Journal of Advanced Research in Cloud Computing, Virtualization and Web Applications

DIGITAL BANKING TRANSFORMING THE FUTURE OF BANKING

Wed, 30 Sep 2026 00:00:00 +0000

Digital banking is rapidly transforming the financial services industry through technological innovation, changing customer expectations, and the increasing adoption of digital financial services. The future of digital banking is expected to be shaped by technologies such as artificial intelligence, machine learning, blockchain, cloud computing, biometric authentication, open banking, and mobile platforms. These technologies can enhance customer experience, improve operational efficiency, strengthen fraud detection, and enable personalized financial services. At the same time, digital banking faces challenges related to cybersecurity, data privacy, regulatory compliance, digital inequality, and customer trust. This study examines the emerging trends and future opportunities in digital banking and explores how financial institutions can adapt to a technology-driven financial ecosystem. The study highlights the importance of innovation, security, personalization, and customer-centric strategies in building sustainable digital banking services. The future of banking is likely to involve greater integration between traditional financial institutions and digital technologies, creating faster, more accessible, and intelligent financial services.

Digital Payment Fraud and Safety Measure

Gaurav Surwade, Badal Chavan, Yash Gadiya, Harshal Patil — Wed, 30 Sep 2026 00:00:00 +0000

Real-Time Fraud Detection and Prevention in Financial Transactions Using Big Data Analytics on Microsoft Azure

Wed, 30 Sep 2026 00:00:00 +0000

- The rapid growth of digital financial transactions has increased the risk of financial fraud and created a need for fast and accurate fraud detection systems. Traditional rule-based and batch-processing approaches may not be effective against continuously changing fraud patterns and can also produce a high number of false alerts. This research proposes a real-time fraud detection and prevention framework using Big Data Analytics, Machine Learning, and Microsoft Azure. The proposed system integrates transaction data, customer information, behavioral patterns, and other relevant data sources to identify suspicious transactions in real time. Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, Azure Stream Analytics, and Azure Machine Learning are used to support data storage, distributed processing, real-time analysis, and machine learning model deployment. The study evaluates multiple machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, LightGBM, Neural Networks, and Isolation Forest. The research also considers class imbalance using techniques such as SMOTE, cost-sensitive learning, and threshold optimization. Initial analysis of the Credit Card Fraud Detection dataset containing 284,807 transactions identified 492 fraudulent transactions, representing only 0.17% of the total data. Preliminary results show that XGBoost achieved an F1-score of 90.1%, while the implemented Azure-based approach demonstrated the potential for sub-100 ms inference latency. The research aims to provide a scalable, secure, and cost-effective framework for real-time fraud detection while considering privacy, regulatory compliance, and evolving fraud patterns.

THE FUTURE OF CLOUD COMPUTING: BENEFITS AND CHALLENGES

Wed, 30 Sep 2026 00:00:00 +0000

Cloud computing is fundamentally redefining the future of digital infrastructure, offering unprecedented flexibility and scalability for modern enterprises. By leveraging virtualized resources, distributed server networks, and pay-as-you-go service models, organizations can eliminate heavy capital expenditures and optimize their operational efficiency. This technological shift is driving massive transformations across diverse sectors, including healthcare, finance, and education, by enabling real-time collaboration, seamless data integration, and rapid deployment of advanced applications. As cloud platforms increasingly integrate with cutting-edge technologies like edge computing and artificial intelligence, they promise profound economic growth and enhanced global connectivity. However, this rapid expansion also raises critical challenges regarding data privacy, regulatory compliance, and vendor lock-in. Addressing these complex security and ethical considerations requires the development of robust zero-trust architectures and sustainable green computing practices to ensure responsible deployment and long-term reliability in the digital era

Business Analytics for Sustainable Supply Chains: Inventory Optimization and Operational Efficiency in the Textile Industry

Dr Rohit Salunkhe , Vaibhav Chaturbhuj — Wed, 30 Sep 2026 00:00:00 +0000

The textile industry is increasingly adopting business analytics to improve supply chain visibility, inventory control, operational efficiency, and resource utilization. This study examines the role of business analytics in supporting sustainable supply chain management, with particular emphasis on inventory optimization and operational efficiency in the textile industry. The study adopted a descriptive research design using a mixed-method approach. Primary data were collected from respondents selected purposively from key supply chain functions, including managers and operational staff, while secondary data were obtained from operational inventory records and ERP/SAP-based information. Given the organization-specific and exploratory nature of the study, the sample was considered suitable for examining relationships among the selected variables. The analysis focused on business analytics usage, inventory visibility, inventory classification, inventory ageing, identification of slow-moving stock, and data-driven procurement decisions. Descriptive analysis of operational records showed that 94% of the total inventory was unrestricted stock, while the inventory ageing analysis revealed that 47.81% of inventory had remained in stock for more than 90 days, indicating the presence of substantial slow-moving inventory. The study also found that ABC classification and ageing analysis help identify critical and excess inventory categories and support better stock-control decisions. Statistical analysis indicated positive relationships between business analytics practices, inventory management, identification of ageing inventory, and operational efficiency. The findings suggest that the integration of analytics with inventory and procurement practices can improve stock visibility, reduce unnecessary inventory accumulation, strengthen resource utilization, and support resource-efficient and operationally sustainable supply chain practices. The study recommends greater use of real-time dashboards, ERP-integrated analytics, predictive tools, and periodic inventory-ageing reviews to strengthen data-driven and sustainable supply chain decision-making in textile organizations.

Cloud-Based Smart Ayurveda Healthcare System for Bharat 2047: A Comprehensive Review

Chhayadeep Kaur — Thu, 06 Aug 2026 00:00:00 +0000

The rapid advancement of Artificial Intelligence (AI), Cloud Computing, and digital healthcare technologies is fundamentally transforming the global healthcare landscape. India, distinguished by its extensive traditional healthcare heritage and the strategic vision of Viksit Bharat 2047, presents substantial opportunities to integrate contemporary technologies with established traditional healthcare systems, thereby creating sustainable and accessible healthcare solutions. This review paper examines the concept of a Cloud-Based Smart Ayurveda Healthcare System and analyzes how AI and cloud technologies can enhance healthcare service delivery throughout urban and rural India.

This study elucidates the critical role of cloud computing in facilitating secure data storage, electronic health records management, telemedicine services, remote patient monitoring, and real-time healthcare accessibility. Additionally, it explores how AI technologies, including machine learning, predictive analytics, and intelligent decision-support systems, can enable personalized healthcare recommendations, disease prediction, and efficient patient management. The paper reviews contemporary developments in digital healthcare infrastructure and underscores the significance of integrating traditional Indian healthcare knowledge with advanced computational technologies to support sustainable national development objectives.

Furthermore, this review identifies several significant challenges, including data privacy concerns, cybersecurity vulnerabilities, insufficient digital literacy among users, infrastructure constraints, and ethical considerations related to AI-based healthcare systems. The study concludes that cloud-enabled smart healthcare systems possess the capacity to make substantial contributions to affordable, scalable, and efficient healthcare delivery while advancing the objectives of Digital India and Bharat 2047.

A Comprehensive Review on Privacy-Preserving Federated Learning Frameworks for Healthcare IoT Using Blockchain and Edge Intelligence

Mohammad Taha, Arun Kumar Rai — Fri, 27 Jun 2025 00:00:00 +0000

The fast growth of the healthcare Internet of Things (IoT) solutions resulted in the generation of vast amounts of confidential medical data, with an equally growing concern regarding data protection and data use, amongst other factors. This review article will cover the most cutting- edge developments in federated learning (FL) frameworks for privacy- preserving blockchain integration and edge computing in healthcare. At a theoretical level, it became clear that the current centralised machine-learning methods are associated with the risks of data-privacy loss, regulatory impediments, and resource-constraint costs. In this situation, federated learning emerges as a very promising new paradigm capable of accommodating decentralised model training without raw data sharing, thereby further ensuring data privacy and security. In recent years, several contributions have been made combining secure characteristics for data privacy during training and aggregation and well-known techniques such as Fully Homomorphic Encryption (FHE), Secure Multi-Party Computation (SMPC), and Differential Privacy (DP). Moreover, the paper also touches upon the possibilities of blockchain to create secure, transparent, and immutable updates to the models using methodologies like Proof of Authority (PoA) and Delegated Proof of Stake (DPoS). Moreover, directing edge computing into the design could further enhance systems’ scalability and minimise latency within the healthcare domain. The text addresses the issues like non- IID data distribution, class imbalance, communication overhead, and convergence problems in the federated setting. Emerging methodologies such as cross-domain federated learning, multitask learning, and adaptive aggregation strategies were discussed to enhance the model’s generalisation and efficiency levels. The overall goal of the paper is to propose and develop a combination of federated learning together with blockchain and privacy-preserving technologies to enhance secure, scalable, and efficient health IoT systems.

How to cite this article:
Taha M, Rai A K. A Comprehensive Review on Privacy-Preserving Federated Learning Frameworks for Healthcare IoT Using Blockchain and Edge Intelligence. J Adv Res Cloud Comp Virtu Web Appl 2026; 9(2): 1-10.

Sustainable Preservation of Indian Knowledge Systems through Cloud Computing: A Roadmap for Bharat 2047

Tisha — Mon, 03 Aug 2026 00:00:00 +0000

India's indigenous knowledge systems — spanning Ayurveda, Yoga, classical performing arts, vernacular literature, oral traditions, folk sciences, and ancient manuscripts — represent millennia of accumulated wisdom that faces accelerating erosion due to inadequate digitization, linguistic fragmentation, the passing of aging knowledge holders, and weak archival infrastructure. This paper proposes a structured roadmap for leveraging cloud computing technologies to sustainably preserve, digitize, and democratize access to Indian knowledge systems in alignment with the Bharat 2047 vision of a self-reliant, technologically empowered, and culturally rooted nation. We examine the current landscape of digital preservation initiatives — including the National Digital Library of India (NDLI), the National Mission for Manuscripts (NMM), and the Indira Gandhi National Centre for the Arts (IGNCA) — and identify critical gaps in scalability, interoperability, and multilingual accessibility. A federated cloud architecture is proposed, integrating AI-assisted transcription and translation, metadata standardization aligned with international frameworks, and edge computing for rural community access. Governance frameworks addressing intellectual property, community ownership, and ethical data stewardship are discussed. The paper concludes that a community-participatory cloud ecosystem, supported by public-private partnerships and open standards such as IIIF and Dublin Core, represents the most viable path for ensuring India's knowledge heritage is preserved not merely as static artifacts but as living, accessible, and actionable resources for future generations.

Predictive Modelling of Livestock Health using Wearable Sensors

Arsh Nishad, Dr. Raman Chadha, Komal, Sia Chawla — Tue, 21 Jul 2026 00:00:00 +0000

Animal welfare, farm productivity, and food security are basically on animal health management. The conventional forms of monitoring make a lot of use of manual surveillance, which takes time and mostly identifies diseases when they are on advanced stages. This paper provides a predictive modelling tool of animal health care utilizing wearable sensors, Internet of Things (IoT), and machine learning tools that deliver real time data of physiological and behavioural aspects of animals like body temperature and activity levels, rumination patterns, and location. The resulting data are then sent via the wireless connections to the edge and cloud systems where the preprocessing and predictive analytics are implemented to detect abnormalities and any health risks. Different machine learning methods are used to predict health conditions and provide early warning to farmers and veterinarians. The experimental outcomes prove that multimodal sensor integration is a better way to predict health conditions and provide an opportunity to intervene before it is too late than traditional methods of monitoring. The suggested system promotes the active disease control, lessening the economic losses, and increasing the accuracy of the livestock farming based on the real-time and data-driven decision-making.