Journal of Global Research in Multidisciplinary Studies(JGRMS)
type: channel
description: 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 Bio…
publisher: Saanvi Publications
language: en
publication name: Journal of Global Research in Multidisciplinary Studies(JGRMS)
issn: 3049-2963
copyright: 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.
items: Software Development Methodologies Evolutionary Trends from Waterfall to DevOps
items: Review of Multi-Cloud Resource Allocation Optimization and Security Assurance Techniques Systems
items: Artificial Intelligence-Assisted Pharmaceutical Manufacturing Optimization Using Predictive Machine Learning Techniques
items: Autonomous Vehicle Safety Prediction Using Machine Learning Techniques
items: A Survey of Enterprise Supply Chain Management with Digital Transformation Artificial Intelligence Technologies Applications
items: A Survey of Tools and Techniques of Big Data Management in Cloud Environments
items: U-Net based deep learning framework for automated lung nodule segmentation using CT images
items: Quantum Computing for cybersecurity and Intelligent Threat Detection: A Survey
items: Deep Neural Network-Based Detection of Brain Tumors Using MRI Images
items: Load Balancing in Edge-Cloud Ecosystems: A Survey of Hybrid Orchestration Models
items: A Survey on Interpretable Machine Learning Techniques for Student Adaptability Prediction in Educational Systems
items: A Survey of Data Handling Techniques in NextGeneration Database Systems
items: A Predictive Model for Fraud Detection in Digital Payment Transactions using ML Algorithms
items: Technology Acceptance Model (TAM) and Consumer Behaviour in Digital Product Adoption
items: A Detailed Survey of Deep Learning Frameworks for Early Prediction and Classification of Tomato Leaf Diseases
items: Measuring the Effectiveness of AI-Powered Training in Luxury Automotive Retail Sales Networks
items: The Future of Project Leadership: What AI Means for PMOs in 2026 and Beyond
items: Machine Learning-Based Predictive Maintenance Classification for Industrial Equipment Failure Detection
items: An Empirical Study on Business Continuity Governance and Organizational Effectiveness in Contemporary Enterprises
items: Security, Privacy, and Trust in Cloud Computing: An Integrated Survey of Techniques
items: Reimagining Maintainability Machine Learning Techniques For Security Requirement Optimization
items: A Robust Cross-Platform Deepfake DetectionFramework Using Multi-Modal Deep Learning and Explainable AI
items: Deep Learning Approaches for Apple Leaf DiseaseDetection and Classification: A Comprehensive Review
items: Shifting Hegemonies: Regional Power Dynamics in Post-Panipat India (1761-1800)
items: Hybrid CNN–Vision Transformer Architecture forAccurate Liver Cancer Diagnosis from MedicalImaging
items: Culture and the Fragmentation of Women’s Agency:Evidence from Tribal and Non-Tribal Communitiesin Tripura
items: AI-Powered Industrial Robots in Smart Factories: A Survey of Applications Challenges and Future Trends
items: Auto-Scaling and Load Balancing Strategies inKubernetes For High-Availability CloudApplications
items: Survey on Integration of IoT with Artificial Intelligence (AI) Technologies for Smart Manufacturing Operations
items: Machine Learning-Based Default Loan Prediction for Financial Risk Assessment in Digital Lending

Software Development Methodologies Evolutionary Trends from Waterfall to DevOps
type: item
description: Agile and DevOps are currently the most popular software development techniques. Software methodologies started with the application of the Waterfall model and other models of Software development. It provides a general overview of the approaches and their integration throughout the Software Development Life Cycle (SDLC). SDLC phases are described and different SDLC methodologies are discussed with respect to their impact on motivation, productivity and quality. A comparison of the traditional Waterfall and newer iterative/incremental/agile is identified, as are the reasons behind this change, such as customer participation, timely feedback, and quality improvement. The following issues are common to all methodologies: architecture limitations, scalability and organizational restrictions. It was identified that the industry tends to follow a hybrid Agile-DevOps practice with more emphasis on automation, continuous integration and collaboration to achieve quick and competitive software environments.
subject: Review Article
  topic: section
subject: Software Development Life Cycle (SDLC)
  topic: keywords
subject: Software Development Methodologies
  topic: keywords
subject: Waterfall Model
  topic: keywords
subject: Agile Software Development
  topic: keywords
subject: DevOps
  topic: keywords
subject: Iterative and Incremental Models
  topic: keywords
creator: Mr. Raman Kumar
rights: Copyright (c) 2026 Mr. Raman Kumar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-08-12
publication date: 2026-08-12
starting page: 1
ending page: 8
doi: 10.67805/jgrms.v2i8.150

Review of Multi-Cloud Resource Allocation Optimization and Security Assurance Techniques Systems
type: item
description: Multi-cloud computing is becoming a prominent paradigm to improve scalability, flexibility, reliability and costeffectiveness by leveraging services from multiple cloud providers. But distributed resource management with strong security is a big challenge in multi-cloud scenarios, which are heterogeneous and dynamic. This review paper provides an all inclusive overview on various multi-cloud architectures, deployment models,resource allocation techniques, optimization methods, and security assurance mechanisms. It covers the major resource allocation strategies such as provisioning, scheduling, load balancing, resource scaling and intelligent optimization through machine learning and metaheuristicalgorithms to optimize resource utilization and Quality of Service (QoS). Additionally, the article delves into significant security methods for protecting decentralized cloud systems, including authentication, authorization, encryption, intrusion detection, trust management, and zero-trust designs. Also, through the comparison of the most recent literature, the current research trends, challenges and limitations for optimizing resources while keeping security in mind are pointed out. According to the review, combining AI-powered optimisation with sophisticated security frameworks has the potential to enhance the performance, resilience and reliability of multi-cloud environments. Last but not least, the paper outlines future research avenues for explainable AI, federated learning, blockchain-based trust management, energy-efficient resource allocation, and autonomous cloud orchestration to enable secure, scalable, and sustainable next-generation multi cloud computing environments.
subject: Review Article
  topic: section
subject: Multi-Cloud Computing
  topic: keywords
subject: Resource Management
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Security Assurance
  topic: keywords
subject: Cloud architecture
  topic: keywords
subject: Resource Scheduling
  topic: keywords
creator: Dr. Amit Jain
rights: Copyright (c) 2026 Dr. Amit Jain (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-08-12
publication date: 2026-08-12
starting page: 9
ending page: 17
doi: 10.67805/jgrms.v2i8.149

Artificial Intelligence-Assisted Pharmaceutical Manufacturing Optimization Using Predictive Machine Learning Techniques
type: item
description: The pharmaceutical manufacturing sector is seeing growing difficulties in streamlining operations, preserving product quality, and meeting stringent regulatory standards. Now more than ever, AI and ML are poised to revolutionize pharmaceutical production by evaluating data and generating predictions to optimize various elements. Utilizing a combination of AI-driven approaches, massive volumes of industrial data may be employed for process monitoring, quality prediction, defect detection, and production optimization. Using machine learning techniques like the XGBoost algorithm in conjunction with the SECOM production Process Dataset from UCI, this research suggests a smart framework for optimizing pharmaceutical production. Here are some potential approaches for data cleaning: Data balancing, feature scaling, dimensionality reduction, and missing data imputation are the four pillars of ADASYN, a methodology that improves the precision and effectiveness of data models. Experimental results show that the suggested XGBoost model achieves better results than competing models in terms of ACC (98.93%), PRE (98.97%), REC (98.99%), and F1 score (98.92%). These models include SVM, RF, DT, DNN, and MLP. Improvements in quality control, process efficiency, and intelligent decision-making are achieved via the use of the AI-based framework, which is proven to be a reliable way for enhancing predictive pharmaceutical manufacturing.
subject: Research Paper
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Pharmaceutical Formulation
  topic: keywords
subject: Drug Delivery Systems
  topic: keywords
subject: Quality by Design
  topic: keywords
subject: Pharmaceutical Manufacturing
  topic: keywords
creator: Dr Chintal Kumar Patel
rights: Copyright (c) 2026 Dr Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-28
publication date: 2026-06-28
starting page: 29
ending page: 36
doi: 10.5281/zenodo.21836894

Autonomous Vehicle Safety Prediction Using Machine Learning Techniques
type: item
description: Autonomous Vehicles (AVs) offer promise of mobilizing the world, optimizing traffic flow and safety. However, maintaining safety for pedestrians in the presence of AVs is challenging. The proposed work demonstrates that using machine learning (ML) techniques can improve pedestrian safety in cases where AVs are present. The BDD100K dataset, which includes a variety of driving scenarios under varying lighting, traffic, and weather situations, is the basis for the suggested method in this paper, which is a Deep Neural Network (DNN) for predicting the safety of autonomous vehicles. Preprocessing the dataset using picture inspection, irrelevant class removal, image scaling, data augmentation, one-hot encoding, Min-Max normalization, and data balancing using SMOTE improves prediction performance. After training on 80% of the data, the suggested DNN is tested on 20%. The proposed model achieves empirically better results than CNN, MLP, and ResNet18 in terms of ACC, PRE, REC, and F1 score (F1), with a total of 95.9%. For smarter, safer transportation networks, the suggested method is a solid option for predicting the safety of autonomous vehicles.
subject: Research Paper
  topic: section
subject: Internet of Things
  topic: keywords
subject: Autonomous Vehicles
  topic: keywords
subject: Safety Management
  topic: keywords
subject: Vehicle Safety
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Road Network
  topic: keywords
creator: Dr. Prashant Kumar Srivastava
rights: Copyright (c) 2026 Dr. Prashant Kumar Srivastava (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-28
publication date: 2026-07-28
starting page: 15
ending page: 21
doi: 10.5281/zenodo.21827549

A Survey of Enterprise Supply Chain Management with Digital Transformation Artificial Intelligence Technologies Applications
type: item
description: Enterprise supply chain management (ESCM) is undergoing a sea change due to technological advancements such as digital twins, cloud computing, big data analytics, blockchain, and the IoT. Businesses may see improvements in their supply chains' transparency, efficiency, cooperation, agility, and resilience after implementing these technologies. This review delves into the history and evolution of corporate supply networks, as well as the digital and intelligent supply chain architecture. As a result of digital transformation, organizations are able to automate processes, make data-driven decisions, monitor in real-time, and anticipate risks. Stakeholders in the supply chain can utilize digital twin technology to enhance real-time system presentation, scenario simulation, resource optimization, and collaborative decision-making. In addition, tasks like transportation, distribution, inventory management, forecasting, and manufacturing are all improved by AI technologies including deep learning, machine learning, large language models, natural language processing, and generative AI. AI and IoT can be combined to allow data to flow continuously and be intelligently processed, further enhancing operational responsiveness and predictive capabilities. Finally, AI-powered digital transformation is a complete answer for companies that want to improve supply chain resilience, cut costs, and streamline their supply chain processes. However, standardization of data, interoperability between systems, the scalability of technology, and appropriate utilization of technology are important future research and implementation issues.
subject: Review Article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Enterprise Supply Chain Management
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: Supply Chain Resilience
  topic: keywords
creator: Mr. Kapil Ahir
rights: Copyright (c) 2026 Mr. Kapil Ahir (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-28
publication date: 2026-07-28
starting page: 22
ending page: 28
doi: 10.5281/zenodo.21826981

A Survey of Tools and Techniques of Big Data Management in Cloud Environments
type: item
description: Big data has emerged as a critical asset for organizations due to the rapid growth of data generated from social media, Internet of Things (IoT) devices, business transactions, and digital services. Managing such massive volumes of data requires scalable, efficient, and reliable computing infrastructures. Cloud computing provides a flexible platform for storing, processing, and analyzing big data through distributed resources and on-demand services. This survey paper presents an overview of big data management in cloud environments, focusing on the key tools and techniques used for data storage, processing, analytics, and security. The study discusses widely adopted tools such as Hadoop, MapReduce, Spark, HBase, and NoSQL databases, along with techniques including data mining, machine learning, predictive analytics, optimization methods, and text analytics. Furthermore, the paper highlights major challenges related to scalability, resource management, data privacy, and security in cloud-based big data systems. A comparative review of recent research works is also provided to identify current trends and future directions. The survey concludes that the integration of advanced analytics techniques with cloud computing can significantly improve the efficiency, reliability, and security of big data management.
subject: Review Article
  topic: section
subject: Big Data
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Hadoop
  topic: keywords
subject: MapReduce
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Spark
  topic: keywords
creator: Mr. Himanshu Barhaiya
rights: Copyright (c) 2026 Mr. Himanshu Barhaiya (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-13
publication date: 2026-07-13
starting page: 8
ending page: 14
doi: 10.5281/zenodo.21332231

U-Net based deep learning framework for automated lung nodule segmentation using CT images
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Lung Nodule Segmentation
  topic: keywords
subject: U-Net Architecture
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Medical Image Segmentation
  topic: keywords
subject: Computer-Aided Diagnosis
  topic: keywords
creator: Neha Thakur
rights: Copyright (c) 2026 Neha Thakur (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-02
publication date: 2026-07-02
starting page: 01
ending page: 07
doi: 10.5281/zenodo.21129297

Quantum Computing for cybersecurity and Intelligent Threat Detection: A Survey
type: item
description: 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.
subject: Review Article
  topic: section
subject: Intelligent Cyber Defense
  topic: keywords
subject: Quantum Threat Detection
  topic: keywords
subject: Cryptographic Security
  topic: keywords
subject: Quantum-Enhanced Networks
  topic: keywords
subject: Fault-Tolerant Computing
  topic: keywords
subject: Qubits
  topic: keywords
creator: Mr. Sachin Manekar
rights: Copyright (c) 2026 Mr. Sachin Manekar (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 80
ending page: 85
doi: 10.5281/

Deep Neural Network-Based Detection of Brain Tumors Using MRI Images
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Medical Image Analysis
  topic: keywords
subject: Brain Tumor Classification
  topic: keywords
subject: Magnetic Resonance Imaging
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Artificial Hummingbird Algorithm,
  topic: keywords
creator: Dr. Jvalant kumar Kanaiyalal Patel
rights: Copyright (c) 2026 Dr. Jvalant kumar Kanaiyalal Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 19
ending page: 25
doi: 10.5281/

Load Balancing in Edge-Cloud Ecosystems: A Survey of Hybrid Orchestration Models
type: item
description: 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.
subject: Review Article
  topic: section
subject: hybrid orchestration
  topic: keywords
subject: distributed computing
  topic: keywords
subject: Edge-cloud computing
  topic: keywords
subject: hybrid orchestration
  topic: keywords
subject: Internet of Things (iot)
  topic: keywords
subject: load balancing
  topic: keywords
subject: Quality of Service (qos)
  topic: keywords
subject: Resource Management
  topic: keywords
subject: Workload scheduling
  topic: keywords
creator: Dr. Bal Krishna Sharma
rights: Copyright (c) 2026 Dr. Bal Krishna Sharma (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 66
ending page: 71
doi: 10.5281/

A Survey on Interpretable Machine Learning Techniques for Student Adaptability Prediction in Educational Systems
type: item
description: 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.
subject: Review Article
  topic: section
subject: Educational Data Mining
  topic: keywords
subject: Interpretable Machine Learning
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Student Adaptability
  topic: keywords
subject: Educational Systems
  topic: keywords
subject: Learning Analytics
  topic: keywords
creator: Sushmita Gour
creator: Kamlesh Raghuvanshi
creator: Ram kumar Sahu
rights: Copyright (c) 2026 Sushmita Gour , Kamlesh Raghuvanshi , Ram kumar Sahu (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-25
publication date: 2026-06-25
doi: 10.5281/zenodo.20841827

A Survey of Data Handling Techniques in NextGeneration Database Systems
type: item
description: 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.
subject: Review Article
  topic: section
subject: Next-Generation Database Systems
  topic: keywords
subject: Data Handling Techniques
  topic: keywords
subject: NoSQL Databases
  topic: keywords
subject: Cloud-Native Databases
  topic: keywords
subject: Data Partitioning
  topic: keywords
creator: Dr. Parth Gautam
rights: Copyright (c) 2026 Dr. Parth Gautam (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-24
publication date: 2026-06-24
starting page: 60
ending page: 65
doi: 10.5281/

A Predictive Model for Fraud Detection in Digital Payment Transactions using ML Algorithms
type: item
description: 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%.
subject: Research Paper
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Digital Payment Fraud Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Financial Transaction Security
  topic: keywords
subject: Credit Card Fraud Detection
  topic: keywords
creator: Dr. Neetu Sikarwar
rights: Copyright (c) 2026 Dr. Neetu Sikarwar (Author) https://creativecommons.org/licenses/by/4.0/
date: 2026-06-16
publication date: 2026-06-16
starting page: 53
ending page: 59
doi: 10.5281/zenodo.20734944

Technology Acceptance Model (TAM) and Consumer Behaviour in Digital Product Adoption
type: item
description: 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.
subject: Review Article
  topic: section
subject: Technology Acceptance Model (TAM)
  topic: keywords
subject: Consumer Behavior
  topic: keywords
subject: Digital Innovation
  topic: keywords
subject: Technology Adoption
  topic: keywords
subject: E-commerce
  topic: keywords
subject: Digital Consumers
  topic: keywords
subject: Perceived Usefulness
  topic: keywords
creator: Susanna Pabbineedi
creator: Dr. Aravindh Balan
rights: Copyright (c) 2026 Pabbineedi, Dr. Aravindh Balan (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-15
publication date: 2026-06-15
starting page: 45
ending page: 52
doi: 10.5281/zenodo.20729525

A Detailed Survey of Deep Learning Frameworks for Early Prediction and Classification of Tomato Leaf Diseases
type: item
description: 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.
subject: Review Article
  topic: section
subject: Tomato Leaf Disease
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Image Classification
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Pankaj Modi
creator: Dr. Nilesh Jain
rights: Copyright (c) 2026 Pankaj Modi, Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-16
publication date: 2026-06-16
starting page: 37
ending page: 44
doi: 10.5281/zenodo.20730571

Measuring the Effectiveness of AI-Powered Training in Luxury Automotive Retail Sales Networks
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Automotive industry
  topic: keywords
subject: Sales forecasting
  topic: keywords
subject: Digital transformation
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Production planning
  topic: keywords
subject: vehicle sales data
  topic: keywords
creator: Mohit Kohli
rights: Copyright (c) 2026 Mohit Kohli (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-15
publication date: 2026-06-15
starting page: 31
ending page: 36
doi: 10.5281/zenodo.20728968

The Future of Project Leadership: What AI Means for PMOs in 2026 and Beyond
type: item
description: 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.
subject: Review Article
  topic: section
subject: Artificial Itelligence
  topic: keywords
subject: Project management office
  topic: keywords
subject: Project Leadership
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Predictive Decision-Making
  topic: keywords
subject: Autonomous PMO
  topic: keywords
subject: Data-Driven Leadership
  topic: keywords
subject: Human–AI Collaboration
  topic: keywords
creator: Matthew M. Fernandes
rights: Copyright (c) 2026 Matthew M. Fernandes (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-15
publication date: 2026-06-15
starting page: 23
ending page: 30
doi: 10.5281/zenodo.20729189

Machine Learning-Based Predictive Maintenance Classification for Industrial Equipment Failure Detection
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Machine Learning
  topic: keywords
subject: Industrial Machinery
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Condition Monitoring
  topic: keywords
subject: Failure Prediction
  topic: keywords
creator: Aravindh Balan
rights: Copyright (c) 2026 Aravindh Balan (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-15
publication date: 2026-06-15
starting page: 16
ending page: 22
doi: 10.5281/zenodo.20735009

An Empirical Study on Business Continuity Governance and Organizational Effectiveness in Contemporary Enterprises
type: item
description: 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 strateg…
subject: Review Article
  topic: section
subject: Enterprise Resilience
  topic: keywords
subject: Business Continuity Governance
  topic: keywords
subject: Organizational Effectiveness
  topic: keywords
subject: ISO 22301
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Strategic Governance
  topic: keywords
subject: Business Sustainability
  topic: keywords
subject: Continuity Management
  topic: keywords
creator: Sandile Khoza
rights: Copyright (c) 2026 Sandile Khoza (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-13
publication date: 2026-06-13
starting page: 9
ending page: 15
doi: 10.5281/zenodo.20729729

Security, Privacy, and Trust in Cloud Computing: An Integrated Survey of Techniques
type: item
description: 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.
subject: Review Article
  topic: section
subject: Trust Management
  topic: keywords
subject: Cloud Computing,
  topic: keywords
subject: Trust Evaluation
  topic: keywords
subject: Security
  topic: keywords
subject: Privacy
  topic: keywords
subject: Data Protection
  topic: keywords
subject: Access Control
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Dr. Nilesh Jain
rights: Copyright (c) 2026 Dr. Nilesh Jain (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 1
ending page: 8
doi: 10.5281/

Reimagining Maintainability Machine Learning Techniques For Security Requirement Optimization
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Soft Software Maintainability
  topic: keywords
subject: Metrics
  topic: keywords
subject: Security
  topic: keywords
subject: Software
  topic: keywords
subject: machine learning
  topic: keywords
creator: Gopal Verma
creator: Atul Kumar Mishra
rights: Copyright (c) 2026 Gopal Verma, Atul Kumar Mishra (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 10
ending page: 18
doi: 10.5281/

A Robust Cross-Platform Deepfake DetectionFramework Using Multi-Modal Deep Learning and Explainable AI
type: item
description: 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.
subject: Research Paper
  topic: section
subject: deepfake detection
  topic: keywords
subject: multi-modal learning
  topic: keywords
subject: explainable AI
  topic: keywords
subject: cross-dataset generalization
  topic: keywords
subject: CNN-Transformer
  topic: keywords
subject: biological signals
  topic: keywords
creator: Sweta Sharma
creator: Pradeep Chouksey
creator: Aastha Rathi
creator: Parveen Sadotra
creator: Mayank Chopra
rights: Copyright (c) 2026 Sweta Sharma, Pradeep Chouksey, Aastha Rathi, Parveen Sadotra, Mayank Chopra (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-18
publication date: 2026-05-18
starting page: 05
ending page: 09
doi: 10.5281/zenodo.20265382

Deep Learning Approaches for Apple Leaf DiseaseDetection and Classification: A Comprehensive Review
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Apple disease detection
  topic: keywords
subject: CNN models
  topic: keywords
subject: Deep neural networks
  topic: keywords
subject: Smart agriculture
  topic: keywords
subject: Transfer learning
  topic: keywords
subject: Model generalization
  topic: keywords
subject: Mobile deployment.
  topic: keywords
creator: Reena kumari
creator: Pradeep Chousky
creator: Praveen Sadotra
creator: Mayank Chopra
rights: Copyright (c) 2026 Reena kumari, Pradeep Chousky, Praveen Sadotra, Mayank Chopra (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-16
publication date: 2026-05-16
starting page: 01
ending page: 04
doi: 10.5281/zenodo.20238561

Shifting Hegemonies: Regional Power Dynamics in Post-Panipat India (1761-1800)
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Panipat 1761: Regional Power Shifts
  topic: keywords
subject: Eighteenth-Century India
  topic: keywords
subject: State Formation
  topic: keywords
subject: Colonial Ascendancy in South Asia
  topic: keywords
creator: Krishan Kumar Joon
creator: Dr Pradeep Kumar Kesharwani
rights: Copyright (c) 2026 Krishan Kumar Joon, Dr Pradeep Kumar Kesharwani (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-21
publication date: 2026-01-21
starting page: 86
ending page: 90
doi: 10.5281/zenodo.20175130

Hybrid CNN–Vision Transformer Architecture forAccurate Liver Cancer Diagnosis from MedicalImaging
type: item
description: 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
subject: Review Article
  topic: section
subject: Hybrid Deep Learning
  topic: keywords
subject: CNN
  topic: keywords
subject: Vision Transformer
  topic: keywords
subject: Liver Cancer
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Feature Fusion
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: Satyendra Sharma
creator: Pradeep Laxkar
rights: Copyright (c) 2026 Satyendra Sharma, Pradeep Laxkar (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-18
publication date: 2026-04-18
starting page: 07
ending page: 11
doi: 10.5281/zenodo.20047881

Culture and the Fragmentation of Women’s Agency:Evidence from Tribal and Non-Tribal Communitiesin Tripura
type: item
description: 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 …
subject: Review Article
  topic: section
subject: Culture
  topic: keywords
subject: Gender
  topic: keywords
subject: Reproductive Agency
  topic: keywords
subject: Women’s Agency
  topic: keywords
creator: Anindita Sinha
rights: Copyright (c) 2026 Anindita Sinha (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-02
publication date: 2026-05-02
starting page: 01
ending page: 06
doi: 10.5281/zenodo.19982901

AI-Powered Industrial Robots in Smart Factories: A Survey of Applications Challenges and Future Trends
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Smart factories
  topic: keywords
subject: AI-powered
  topic: keywords
subject: industrial robots
  topic: keywords
subject: industrial automation
  topic: keywords
subject: machine learning
  topic: keywords
subject: collaborative robots
  topic: keywords
subject: intelligent manufacturing
  topic: keywords
creator: Mr. Kapil Ahir
rights: Copyright (c) 2025 Mr. Kapil Ahir (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-18
publication date: 2025-12-18
starting page: 79
ending page: 86
doi: 10.5281/zenodo.19689776

Auto-Scaling and Load Balancing Strategies inKubernetes For High-Availability CloudApplications
type: item
description: 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.
subject: Research Paper
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Kubernetes (K8s)
  topic: keywords
subject: Edge Orchestration
  topic: keywords
subject: Cloud–Edge Collaboration
  topic: keywords
subject: Platform-Based Solutions
  topic: keywords
subject: Custom Architectures
  topic: keywords
creator: Dr. Chintal Kumar Patel
rights: Copyright (c) 2026 Dr. Chintal Kumar Patel (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-18
publication date: 2026-01-18
starting page: 20
ending page: 26
doi: 10.5281/zenodo.19659371

Survey on Integration of IoT with Artificial Intelligence (AI) Technologies for Smart Manufacturing Operations
type: item
description: 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.
subject: Review Article
  topic: section
creator: Prithviraj Singh Rathore
rights: Copyright (c) 2026 Dr. Prithviraj Singh Rathore (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-14
publication date: 2026-02-14
starting page: 17
ending page: 22
doi: 10.5281/zenodo.18668678

Machine Learning-Based Default Loan Prediction for Financial Risk Assessment in Digital Lending
type: item
description: 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.
subject: Research Paper
  topic: section
creator: Neha Upadhyay
rights: Copyright (c) 2026 Mrs. Neha Upadhyay (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 9
ending page: 16
doi: 10.5281/zenodo.18537911