Fake News Detection: Design, Analysis, andPerformance Evaluation
The accelerating development of social media has largely contributed to the spread of fake news, presenting critical problems to social credibility and the stability of society. Conventional detection methods usually do not reflect the multifaceted association between users, posts, and news content. The paper introduces an attention-based Hypergraph Neural Network (HGNN) to detect fake news based on the UPFD dataset. In contrast to traditional graph-based models, which only describe pair-wise relationships, the proposed model uses hypergraphs to describe higher-order interactions among many entities. The attention mechanism is incorporated in the model so that it can give more priority to various hyperedges, which enhances the learning of the features and the classification. The experimental findings prove that the proposed scheme is more accurate, more precise, more recall, and has a higher F1-score than the traditional machine learning models and regular Graph Neural Networks (GNNs). A comprehensive analysis of the model's efficacy, constraints, and potential future applications is also included in this work.
Copyright (c) 2026 Mitali Saxena, Chandan Kumar, Priyanka Saxena (Author) https://creativecommons.org/licenses/by/4.0
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.
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