Journal of Global Research in Multidisciplinary Studies(JGRMS)
Aim The Journal of Global Research in Multidisciplinary Studies (JGRMS) is an international, peer-reviewed, open-access scholarly journal published by Saanvi Publications. The journal is dedicated to promoting the dissemination of high-quality, original, and impactful research that advances knowledge across multiple academic disciplines. JGRMS aims to provide an inclusive global platform for researchers, academicians, scientists, engineers, healthcare professionals, educators, industry experts, policymakers, and practitioners to publish innovative research findings that address contemporary scientific, technological, social, environmental, and industrial challenges. The journal encourages interdisciplinary collaboration and fosters the exchange of ideas that contribute to sustainable development, innovation, and evidence-based decision-making. All submitted manuscripts undergo a rigorous double-anonymous peer-review process to ensure scientific quality, originality, ethical integrity, and academic excellence. JGRMS follows internationally recognized publishing standards and ethical guidelines and is committed to transparency, research integrity, and open scholarly communication. Scope JGRMS publishes original research articles, review articles, mini reviews, technical notes, case studies, survey papers, and interdisciplinary research in, but not limited to, the following areas: Engineering and Technology Artificial Intelligence and Machine Learning Data Science and Big Data Analytics Computer Science and Information Technology Cyber Security and Digital Forensics Cloud Computing and Edge Computing Internet of Things (IoT) Robotics and Intelligent Systems Software Engineering Computer Networks and Communication Blockchain Technology Embedded Systems Electrical, Electronics, Mechanical, Civil, Chemical, and Industrial Engineering Renewable Energy Technologies Smart Manufacturing and Industry 4.0 Natural and Applied Sciences Physics Chemistry Mathematics Statistics Biological Sciences Biotechnology Environmental Science Agricultural Sciences Earth and Environmental Studies Food Science and Technology Nanotechnology Materials Science Medical and Health Sciences Medicine Pharmacy and Pharmaceutical Sciences Nursing Public Health Clinical Research Biomedical Sciences Healthcare Informatics Medical Imaging Nutrition and Dietetics Allied Health Sciences Social Sciences and Humanities Education Psychology Sociology Economics Commerce Business Administration Management Finance Law Political Science Public Administration Media and Communication Studies Library and Information Science History Geography Philosophy Linguistics Literature Cultural Studies Emerging and Interdisciplinary Areas Smart Cities Sustainable Development Climate Change Digital Transformation Green Technologies Innovation Management Entrepreneurship Computational Social Science Human-Computer Interaction E-Governance Precision Agriculture Educational Technology Bioinformatics Computational Biology FinTech Artificial Intelligence in Healthcare Industry–Academia Collaborative Research Types of Manuscripts JGRMS publishes the following categories of manuscripts: Original Research Articles Review Articles Mini Review Articles Technical Notes Case Studies Survey Papers Short Communications Research Articles Based on Questionnaire Studies Special Issue Articles Invited Editorials (by invitation) Publication Standards JGRMS publishes only manuscripts that: Present original and unpublished research. Are not under consideration by another journal. Contribute significantly to existing knowledge. Follow internationally accepted ethical standards. Successfully complete a double-anonymous peer-review process. Comply with the journal's Publication Ethics, Plagiarism Policy, AI Policy, Conflict of Interest Policy, Data Availability Policy, and Copyright & Licensing Policy. The journal welcomes interdisciplinary research that bridges traditional academic boundaries and promotes collaboration among researchers from diverse fields. Open Access Policy JGRMS is a fully Open Access journal. All published articles are immediately and permanently available online without subscription or access charges. Articles are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) License, allowing unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Target Audience The journal serves: Researchers Scientists Engineers Healthcare Professionals Academicians Educators Industry Experts Policy Makers Research Scholars Postgraduate and Doctoral Students Government and Non-Government Organizations JGRMS welcomes high-quality submissions from authors worldwide and is committed to advancing multidisciplinary research through ethical, transparent, and internationally recognized scholarly publishing. Key Features Established in 2025 with an international editorial and reviewer network. Fully Open Access, providing unrestricted online access to all published content. Peer-Reviewed, ensuring the publication of high-quality scholarly research. Monthly Publication, enabling rapid dissemination of research findings. Interdisciplinary Focus, encouraging collaboration across diverse academic fields. Rapid Editorial and Peer Review Process with ethical publishing standards. Global Visibility, welcoming submissions from researchers worldwide. DOI Assignment for all published articles to ensure permanent identification and citation. Target Audience The journal serves researchers, faculty members, scientists, students, industry professionals, policymakers, and practitioners seeking current research, innovative methodologies, and interdisciplinary perspectives from around the world. Journal Particulars Particular Details Journal Title Journal of Global Research in Multidisciplinary Studies (JGRMS) Starting Year 2025 Publication Frequency Monthly Peer Review Double-Blind Peer Review Access Model Fully Open Access Publication Format Online Language English ISSN(Online) 3049-2963 ISSN(Print) Not Available DOI Prefix https://doi.org/10.5281/zenodo.14640835 Publisher Mr. Sandeep Gupta, Ward No. 5, Mathur Vaishy Colony, Ambah, Morena, Madhya Pradesh – 476111, India Chief Editor Mr. Sandeep Gupta Subject Areas Science and Technology, Engineering and Applied Sciences, Medicine and Health Sciences, Business and Economics, Social Sciences and Humanities, Environmental Studies, Arts and Literature Email editor@saanvipublications.com, editor.jgrms@saanvipublications.com Mobile +91-8305233223 Website https://saanvipublications.com/journals/index.php/jgrms/index Correspondence Address Ward No. 5, Mathur Vaishy Colony, Ambah, Morena, Madhya Pradesh – 476111, India
This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain the copyright of their work and grant the Journal of Global Research in Multidisciplinary Studies (JGRMS) the right of first publication. This license permits unrestricted use, distribution, adaptation, and reproduction in any medium or format, provided the original author(s), source, and publication are properly credited. Users may copy, redistribute, remix, transform, and build upon the published material for any purpose, including commercial use, in accordance with the terms of the CC BY 4.0 License.
U-Net based deep learning framework for automated lung nodule segmentation using CT images
Lung cancer is a leading cause of cancer-related fatalities globally. Early detection of lung nodules via CT scans is crucial for prompt diagnosis and treatment. This study presents a U-Net-based deep learning framework for automated lung nodule segmentation with the LIDC-IDRI dataset. The CT images and corresponding segmentation masks were preprocessed and used to train the proposed model. U-Net was adopted to identify the spatial features of lung nodules and to produce the masks at pixel level. The experimental results demonstrated learning performance with an accuracy of 99.44% and 99.46% during training and validation phases, respectively. The model yielded Dice Similarity Coefficient of 0.429 and localized the majority of nodule regions in CT images. Further visual comparison of the masks verified the validity of the proposed method. Thus, it can be concluded that the proposed system can aid in automatic localization of the lung nodules and in developing computer aided diagnosis systems.
Copyright (c) 2026 Neha Thakur (Author) https://creativecommons.org/licenses/by/4.0
Quantum Computing for cybersecurity and Intelligent Threat Detection: A Survey
Quantum Computing is the technology of the future that could revolutionize today's computing and security. Quantum computers able to run complex calculations faster than classical computers, thanks to the principles of quantum mechanics, such as superposition, entanglement and quantum parallelism. In this paper, authors give an extensive overview of quantum computing and its application in cybersecurity. It introduces basic concepts of quantum computing such as qubits, quantum gates, quantum circuits, and highlights of some of the important quantum algorithms like Shor's algorithm and Grover's algorithm. The research also covers the use of quantum computing in improving cybersecurity by leveraging post-quantum cryptography, quantum machine learning, network security, and quantum intrusion detection systems. Furthermore, the paper explores the key challenges and limitations of quantum technologies, such as scalability, fault tolerance, quantum noise, and security concerns. Additionally, a comprehensive literature survey of the latest developments and research works of quantum computing and quantum security are given. The results show the potential of quantum computing to enhance cyber security, but also highlight the challenges that will need to be addressed in the future, as quantum computers become increasingly common and powerful.
Copyright (c) 2026 Mr. Sachin Manekar (Author) https://creativecommons.org/licenses/by-nc/4.0
Deep Neural Network-Based Detection of Brain Tumors Using MRI Images
Brain tumors constitute the most dangerous type of neurological disorder, where delayed or incorrect diagnoses may significantly affect patient survival chances. Owing to the fast development of artificial intelligence, Machine learning (ML) and Deep Learning (DL) approaches along with the Magnetic Resonance Imaging (MRI) method proved to be efficient solutions for brain tumor detection and classification. This research paper presents the novel DNN-based method for precise detection of brain tumors based on MRI images of the BraTS 2020 dataset. In total, 2,892 MRI images were used for the training of the model, which included T1-weighted, T2-weighted, and FLAIR modalities. The preprocessing of the images included data cleaning, resizing, augmentation, one-hot encoding, and Z-score normalization of the images to increase their quality and model generalization. The information set was divided into 70:20:10 training, validation, and testing sets. The proposed DNN framework could learn discriminative tumor features and demonstrated better classification results than CNN, FCN, ResNet50, and InceptionNet models. As a result, the proposed DNN provided the following values: Accuracy (ACC) - 99.80%, Precision (PRE) - 99.85%, Recall (REC) - 99.84% and F1-Score (F1) - 99.81%. These findings Demonstrate the efficacy and dependability of the devised method, which holds tremendous potential as a trustworthy computer-aided diagnostic tool for brain tumour diagnosis.
Copyright (c) 2026 Dr. Jvalant kumar Kanaiyalal Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
Load Balancing in Edge-Cloud Ecosystems: A Survey of Hybrid Orchestration Models
Edge-cloud computing has emerged as a promising paradigm for supporting the growing demands of Internet of Things (IoT) applications, intelligent services, and data-intensive workloads. By integrating distributed edge resources with centralized cloud infrastructures, edge-cloud ecosystems enable low-latency processing, improved scalability, and efficient resource utilization. However, the dynamic and heterogeneous nature of these environments introduces significant challenges in workload distribution, resource allocation, and service orchestration. Consequently, load balancing and orchestration mechanisms have become essential for maintaining Quality of Service (QoS) and ensuring efficient system operation. This survey presents a comprehensive review of load-balancing techniques and hybrid orchestration models in edge-cloud ecosystems. A taxonomy of load-balancing approaches is discussed based on decision location, orchestration strategy, and system architecture. The study further examines recent advances in distributed computing frameworks, Software-Defined Networking (SDN), Network Function Virtualization (NFV), workload scheduling, collaborative resource management, and intelligent orchestration techniques. A comparative analysis of existing research is provided to identify key strengths, limitations, and emerging trends. The findings indicate that hybrid orchestration models, which combine centralized coordination with decentralized decision-making, offer significant advantages in terms of scalability, adaptability, resource utilization, and service performance. Additionally, critical challenges and future research directions are highlighted to support the development of efficient, secure, and intelligent edge-cloud ecosystems.
Copyright (c) 2026 Dr. Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by-nc/4.0
A Survey on Interpretable Machine Learning Techniques for Student Adaptability Prediction in Educational Systems
Education is an important part of growing knowledge, skills and adaptability in today's rapidly evolving society. The digital revolution has led to the widespread adoption of online learning platforms, smart tutoring systems, and data-driven learning environments in education, enhancing learning effectiveness and accessibility. This is because the difficulty of predicting students' adaptability has become an important research topic, helping to understand learning difficulties, improve learning outcomes and personalize learning. This survey explores how interpretable machine learning (IML) techniques can be used to predict student adaptability in the context of education. It explores machine learning and deep learning models such as Decision Trees, Random Forests, Support Vector Machines, XGBoost and Long Short-Term Memory (LSTM) networks for learning student behaviour, engagement and learning patterns. Furthermore, the paper explores the concept of Explainable Artificial Intelligence (XAI) methods (including SHAP analysis, LIME, feature importance analysis, and rule-based explanations) that contribute to making AI in education more transparent and trusted. In smart educational environments, the results indicate that applicable AI methods enhance the adaptive learning approaches, timely interventions, provide individual assistance to students, and provide clear analytics.
Copyright (c) 2026 Sushmita Gour , Kamlesh Raghuvanshi , Ram kumar Sahu (Author) https://creativecommons.org/licenses/by-nc/4.0
A Survey of Data Handling Techniques in NextGeneration Database Systems
The rapid expansion of digital technologies such as cloud computing, Internet of Things (IoT), artificial intelligence, and big-data applications has significantly increased the volume, variety, and velocity of data generated across modern systems. Traditional relational database management systems (RDBMS), while effective for structured and transactional data, often encounter limitations in handling large-scale, heterogeneous, and real-time data environments. Consequently, next-generation database systems, including NoSQL, NewSQL, graph, distributed, and cloud-native databases, have emerged to address these challenges through scalable, flexible, and high-performance architectures. This review paper surveys major data handling techniques adopted in next-generation database systems, emphasizing data storage models, organization strategies, query processing, optimization mechanisms, and partitioning techniques. The study further discusses advanced challenges associated with scalability, performance, security, privacy, and governance in distributed and cloud-based environments. In addition, emerging technologies such as artificial intelligence-driven database management, serverless databases, edge computing, blockchain integration, and multi cloud optimization are examined as transformative developments shaping future database ecosystems. A comprehensive literature review summarizes recent contributions, identifies limitations, and highlights research opportunities. The findings indicate that intelligent and adaptive data handling techniques are essential for ensuring efficient, secure, and scalable database management in modern data-intensive applications.
Copyright (c) 2026 Dr. Parth Gautam (Author) https://creativecommons.org/licenses/by-nc/4.0
A Predictive Model for Fraud Detection in Digital Payment Transactions using ML Algorithms
The rapid evolution of online payment technologies has greatly simplified access to capital. But instead, the possibility of fraudulent transactions is on the rise. Fraud of a more complex kind may occasionally go undetected by conventional rule-based systems. Combining LightGBM and Random Forest (RF), two machine learning approaches, this study presents a robust fraud detection tool to combat fraudulent financial transactions. The method consists of applying data preprocessing methods which include handling missing data, deleting duplicate data, data normalization, applying one hot encoder method, and applying data balancing using SMOTE technique. Moreover, exploratory data analysis includes using correlation matrix and analyzing transactions in order to discover any fraud patterns. In addition, dividing the processed dataset into a test and train dataset for implementing the model. The LightGBM model surpasses the other strategies with a 98.80% accuracy (ACC), 99.37% precision (PRE), 99.37% recall (REC), 98.80% F1-Score (F1), and an AUC-ROC of 0.99%.
Copyright (c) 2026 Dr. Neetu Sikarwar (Author) https://creativecommons.org/licenses/by/4.0/
Technology Acceptance Model (TAM) and Consumer Behaviour in Digital Product Adoption
The growth of technology has reached a very fast pace resulting in a shift in consumer behavior and upsurge in the number of people who use products and services that come in digital form like banking on the internet, shopping on the internet, social networks, Artificial intelligence, and electronic payments. This research paper will focus on the role of Technology Acceptance Model (TAM) in explaining consumer behavior towards digital product usage. The research pays attention to how consumer behavior is affected by perceived usefulness (PU) and perceived ease of use (PEOU). Moreover, it reviews the utilization of the TAM within different digital contexts and integration with behavioral theories like TPB, IDT, and UTAUT for good understanding the process of adopting digital goods and services. In addition, the limitations of the TAM are critically analyzed. These limitations include the oversimplification of consumer behavior, lack of consideration of emotional and social aspects of consumer behavior, rapid developments in technology, and cultural differences. According to this paper, modern consumers are influenced by factors other than PU and PEOU.
Copyright (c) 2026 Pabbineedi, Dr. Aravindh Balan (Author) https://creativecommons.org/licenses/by-nc/4.0
A Detailed Survey of Deep Learning Frameworks for Early Prediction and Classification of Tomato Leaf Diseases
Tomato leaf diseases caused by bacterial, viral, and fungal pathogens constitute a significant global barrier to agricultural production and tomato crop quality. Early and correct diagnosis/detection are crucial to limit economic losses and sustainable farming. Using leaf images for identifying diseases of the plant has proved to be a good solution, and recently with the advent of Artificial Intelligence (AI) and Deep Learning (DL) it has been possible to find a more efficient solution. This paper offers a thorough evaluation of DL frameworks for the early detection and classification of tomato leaf disease. examination of several methods, including hybrid DL models, Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), transfer learning models, and their accuracy and performance. The study also explores and discusses commonly used agricultural datasets, preprocessing strategies, feature extraction techniques, and performance assessment measures (such as accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), and ROC-AUC). Furthermore, recent advancements in Explainable AI (XAI), Edge AI and small DL models for Smart Agriculture applications were highlighted. Lastly, challenges, limitations and future directions of real-time tomato disease detection systems are discussed.
Copyright (c) 2026 Pankaj Modi, Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Measuring the Effectiveness of AI-Powered Training in Luxury Automotive Retail Sales Networks
The vehicle valuation process in the dynamic secondary automotive market is a challenging task because the vehicle value depends on condition, mileage, and the constantly changing market in a non-linear relationship. The objective of this study is to create a very accurate, scalable predictive model of vehicle selling prices that combines institutional market benchmarks with vehicle physical characteristics. For this purpose, a comprehensive end-to-end pipeline is designed based on automatic column transformations that convert high-dimensional categorical features into continuous numerical features. In this study, three advanced ensemble learning models XGBoost (XGB) Random Forest (RF) and Gradient Boosting (GB), were considered and compared to the traditional models. The results show that ensemble architectures outperform individual architectures at predicting accuracy. The Gradient Boosting model had the best empirical performance with a score of 0.976, MSE of 2161666.55 and a RMSE of 1,470.26, followed closely by Random Forest and XGBoost with an score of 0.975. The proposed models were rigorously validated against existing models such as Support Vector Regression, LSTM, KNN, Linear Regression, and Prophet, where the results presented that the proposed models significantly outperform the traditional models. Results from the feature importance mapping confirmed that the Manheim Market Report (MMR) remains most useful as a predictive indicator, and the results from the residual diagnostics showed performance boundaries in the premium asset segments. The overall contribution of this research is a validated data-driven methodology that improves the accuracy of valuing assets in real-time, and offers a solid framework for structured data prediction in the secondary markets.
Copyright (c) 2026 Mohit Kohli (Author) https://creativecommons.org/licenses/by-nc/4.0
The Future of Project Leadership: What AI Means for PMOs in 2026 and Beyond
With the advent of artificial intelligence (AI), PMOs have been experiencing rapid transformation in their functions and importance as well as their structure in contemporary organizations. Being mainly concerned with administrative coordination, control, documentation, and reporting, PMOs have started changing into smart governance systems through the use of technologies such as machine learning, generative AI, predictive analytics, natural language processing, and robotic process automation. The current research is dedicated to exploring the future of project management leadership and will analyze the evolution of PMO functions, governance structures, decision-making approaches, and leaders' competencies in the new concept of PMO 4.0. An exploratory approach with a focus on narrative literature analysis has been used as the main methodology. The research results confirm that AI improves project planning, prediction of possible risks, optimal allocation of resources, portfolio management, stakeholder communication, and decision-making in strategic processes, thus allowing PMOs to transition from managerial functions to value creation activities. It should also be noted that future project managers will need such skills as data literacy, strategic thinking, ethical management, emotional intelligence, and effective human-AI collaboration management. Among the potential obstacles in AI application for projects, there are data-related problems, algorithm biases, cybersecurity threats, and employees' resistance to automation. Finally, the paper shows that the future of project management will be determined by collaborative intelligence that combines the strengths of artificial and human intelligence and allows firms to become more agile, innovative, resilient, and competitively successful.
Copyright (c) 2026 Matthew M. Fernandes (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning-Based Predictive Maintenance Classification for Industrial Equipment Failure Detection
Industrial equipment optimization is crucial in modern industries for boosting operating efficiency, decreasing downtime, and minimizing maintenance costs. Predictive maintenance helps achieve these goals by predicting equipment failures and scheduling repairs using machine learning. For the purpose of detecting machine failures, this study employs machine learning-based predictive maintenance using operational data from a subset of industrial equipment included in the AI4I 2020 Predictive Maintenance dataset. Data is cleaned, outliers are detected and removed, One-Hot Encoding is applied, and class balancing is achieved utilizing SMOTE techniques to improve data quality, generate a more balanced class distribution, and improve the performance of the predictive model. Classification of industrial equipment failures was investigated using a variety of models, including Decision Tree (DT), Logistic Regression (LR), CNN, K-Nearest Neighbors (KNN), Random Forest and XGBoost. The testing findings showed that out of all the models tested, XGBoost achieved 98.4% accuracy and Random Forest 98.3%. Overall, the results showed the high accuracy of predictive maintenance and detection of industrial equipment failures that can be achieved using advanced ensemble and deep learning methods.
Copyright (c) 2026 Aravindh Balan (Author) https://creativecommons.org/licenses/by-nc/4.0
An Empirical Study on Business Continuity Governance and Organizational Effectiveness in Contemporary Enterprises
Modern organizations function in an unstable environment where technological disruptions, cyber-attacks, natural disasters, political instability, pandemics, among others, represent operational challenges that have the potential to impair an organization’s performance and viability. As a consequence, Business Continuity Governance (BCG) has become an essential strategy to improve organizational resilience, adaptability, and efficiency. This paper seeks to explore the link between business continuity governance and organizational effectiveness via a review of scholarly literature, theoretical approaches, and current organizational practices in relation to the issue under consideration. An exploratory research design has been employed in analyzing academic papers, reports, and standards regarding business continuity management, organizational resilience, risk management, and sustainability. This review critically assesses key theories such as Enterprise Risk Management, Organizational Resilience Theory, Crisis Management Theory, Systems Theory, and Resource-Based View, and analyzes ISO 22301’s contribution in improving continuity governance through risk assessment, crisis management, recovery, and continuous improvements. From the analysis of the findings, it is clear that organizations that implement mature Business Continuity Governance show high levels of resilience, faster recovery in case of disruptions, higher operational efficiencies, improved stakeholder confidence, greater technological readiness, and competitive advantages. Additionally, from the literature review, it is apparent that some of the challenges associated with implementing Business Continuity Governance include fragmented governance, lack of engagement by leaders, old technological systems, and the lack of expertise in Business Continuity Management. Overall, it can be noted that Business Continuity Governance has developed from simply being used for disaster recovery purposes to being a strategic tool for organizational success.
Copyright (c) 2026 Sandile Khoza (Author) https://creativecommons.org/licenses/by-nc/4.0
Security, Privacy, and Trust in Cloud Computing: An Integrated Survey of Techniques
Cloud computing has emerged as a transformative model for delivering IT services, offering scalable, flexible, and cost-efficient access to computing resources over the internet. It is structured around three primary service models—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)—which delineate varying levels of user control and provider responsibility. Deployment models, including public, private, hybrid, and community clouds, further define ownership, access, and operational scope. Key stakeholders in the cloud ecosystem include service providers, consumers, and intermediaries such as brokers. However, the adoption of cloud technologies introduces significant challenges, particularly in areas of security, privacy, and trust. Threats such as data breaches, insecure APIs, insider risks, and multi-tenancy vulnerabilities compromise cloud integrity, while issues like data ownership, cross-border data flow, and regulatory compliance raise privacy concerns. Trust in cloud environments hinges on transparency, robust service-level agreements (SLAs), auditability, and third-party certifications like ISO 27001 and SOC 2. Addressing these challenges is essential for ensuring secure, privacy-compliant, and trustworthy cloud services that can support mission-critical applications across sectors.
Copyright (c) 2026 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Reimagining Maintainability Machine Learning Techniques For Security Requirement Optimization
Software maintainability has gained recent popularity in the sphere of software engineering throughout the past few years in an effort to determine the quality of software. Hence, it is important to predict this maintainability in time and with accuracy for the effective administration of software during the maintenance stage. In turn, it is causing the developer to focus more on those modules that are expensive to maintain. software maintainability prediction (SMP) machine learning model suggested in this paper is informed by the Students project requirements software requirements dataset. This study is a description of the use of machine learning high-end methods of classification namely the Random Forest, AdaBoost and Voting Classifier which significantly contribute to the evaluation of maintainability in terms of software security requirements. To make a comparative analysis, these models have significant accuracy improvement with the highest accuracy of 87.85 in binary classification and 99.37 in multi-class classification compared to 79.43 and 85.08 of the baseline models respectively. The results show that more ML methods can be used to enhance the efforts to measure the maintainability of the software and that these methods can be used to meet the requirements of software security.
Copyright (c) 2026 Gopal Verma, Atul Kumar Mishra (Author) https://creativecommons.org/licenses/by-nc/4.0
A Robust Cross-Platform Deepfake DetectionFramework Using Multi-Modal Deep Learning and Explainable AI
The Deepfake technology presents unprecedented challenges to the authenticity of digital media. The paper gives a presentation of a top-down detection systems that combine lightweight CNN Transformer hybrids, multi-modal combination of visual, audio and biological evidence, and universal forensic clues. Our systematic review of 21 state-of-the-art methods reveals key barriers to deployment, and offers solutions that achieve 96.8% accuracy with 88.5% cross-dataset generalization – a 25–30% improvement over state-of-the-art methods in existence today. The framework includes explainable AI elements that produce transparent decisions that can be used in forensic applications, with inference latency of 200 ms to produce transparent decisions suitable to be used in forensic applications.
Copyright (c) 2026 Sweta Sharma, Pradeep Chouksey, Aastha Rathi, Parveen Sadotra, Mayank Chopra (Author) https://creativecommons.org/licenses/by-nc/4.0
Deep Learning Approaches for Apple Leaf DiseaseDetection and Classification: A Comprehensive Review
foliar diseases pose a major threat to apple cultivation, severely affecting crop yield and quality. Conventional disease detection relies on manual expert examination—a slow, subjective, and error-prone process. Recent advances in deep learning, especially Convolutional Neural Networks (CNNs), have demonstrated remarkable capability to automate plant disease detection with high precision. This review comprehensively surveys state-of-the-art deep learning models for apple leaf disease detection, covering research published between 2021 and 2025. We compare architectures such as Dense Net, Efficient Net, Mobile Net, Inception-V3, and Vision Transformers with respect to performance metrics, limitations, and real-world applicability. Current models achieve 90–98.32% classification accuracy, yet significant challenges remain in dataset diversity, field generalization, early-stage detection, and edge deployment. The review provides actionable directions for future research in precision agriculture.
Copyright (c) 2026 Reena kumari, Pradeep Chousky, Praveen Sadotra, Mayank Chopra (Author) https://creativecommons.org/licenses/by-nc/4.0
Shifting Hegemonies: Regional Power Dynamics in Post-Panipat India (1761-1800)
The 1761 Third Battle of Panipat represented a watershed moment in South Asian political history, precipitating the dissolution of Maratha hegemony and generating a political vacuum that fundamentally reshaped regional power structures. This research offers a comparative examination of key political actors, the British East India Company, Mysore, Hyderabad, Bengal, Awadh, and the Sikh Confederacy, analysing their strategic adaptations to the transformed post-Panipat landscape. Utilising Persian and English historical records, administrative archives, and contemporary historiography, the study evaluates critical parameters including territorial aggrandizement, military modernization, revenue administration, diplomatic engagement, and institutional resilience. The analysis reveals disparate trajectories: while Mysore and the Sikh polities achieved territorial gains, they failed to establish enduring state structures; Awadh and Hyderabad maintained regional significance but progressively fell under British suzerainty; Bengal transitioned to Company governance following the Diwani grant of 1765. The British East India Company's ascendancy stemmed from its sophisticated military apparatus and efficient fiscal extraction mechanisms. Ultimately, this study establishes that the aftermath of Panipat catalyzed political disintegration across the subcontinent, thereby enabling the systematic expansion of British colonial authority in South Asia.
Copyright (c) 2026 Krishan Kumar Joon, Dr Pradeep Kumar Kesharwani (Author) https://creativecommons.org/licenses/by-nc/4.0
Hybrid CNN–Vision Transformer Architecture forAccurate Liver Cancer Diagnosis from MedicalImaging
Detecting liver cancer early remains challenging because medical images can vary widely between patients, and differences in scan contrast are often subtle. This study describes a hybrid model that combines a CNN with a Vision Transformer, aiming to capture both fine, local image details and broader contextual information. In this setup, the CNN is used to focus on nearby visual signals such as edges and textures, while the transformer analyzes the full image to learn longer-range relationships between different regions. The method is evaluated on public datasets, including LiTS and TCGA-LIHC, with consistent preprocessing applied across all data. The reported accuracy is 94.8%, which is higher than the results from models using only a CNN or only a transformer. These findings indicate that leveraging both local and global features may lead to better performance in liver cancer detection
Copyright (c) 2026 Satyendra Sharma, Pradeep Laxkar (Author) https://creativecommons.org/licenses/by-nc/4.0
Culture and the Fragmentation of Women’s Agency:Evidence from Tribal and Non-Tribal Communitiesin Tripura
India’s tribes have stood out by virtue of according distinctly higher status and agency to women, manifested in greater personal freedom, mobility, and lack of division of labor along gender lines. However, several scholars have pointed out an ongoing process of socio-cultural adaptation among tribes. A specific dimension of these changes has been the adoption of cultural practices of the dominant non-tribal Hindu community, described by what has been called ‘Sanskritization’/‘Hinduisation’ of tribes in literature. This has arguably caused diminution of women’s agency in both the productive and reproductive domains. In this context, drawing on a sample of women from tribal (Reang) and non-tribal(Bengali-speaking) communities inhabiting Tripura, this paper attempts to answer two questions (a) If and to what extent is sociocultural assimilation a reality among the studied tribe? (b) How strongly does sociocultural group belongingness, in fact, impact women’s decision-making autonomy in the productive and reproductive spheres? In the absence of longitudinal data, the answer to the aforesaid questions has been sought through comparative study of the Reangs and Bengalis. Results from bivariate and multivariate analyses reveal clear signs of cultural assimilation among Reangs. Culture as embodied in group belongingness, has a significant role in shaping women’s agency in both the productive and reproductive dimensions While Reang women have more agency vis-à-vis their nontribal counterparts in the nonreproductive sphere, the same cannot be said for reproductive agency. One of the interesting findings of the paper is the dissociation between the reproductive and non-reproductive agency dimensions, which inter alia calls into question existing notions of high agency among tribal women in India. One of the main policy implications that follows is that, given the importance of sociocultural groups in determining women's agency, policy interventions targeting specific agency domains seem inevitable to further women’s economic and social standing.
Copyright (c) 2026 Anindita Sinha (Author) https://creativecommons.org/licenses/by-nc/4.0
AI-Powered Industrial Robots in Smart Factories: A Survey of Applications Challenges and Future Trends
The integration of Artificial Intelligence (AI) with industrial robotics is driving the evolution of smart factories, creating environments that are intelligent, adaptive, and highly efficient. By combining machine learning, computer vision, and real-time decision-making with advanced robotics, industries are achieving greater flexibility, precision, and productivity. Smart factories leverage enabling technologies such as Industrial Internet of Things (IIoT), cloud/edge computing, and collaborative robots to enhance workflow optimization, predictive maintenance, and quality assurance. This survey explores the applications of AI-powered industrial robots, including autonomous material handling, adaptive manufacturing, defect detection, and micro-manufacturing, which contribute significantly to cost efficiency and operational reliability. It further highlights the challenges hindering adoption, such as high implementation costs, integration with legacy systems, safety concerns, and the requirement for skilled expertise. The paper also reviews existing literature to provide insights into technological advances, industrial impact, and global adoption trends. Findings emphasize that AI-powered robotics not only enhances productivity and sustainability but also paves the way for Industry 4.0 transformation. Future directions focus on interdisciplinary collaboration, ethical AI deployment, and advanced cognitive robotic systems for sustainable and resilient manufacturing ecosystems.
Copyright (c) 2025 Mr. Kapil Ahir (Author) https://creativecommons.org/licenses/by-nc/4.0
Auto-Scaling and Load Balancing Strategies inKubernetes For High-Availability CloudApplications
In cloud computing, virtual infrastructure based on virtual machines has been widely used to support various businesses. Kubernetes is one of the most iconic of these systems. Kubernetes, K8s in short, is used for managing containers and it is most widely used. This study examines Kubernetes as a foundational edge orchestration platform within modern cloud–edge computing environments. It highlights Kubernetes’ role in enabling automated deployment, horizontal scaling, and efficient resource utilisation across heterogeneous infrastructures, including both x86 and emerging ARM-based platforms. The work classifies Kubernetes-based edge orchestration into three categories: platform-based solutions that extend Kubernetes without modification, cloud–edge collaborative architectures that centralize orchestration while offloading execution to the edge, and customized edge-specific solutions that adapt Kubernetes for constrained fog computing scenarios. Core distributed service mechanisms are discussed in terms of application-level versus platform-provided service discovery models. The study further analyzes Kubernetes resource management components, including scheduling, admission control, auto-scaling, load balancing, and health monitoring. Autoscaling mechanisms—including HPA, VPA, and Cluster Autoscaler—are presented alongside resource and custom metrics pipelines powered by cAdvisor, Metrics Server, and Prometheus. Finally, load balancing strategies are evaluated, including in-cluster mechanisms (e.g., kube-proxy), external load balancers (e.g., cloud or MetalLB), and service-mesh-based intelligent routing (e.g., Istio, Linkerd). Overall, the study emphasizes Kubernetes’ flexibility and maturity in orchestrating distributed workloads across cloud–edge systems.
Copyright (c) 2026 Dr. Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
Survey on Integration of IoT with Artificial Intelligence (AI) Technologies for Smart Manufacturing Operations
The IoT and AI are assisting in the development of smart, interconnected, and flexible industrial systems, which in turn are facilitating the evolution of manufacturing. Through its network of interconnected sensors and devices, the Internet of Things (IoT) facilitates the automation of various stages of the manufacturing process. AI adds value by providing detailed analysis, making predictions, and helping with decisions that make workflows smoother, cut down on stops in production, and improve product quality. Together, these technologies form the base of Industry 4.0, which supports eco-friendly practices, better use of resources, and gives companies an edge in the global market. Recent studies show that combining IoT data with AI insights is used in areas like predicting equipment failures, making processes more efficient, managing energy use, building reliable supply chains, and using smart robots. All these improvements help manufacturers be more adaptable, lower the chances of problems, and make products that meet customer needs. This overview shows how combining IoT and AI is making manufacturing more self-sufficient, driven by data, and stronger, which greatly helps in the ongoing development of modern industrial practices.
Copyright (c) 2026 Dr. Prithviraj Singh Rathore (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning-Based Default Loan Prediction for Financial Risk Assessment in Digital Lending
In recent decades, the global financial system has undergone tremendous change. While capital markets play a crucial role in some situations and traditional banks and financial institutions continue to be the main source of funding for businesses and people in the majority of nations, new digital lending platforms have lately emerged. In this paper, the author introduces a viable machine learning-based approach to the estimation of financial risk in online lending on the Lending Club dataset. The suggested solution starts with an extensive data collection and exploratory analysis, followed by intensive preprocessing of data that includes outlier treatment, feature engineering, one-hot encoding, and normalization, as well as the class imbalance mitigation method of SMOTE. A systematic hyperparameter optimization is applied to creating an optimized Extreme Gradient Boosting (XGBoost) model that is used to ensure that nonlinear relationships between borrower and loan traits are learned. There are various performance measures that are used to evaluate the model, some of them include accuracy, precision, recall, F1-score, ROC curve, and confusion matrix analysis. The experimental outcomes are highly predictive with an accuracy of 97.70, precision of 94.78, recall of 93.30 and a near perfection of the AUC of 0.9991. The fact that the proposed model is generalizable compared to the traditional machine learning models also justifies the superiority of the given approach and shows that it has power, can be scaled, and placed in the context of applying it into practice to evaluate financial risks in the digital lending model.
Copyright (c) 2026 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
Engineering Strategies for High-Availability Mission-Critical Facilities: A Survey
Mission-Critical Facilities in Diverse Domains investigates the key features, engineering requirements, as well as design provisions that allows continuous operation in high-consequence environments. The paper identifies that mission-critical facilities are becoming more important in various sectors, including data infrastructure, healthcare, defense, and manufacturing, and that they are sensitive to reliability, security, and resilience. It scans classification structures in terms of operational urgency and defines performance expectations which stimulate redundancy, real-time monitoring and disaster preparedness. Contributions of mechanical engineering are examined in reference to the redundancy strategy (including N+1, 2N strategy) and the task of adaptive controls in alleviating dynamic uncertainty. Structural and civil engagements are analyzed using modular construction and structural optimization practices that increase performance in terms of efficiency, safety and lifecycle. The research provides a multidisciplinary basis to the understanding and enhancement of mission-critical systems and balances design practices with current requirements of fault tolerance and always-on operation.
Leveraging Artificial Intelligence Algorithms for Retirement Income Optimization in Financial Planning
Responsibility for one's own financial future is required in people's financialized everyday lives. However, as a larger proportion of the population ages, more people are concerned about their financial stability due to factors such as fluctuating retirement and pension systems, uncertain government funding, and market and other crises. The present study addresses the urgent issue of low uptake of pension schemes by developing and testing high-performance predictive models based on data from FinAccess. To address the extreme imbalance among the classes, the more extensive AdaSyn resampling method was used. A sophisticated combination of 22 sociodemographic predictors was specified to train and optimize Multi-Layer Perceptron (MLP) and Decision Tree (DT) models systematically. The obtained results show outstanding predictive power, as the MLP model has an accuracy of 95.74%, precision of 96.26%, recall of 95.60%, F1-score of 95.76%, and a near-perfect AUC of 0.9968, while the DT model yielded closely competitive metrics of 95.25% accuracy, 95.65% precision, 95.03% recall, 95.26% F1-score, and a 0.9950 AUC. A comparative study proves that both of the proposed models are far much better than modern standards, such as RNN-LSTM, LightGBM, and Random Forest. The study develops a new analytical pipeline that offers practical, quantitative information of creating specific financial inclusion initiatives to enhance security in changing retirement environments.
Copyright (c) 2025 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
Influence Of Die Geometry And Friction On Springback During Cold Drawing Of Tubes: An Fea Investigation
The cold drawing of seamless tubes can cause unwanted geometric deviations in the form of undesired springbuck, where die geometry, frictional conditions, and material properties are the main factors of influence. The AISI 304 stainless steel tubes were simulated using both experimental and finite element (FE) models in this paper in order to discuss how die semi-angle, bearing length, and friction coefficient influence the springback of the tubes. The numerical model has been constructed based on ABAQUS/ Explicit and checked against laboratory-scale experiments of tube drawing. The Coulomb law was used to model the frictional contact with a coefficient between 0.05 and 0.15. Findings have shown that the smaller die angles and reduced friction coefficients are useful in reducing the elastic recovery and dimensional deviations. The springback values predicted by the FEA were close to experimental values, with the highest deviation of 6.8, which proved that the model was accurate. These results can be used in the future to design processes and the selection of dimension control parameters in the production of seamless tubes.
Copyright (c) 2026 Mr. Jayesh Nagraj Chaudhari, Dr. Kamal Kumar Ojha, Dr. Kasi Raja Rao (Author) https://creativecommons.org/licenses/by-nc/4.0
Addressing The Idiom Challenge in Machine Translation: A Review Focused on Low-Resource Languages
Machine translation (MT) has made significant progress for high-resource languages, yet idiomatic translation remains a persistent challenge, especially for low-resource languages like Punjabi. Idioms are non-compositional with respect to cultural values, which makes literal translations insufficient. This paper presents a systematic review of idiom translation in pairs of low-resource-to-high-resource languages, focusing on Punjabi-English as a case study. We analyze key challenges—including dataset limitations, figurative-literal ambiguity, structural complexity, and evaluation limitations—and examine existing approaches, including rule-based, statistical, neural, and large language model (LLM)-based methods. We identify gaps in idiom-specific datasets, evaluation frameworks, and multilingual transfer techniques. Finally, we provide some guidance for future research, highlighting hybrid models, community-driven datasets, multimodal translation, and idiom-aware evaluation metrics. This review aims to guide the development of more accurate and culturally aware MT systems for low-resource languages.
Copyright (c) 2025 Manjot Kaur, Jasvir Kaur, Jasmin Kaur Gahlot, Prof. Palak Sood (Author) https://creativecommons.org/licenses/by-nc/4.0
Facial Emotion Detection in Low Resolution Image: Review and Challenges
Face emotion is a form of non-verbal communication extensively used by human beings. Detecting emotion to ascertain a human's mental state is a vital research area. Emotion detection for images with low resolution is an area where research is going as practical real-world images will be mostly low resolution. Real-world applications like surveillance, forensic and drone imagery capture low-resolution images due to camera resolution, distance of face, and motion of the camera as well as face and may contain noise. Most of time, researchers consider image less than 32* 32 pixels as low-resolution image as there is no fixed definition for categorizing low-resolution images. Human face emotion detection performance depends on factors such as lighting conditions, facial occlusion, and facial movements. The loss of resolution will reduce the image's vital facial details. Performance on facial emotion recognition is improving with newer deep learning models, but model accuracy remains a major issue. This paper will elaborate methods and algorithms used in face emotion detection in low resolution conditions. The paper will summarize different datasets, methodology and performance parameters and challenges.
Machine learning (ML) and Deep Learning (DL) Approaches
Copyright (c) 2025 A.R. Landge, Manish Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence as a Catalyst for Sustainable Development: Addressing Environmental, Healthcare, and Urban Infrastructure Challenges
The escalating environmental degradation, healthcare disparities, and urban infrastructure challenges demand transformative solutions that transcend conventional approaches. This paper investigates the role of artificial intelligence as a catalyst for sustainable development across three critical domains: environmental conservation, healthcare delivery, and urban infrastructure management. Through a comprehensive analysis of recent implementations and empirical evidence, we demonstrate how machine learning algorithms, neural networks, and intelligent systems are reshaping sustainability practices. The study examines specific case studies in which AI has achieved measurable improvements in carbon footprint reduction, disease outbreak management, and smart city operations. We present quantitative evidence showing that AI-enhanced systems can reduce energy consumption by 30-50%, improve diagnostic accuracy by up to 95%, and decrease urban traffic congestion by approximately 60%. The paper also addresses implementation challenges, including algorithmic transparency, data governance, and the digital divide. Our findings suggest that successful AI integration requires collaborative frameworks involving policymakers, technologists, and community stakeholders. This research contributes to the growing body of knowledge on AI-driven sustainability by providing a holistic framework that bridges technological innovation with practical implementation strategies.
Copyright (c) 2026 Srinivas Reddy Kosna (Author) https://creativecommons.org/licenses/by-nc/4.0