Category Archives: Uncategorized

Authors: Devansh Rithala

Abstract: Distributed wireless cloud–IoT architectures are increasingly critical in enabling real-time monitoring, data analytics, and intelligent decision-making across various industries, including smart cities, healthcare, industrial automation, and agriculture. However, the complexity, heterogeneity, and geographic distribution of these systems introduce significant operational risks that can compromise performance, reliability, and security. This article provides a comprehensive analysis of operational risks in distributed wireless cloud–IoT architectures, including hardware failures, network disruptions, cybersecurity threats, data integrity issues, and cloud service outages. It examines risk assessment and analysis techniques, such as fault tree analysis, failure mode effects analysis, and probabilistic modeling, to identify and prioritize vulnerabilities. The article also presents mitigation strategies, including redundancy, edge computing, network optimization, real-time monitoring, predictive maintenance, and security measures, while discussing challenges in implementation, such as scalability, interoperability, cost, and performance trade-offs. Future directions, including the integration of artificial intelligence, blockchain, next-generation wireless networks, and standardized risk management frameworks, are explored to enhance system resilience. By adopting a proactive and systematic approach to operational risk management, organizations can ensure reliability, efficiency, and sustainability in complex distributed wireless cloud–IoT ecosystems.

DOI: http://doi.org/10.5281/zenodo.18169504

Published by: vikaspatanker

Authors: Prof. Supriya G Purohit, Mr.Tanveer Ahmed, Mr.Syed Adnan, Mr.Mohammed Zaid Noman

Abstract: In an increasingly digital world, the need for accessible and intuitive human-computer interaction (HCI) solutions is more critical than ever—especially for individuals with physical disabilities. This project proposes a Virtual Mouse System that integrates hand gestures, eye tracking, and an AI-powered chatbot to offer a seamless, multimodal interface for touch-free computing. By combining real-time computer vision, deep learning, and natural language processing (NLP), the system replaces traditional input devices like keyboards and mice with a more inclusive and efficient alternative. The hand gesture module utilizes OpenCV, MediaPipe, and Convolutional Neural Networks (CNNs) to detect and interpret finger movements and predefined gestures for cursor movement, clicking, and scrolling. The eye-tracking module employs Haar cascade classifiers, Hough Transform, and Eye Aspect Ratio (EAR) techniques to track gaze and blinks for cursor control and selection, enabling hands-free navigation. To enhance interactivity, a chatbot module powered by NLP models such as BERT or GPT handles voice and text-based queries for performing system-level commands and basic computational tasks. Communication among these modules is managed through a Flask-based backend, ensuring synchronized, responsive interaction. Designed for both general users and those with motor impairments, the system addresses limitations found in standalone gesture or voice- based solutions, such as lighting sensitivity, gesture misrecognition, or voice command errors. By integrating multiple input modalities, the system enhances accuracy, usability, and user autonomy. Applications span from accessibility tools to smart environments, virtual reality, and beyond. Future work includes improving gaze estimation through deep learning and enhancing chatbot capabilities for broader conversational interaction.

DOI: https://doi.org/10.5281/zenodo.18169212

Published by: vikaspatanker

Authors: Anirudh S, Tanuja R

Abstract: Fractures and bone trauma are serious injuries that are increasing in frequency worldwide. In some cases, these injuries are not easily visible through traditional diagnostic methods such as x-rays, leading to misdiagnosis and inadequate treatment. To address this issue, a Computer-Aided Diagnosis and Recommendation System could be developed, which utilizes various deep learning techniques to accurately detect the severity of the fracture and recommend appropriate exercises, diet plans, and surgeries for recovery. This system would incorporate techniques such as deep learning convolutional neural networks, edge detection, ridge regression, and image smoothing to enhance accuracy and provide more precise recommendations. Each technique would contribute unique features to the system, resulting in better outcomes for patients.

DOI: https://doi.org/10.5281/zenodo.18169049

 

Published by: vikaspatanker

Authors: Adiv Jainwal

Abstract: The rapid evolution of the Internet of Things (IoT) and wireless cloud computing has led to highly distributed system architectures that support large-scale, data-intensive, and latency-sensitive applications. While these architectures offer improved scalability and flexibility, they also introduce significant risks related to security, privacy, reliability, performance, and operational management. Traditional IoT architectural designs primarily focus on functional and performance requirements and often lack explicit mechanisms to address these risks. Consequently, risk-aware architectural design has emerged as a critical paradigm for enhancing the robustness and trustworthiness of distributed IoT systems over wireless clouds. This paper presents a comprehensive review of risk-aware architectural design approaches for distributed IoT environments integrated with wireless cloud infrastructures. It examines the fundamental architectural principles, identifies key risk factors across multiple system layers, and analyzes existing risk-aware design strategies, including secure-by-design, privacy-preserving, and resilience-oriented architectures. The review further explores architectural frameworks and reference models that incorporate risk management as a core design component and evaluates their applicability across various IoT application domains such as smart cities, industrial IoT, healthcare, and smart energy systems. Through a comparative analysis of existing solutions, the paper highlights current limitations, trade-offs, and research gaps. Finally, it outlines open challenges and future research directions to guide the development of adaptive, scalable, and sustainable risk-aware IoT architectures. This review aims to support researchers and practitioners in designing resilient distributed IoT systems capable of operating securely and efficiently in dynamic wireless cloud environments.

DOI: http://doi.org/10.5281/zenodo.18162850

Published by: vikaspatanker

Authors: Kritika Somvanshi

Abstract: The rapid growth of the Internet of Things, combined with advancements in cloud computing and next-generation wireless technologies, has created unprecedented opportunities for intelligent, interconnected systems. These systems, often referred to as wireless cloud-IoT platforms, rely on seamless connectivity to enable real-time data exchange, remote management, and advanced analytics. However, the increasing number of connected devices, diversity of communication protocols, and dynamic network conditions pose significant challenges to maintaining reliable and resilient connectivity. Resilient connectivity in this context refers to the ability of the network to maintain service continuity, recover from failures, and adapt to changing environmental and operational conditions without significant degradation in performance. This review examines the state-of-the-art approaches for achieving resilient connectivity in next-generation wireless cloud-IoT platforms, highlighting the key technological enablers such as 5G and 6G networks, low-power wide-area networks, edge and fog computing, and software-defined networking. It also provides a detailed discussion of fault-tolerant designs, adaptive and resource-aware connectivity models, and security-driven approaches that ensure continuity and reliability in heterogeneous IoT environments. By comparing existing methods and analyzing their performance metrics, this review identifies gaps in current research and outlines open challenges, including scalability, energy efficiency, and security. Furthermore, the review explores emerging directions such as artificial intelligence-driven network adaptation, digital twin integration, and autonomous connectivity management. The insights provided in this work are intended to guide researchers, engineers, and practitioners in designing next-generation wireless cloud-IoT platforms that are robust, flexible, and capable of supporting the increasing demands of smart applications. Overall, this article emphasizes the importance of resilient connectivity as a foundational requirement for the successful deployment and operation of future IoT ecosystems, offering a comprehensive overview of current solutions and potential pathways for further innovation.

DOI: http://doi.org/10.5281/zenodo.18162844

Published by: vikaspatanker

Authors: Yuvraj Deshmora

Abstract: Enterprise Resource Planning systems, particularly SAP ERP, have become critical tools for organizations seeking to streamline operations, integrate business processes, and manage risks effectively. Traditional risk management approaches in SAP ERP often rely on periodic assessments, static risk scoring, and manual intervention, which may fail to capture emerging threats or changes in operational environments. Continuous risk scoring powered by autonomous learning algorithms offers a transformative approach, enabling real-time identification, evaluation, and mitigation of risks across various business processes. By leveraging machine learning, deep learning, and adaptive algorithms, organizations can continuously analyze transactional, master, and operational data to detect anomalies, predict potential failures, and proactively respond to emerging threats. This review examines the current state of continuous risk scoring in SAP ERP, highlighting the capabilities of autonomous learning algorithms, data integration challenges, practical applications, and potential limitations. Case studies and literature indicate that organizations adopting autonomous risk scoring benefit from improved decision-making, reduced manual oversight, and enhanced compliance with regulatory standards. Furthermore, continuous risk assessment supports proactive management strategies by providing dynamic insights into operational, financial, and compliance risks. The review also identifies future directions, including the incorporation of explainable AI for interpretability, integration with cloud-based SAP systems, and the use of reinforcement learning to enhance predictive accuracy. The findings suggest that continuous risk scoring is not only a technological advancement but also a strategic necessity for organizations aiming to maintain resilience and agility in a rapidly changing business environment. By synthesizing current research and practical implementations, this review provides a comprehensive understanding of how autonomous learning algorithms can revolutionize risk management in SAP ERP. It concludes with recommendations for future research and practical adoption strategies to maximize the benefits of continuous risk scoring.

DOI: http://doi.org/10.5281/zenodo.18162741

Published by: vikaspatanker

Authors: Pranita Lohani

Abstract: The rapid growth of Internet of Things (IoT) devices and the increasing reliance on cloud computing have driven the need for autonomous wireless Cloud–IoT systems capable of supporting large-scale, real-time applications. These systems integrate heterogeneous devices, sensors, and networks with cloud-based platforms to enable seamless data collection, processing, and decision-making without significant human intervention. The architecture of such systems typically involves layered structures, including edge computing, fog nodes, and centralized cloud services, to optimize performance, reduce latency, and enhance scalability. Despite these benefits, the deployment of autonomous Cloud–IoT systems introduces substantial risks, particularly in terms of cybersecurity, data privacy, and system reliability. Vulnerabilities in communication protocols, improper access controls, and potential failures in autonomous decision-making mechanisms pose significant challenges. To address these concerns, robust risk assessment frameworks, adaptive security mechanisms, and fault-tolerant architectural designs are essential. Moreover, ensuring interoperability among diverse devices and standards while maintaining energy efficiency further complicates system design. This study explores the architectural models and operational strategies of autonomous wireless Cloud–IoT systems, emphasizing both their functional advantages and potential threats. It examines current methodologies for risk identification, mitigation, and continuous monitoring to achieve resilient and secure operation. By providing a comprehensive perspective on architecture and risk, this work aims to guide the development of reliable, scalable, and secure autonomous Cloud–IoT systems capable of supporting emerging applications in smart cities, healthcare, industrial automation, and environmental monitoring.

DOI: http://doi.org/10.5281/zenodo.18162732

Published by: vikaspatanker

Authors: Rivansh Kaushik

Abstract: The abstract provides a concise summary of the review article, emphasizing the integration of machine learning (ML) techniques with SAP-based financial planning to improve forecast accuracy. Financial forecasting is a critical function for organizations to manage budgets, allocate resources, and make strategic decisions. Traditional forecasting methods in SAP, such as time-series analysis and rule-based approaches, often struggle with complex and dynamic market conditions, leading to suboptimal planning. Machine learning offers advanced predictive capabilities by identifying hidden patterns in historical data and adapting to new trends over time. This review highlights the role of ML algorithms including regression models, neural networks, and ensemble methods in enhancing forecasting precision. The article systematically examines the integration process of ML with SAP systems, exploring data preprocessing, model selection, and deployment within SAP environments. Key case studies and research findings demonstrate measurable improvements in forecast accuracy, including reduced error metrics such as RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error). The review also addresses practical challenges, such as data quality issues, computational resource demands, and organizational adoption hurdles. Finally, it outlines future directions, including real-time predictive analytics, AI-driven planning, and hybrid approaches combining ML with traditional statistical models. By providing a comprehensive overview, the article aims to guide both practitioners and researchers in leveraging machine learning for enhanced financial decision-making within SAP-based systems. The abstract serves as a snapshot, giving readers insight into the objectives, methodology, findings, and implications of integrating ML in SAP financial planning, emphasizing the potential for improved efficiency, accuracy, and strategic value.

DOI: http://doi.org/10.5281/zenodo.18162637

Published by: vikaspatanker

Authors: Mrinal Daksheen

Abstract: Achieving enterprise-wide financial transparency is a critical challenge for large organizations due to fragmented data, manual reconciliation processes, and delayed reporting. SAP provides a robust platform for integrated financial management, yet traditional reporting methods often fall short in delivering real-time, accurate insights. This article explores the application of data-centric AI pipelines within SAP to enhance financial transparency across the enterprise. By emphasizing high-quality, validated data over purely model-centric approaches, these pipelines enable automated data extraction, cleaning, transformation, and validation, supporting real-time dashboards, predictive forecasting, anomaly detection, and compliance monitoring. The discussion covers pipeline architecture, integration strategies, implementation best practices, and potential benefits, including improved accuracy, operational efficiency, risk mitigation, and regulatory compliance. Challenges such as data inconsistency, integration complexity, and model maintenance are also addressed, along with future directions in adaptive AI and enterprise-wide intelligent financial systems. By adopting data-centric AI pipelines, organizations can transform financial reporting into a proactive, insight-driven function, enhancing decision-making, stakeholder trust, and organizational agility.

DOI: https://doi.org/10.5281/zenodo.18162511

Published by: vikaspatanker

Authors: Bhavya Kaironit

Abstract: Risk-aware cloud architectures play a pivotal role in enhancing the security, compliance, and operational efficiency of SAP-enabled financial and healthcare systems. By integrating risk management principles, data protection mechanisms, and governance frameworks directly into cloud environments, organizations can proactively address threats while enabling innovation. In financial systems, these architectures support secure transaction processing, fraud detection, and regulatory reporting, ensuring reliability and compliance. In healthcare, they facilitate secure electronic health records, analytics, and telemedicine services while adhering to privacy regulations such as HIPAA and GDPR. Incorporating AI and automation further strengthens risk detection, response, and monitoring capabilities. This approach ensures that sensitive financial and patient data are safeguarded, regulatory requirements are met, and organizational trust is maintained. The paper highlights how risk-aware design principles in SAP cloud architectures can simultaneously enable innovation and resilience in highly regulated industries.

DOI: https://doi.org/10.5281/zenodo.18162423

 

Published by: vikaspatanker