Category Archives: Uncategorized

Authors: Nandini Bhalla

Abstract: Machine learning–driven risk management has gained significant attention as organizations increasingly rely on SAP-based financial and enterprise information systems to support critical business operations. Traditional risk management approaches in SAP environments are predominantly rule-based and reactive, limiting their effectiveness in detecting complex, evolving, and previously unknown risks. With the growing volume, velocity, and complexity of enterprise data, machine learning techniques offer advanced capabilities for predictive risk assessment, anomaly detection, and continuous monitoring. This review paper presents a comprehensive analysis of machine learning–driven risk management models applied to SAP-based financial and enterprise information systems. It systematically examines SAP system architectures, risk management frameworks, and the integration of supervised, unsupervised, and hybrid machine learning techniques for managing financial, operational, compliance, and access control risks. The paper also reviews SAP-specific data sources, data preprocessing requirements, and evaluation metrics used to assess model performance, with particular attention to challenges such as data quality, model interpretability, regulatory compliance, and system integration. Furthermore, the review identifies key research gaps and emerging trends, including explainable artificial intelligence, federated learning, and real-time continuous auditing within SAP environments. By synthesizing existing literature and highlighting practical and research implications, this paper provides valuable insights for researchers, practitioners, and organizations seeking to design and implement intelligent, scalable, and compliant risk management solutions in SAP-based enterprise systems.

DOI: http://doi.org/10.5281/zenodo.18170299

Published by: vikaspatanker

Authors: Atharv Joshi

Abstract: The convergence of Artificial Intelligence (AI), Cloud Computing, and the Internet of Things (IoT) has enabled intelligent, data-driven transformation across healthcare and financial systems. However, the integration of these technologies presents significant challenges related to security, scalability, interoperability, and real-time decision-making. Healthcare and financial domains demand highly reliable and secure architectures due to the sensitive nature of their data and strict regulatory requirements. Existing solutions often address these technologies in isolation, resulting in fragmented architectures and increased exposure to operational and security risks. This paper proposes a unified artificial intelligence framework that securely integrates cloud and IoT infrastructures to support intelligent healthcare and financial applications. The framework adopts a layered architecture encompassing IoT data acquisition, cloud-based storage and processing, AI-driven analytics, and embedded security mechanisms. Machine learning and deep learning models are employed to enable predictive analytics, anomaly detection, and decision support while ensuring data confidentiality, integrity, and availability. The framework supports both real-time and batch data processing, enabling scalable and low-latency operations. The proposed framework is validated through healthcare and financial use case scenarios, including remote patient monitoring and real-time financial transaction analysis. Performance evaluation demonstrates improved system efficiency, enhanced decision-making accuracy, and robust security compared to traditional siloed systems. The results confirm that the unified framework effectively addresses integration challenges while maintaining compliance and adaptability. This research contributes a comprehensive and scalable solution for next-generation intelligent healthcare and financial ecosystems, offering a foundation for future advancements in AI-enabled cloud and IoT integration.

DOI: http://doi.org/10.5281/zenodo.18169572

Published by: vikaspatanker

Authors: Parthiv Yodhan

Abstract: The rapid expansion of clinical data and the growing demand for personalized, efficient healthcare necessitate innovative approaches to data management and analytics. Traditional clinical data management (CDM) processes often struggle with data fragmentation, manual processing, and delayed insights, which can negatively impact patient outcomes and operational efficiency. This article explores the integration of Artificial Intelligence (AI) with SAP Digital Health platforms as a transformative solution for optimizing clinical data management and analytics. AI technologies, including machine learning and natural language processing, enhance data cleaning, validation, predictive modeling, and decision support, while SAP platforms provide a secure, interoperable, and scalable infrastructure for data integration and real-time analytics. By leveraging this synergy, healthcare organizations can improve diagnostic accuracy, enable personalized care, optimize operational workflows, and accelerate clinical research. The article also examines implementation challenges such as data privacy, interoperability, adoption barriers, and ethical considerations, and highlights emerging trends including real-time patient monitoring, genomics integration, and telemedicine analytics. Ultimately, AI-powered SAP Digital Health platforms offer a pathway toward a data-driven, patient-centric healthcare ecosystem, where predictive insights and proactive interventions significantly enhance clinical outcomes, operational efficiency, and population health management.

DOI: http://doi.org/10.5281/zenodo.18169570

Published by: vikaspatanker

Authors: Kashvi Uprex

Abstract: The rapid expansion of wireless Internet of Things (IoT) devices has created unprecedented opportunities and challenges for modern IT infrastructures. Traditional systems often struggle to accommodate the massive data volumes, real-time processing demands, and heterogeneous device ecosystems that characterize IoT deployments. Cloud-driven platforms offer scalable, flexible, and centralized solutions, yet integrating them with wireless IoT networks requires careful reengineering of foundational IT infrastructure. This article explores strategies for designing scalable, secure, and efficient cloud-enabled wireless IoT platforms. Key principles such as microservices-based architectures, edge computing, dynamic resource allocation, and robust security frameworks are discussed in detail. The article also examines cloud infrastructure models, data management techniques, performance optimization, and emerging technologies that enhance IoT capabilities, including AI, 5G/6G, and blockchain. Challenges related to legacy integration, interoperability, security, and sustainability are addressed, alongside recommendations for building resilient and future-ready systems. By providing a comprehensive framework for reengineering IT infrastructure, this work aims to guide organizations in deploying efficient, secure, and scalable wireless IoT platforms that can support the next generation of intelligent, connected applications.

DOI: http://doi.org/10.5281/zenodo.18169506

Published by: vikaspatanker

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This thesis examines the persistent rise of land-related conflicts and associated criminal activities in Nigeria, tracing their roots to historical, cultural, administrative, and governance-related inadequacies in the management of landed property. Land, traditionally communally owned and essential for livelihood, has evolved into a highly contested asset due to population growth, modernization, and weak implementation of the Land Use Act. The study highlights how ineffective administration, corruption, poor enforcement of regulations, and conflicting customary and statutory land rights have created conditions enabling violence, territorial claims, extortion, communal clashes, and other atrocities across the country. Materials and Methods: The research adopts a qualitative approach grounded in criminological theory, supported by documentary analysis, non-participant observation, and unstructured interviews. Data were sourced through long-term observational studies of land-related activities in communities, motor parks, markets, land registries, and informal settlements across Nigeria. A combination of cross-sectional and longitudinal designs enabled the researcher to observe patterns, behaviours, and criminal tendencies linked to land ownership struggles. Content analysis was used to interpret data within the theoretical framework of causes of crime—including cultural, economic, psychological, and environmental determinants. Results and Discussion: Findings reveal that inadequacies in land administration—such as corrupt allocation practices, weak enforcement of land regulations, multiple sales of land, extortion by traditional actors (e.g., “omo-onile”), unregulated territorial control, and government-enabled demolitions—have significantly fueled criminal activities. These include communal clashes, armed conflicts, thuggery, property destruction, kidnapping, territorial cultism, and conflict between farmers and herdsmen. The study establishes that such crimes persist largely because of institutional weaknesses, inconsistent policies, and failure to implement culturally sensitive, transparent systems of land governance. Conclusion: The study concludes that strengthening policy enforcement, enhancing governance structures, and implementing culturally aligned regulatory frameworks are essential to reducing land-related atrocities. Effective land administration and accountability at all levels will help curb crime, promote peace, and support sustainable national development.

DOI: https://doi.org/10.5281/zenodo.18169532

 

Published by: vikaspatanker

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