Daily Archives: January 6, 2026

Authors: Ira Somketu

Abstract: The healthcare industry generates vast amounts of sensitive patient data daily, creating both opportunities and challenges for healthcare providers. Secure management and intelligent analysis of this data are critical for improving patient outcomes, operational efficiency, and regulatory compliance. SAP systems, widely used in healthcare, provide robust platforms for data storage, integration, and management; however, traditional implementations often struggle with unstructured data analysis, predictive insights, and advanced security requirements. The integration of Artificial Intelligence (AI) with SAP systems addresses these gaps by enabling real-time analytics, predictive modeling, anomaly detection, and automated security monitoring. This article explores the convergence of AI and SAP in healthcare, focusing on secure patient data management, AI-driven intelligence, implementation strategies, and emerging trends. Through AI-enhanced SAP systems, healthcare organizations can transform complex datasets into actionable insights while maintaining patient privacy, regulatory compliance, and operational excellence. The article also examines future directions, including real-time analytics, ethical AI, and the integration of emerging technologies such as blockchain and federated learning, highlighting the strategic importance of AI-powered patient data intelligence in modern healthcare ecosystems.

DOI: https://doi.org/10.5281/zenodo.18162131

 

Published by: vikaspatanker

Authors: Drupad Gowda N, GS Rajdeep, Puneeth Kumar GJ, Vignesh Shenoy R

Abstract: Videos are now used everywhere — in education, smart cameras, video calls, hospitals, industries, and home security. But whenever the internet becomes slow or the wireless signal becomes weak, the normal video transmission systems fail and the video quality becomes very poor. The MDVSC (Model Division Video Semantic Communication) technique solves this by transmitting only the meaningful information from video frames instead of sending the entire frame pixel by pixel. It carefully separates the information that stays the same across frames (like background or static objects) and the information that changes (like moving objects). Then, it sends only the important features first. Because of this, MDVSC provides better video clarity, uses less data, and continues working even when network strength is low.

Published by: vikaspatanker

Authors: Aarvik Bhatnagar

Abstract: Effective financial governance is critical for ensuring accuracy, transparency, compliance, and risk mitigation in enterprise resource planning (ERP) systems. SAP ERP provides robust financial and control functionalities; however, traditional governance mechanisms largely depend on static rule-based controls and manual audits, which are increasingly insufficient in handling high-volume, complex, and dynamic financial transactions. This paper proposes an intelligent financial governance approach for SAP ERP systems using hybrid machine learning models that combine rule-based logic, statistical methods, and advanced machine learning techniques. The proposed framework integrates seamlessly with SAP financial modules to enable real-time monitoring, anomaly detection, predictive risk assessment, and continuous compliance management. By leveraging hybrid model architectures, the approach balances adaptability and learning capability with transparency and regulatory interpretability. Practical use cases, including fraud detection, compliance monitoring, and predictive financial controls, demonstrate the effectiveness of the proposed solution. Experimental evaluation highlights the superiority of hybrid models over traditional rule-based and standalone machine learning approaches in terms of detection accuracy, false-positive reduction, and operational scalability. The findings indicate that hybrid machine learning models can transform financial governance in SAP ERP from a reactive control function into a proactive, intelligent, and strategic capability.

DOI: http://doi.org/10.5281/zenodo.18159759

Published by: vikaspatanker

Authors: Mehar Bediya

Abstract: The rapid expansion of the Internet of Things (IoT) and the increasing reliance on wireless connectivity have driven the integration of cloud computing into large-scale IoT ecosystems. While cloud-based solutions offer elastic resources and centralized management, the growing number of connected devices, heterogeneous workloads, and dynamic wireless conditions pose significant challenges in terms of scalability and adaptability. Traditional Cloud–IoT architectures often struggle to efficiently accommodate massive device connectivity, fluctuating data rates, and varying quality-of-service requirements. Consequently, there is a growing need for architectural designs that can dynamically scale resources and adapt system behavior in response to changing network and application conditions. This review paper provides a comprehensive analysis of scalable and adaptive Cloud–IoT ecosystem design for wireless networks. It examines foundational architectural models, wireless communication technologies, and cloud computing paradigms that support IoT deployments. The paper further investigates scalability challenges related to device density, data volume, network capacity, and resource provisioning, as well as adaptability mechanisms that enable context-aware, autonomous, and mobility-aware system operation. Key architectural approaches, including edge and fog computing, microservices, containerization, and serverless computing, are reviewed and compared. In addition, the paper discusses resource management, orchestration strategies, and critical considerations related to security, privacy, and reliability. Through a comparative analysis of existing solutions and an exploration of application domains, the review identifies current limitations, trade-offs, and open research challenges. The paper aims to guide researchers and practitioners in designing resilient, efficient, and future-ready Cloud–IoT ecosystems for dynamic wireless environments.

DOI: http://doi.org/10.5281/zenodo.18159755

Published by: vikaspatanker

Authors: Ira Chaturvedi

Abstract: The rapid digitization of corporate finance and the increasing complexity of global regulatory frameworks have necessitated a shift from manual oversight to intelligent automation. This review article investigates the integration of Machine Learning algorithms within the SAP S/4HANA ecosystem to enhance financial compliance and reporting efficiency. By leveraging the SAP Business Technology Platform, organizations can move beyond traditional rule-based systems to implement real-time anomaly detection, automated intercompany reconciliations, and predictive financial closing processes. The analysis explores the technical architecture required to bridge the gap between transactional data and autonomous governance, highlighting the role of the Universal Journal as a single source of truth. Furthermore, the article addresses the strategic challenges of data orchestration, the necessity of Explainable AI for auditability, and the emerging role of Natural Language Processing in interpreting unstructured regulatory documents. As financial reporting transitions toward a continuous monitoring model, the synergy between ERP robustness and machine intelligence becomes a critical factor in reducing operational risk and ensuring transparency. The findings suggest that while intelligent automation significantly reduces the manual burden of compliance, a human-in-the-loop approach remains essential for maintaining ethical oversight and professional judgment. Ultimately, this review provides a comprehensive framework for organizations seeking to leverage SAP and machine learning to transform the finance function into a proactive strategic asset.

DOI: http://doi.org/10.5281/zenodo.18159753

Published by: vikaspatanker

Authors: Kabir Sehgal

Abstract: The seamless integration of the Internet of Things (IoT) with Cloud Computing has revolutionized data-driven ecosystems, yet it has simultaneously birthed a sophisticated class of "Invisible Risks." Unlike traditional cyber threats that target known software vulnerabilities or hardware weaknesses, invisible risks emerge from the systemic complexity, algorithmic opacity, and "gray-zone" interactions inherent in distributed architectures. These risks including data shadowing, logic flaws in cross-protocol interoperability, and the silent propagation of algorithmic bias—often bypass conventional signature-based detection systems, remaining latent until they manifest as catastrophic failures. This review article proposes a comprehensive Conceptual Framework for Managing Invisible Risks by synthesizing multi-disciplinary research across cybersecurity, system engineering, and cognitive psychology. We categorize these risks across a four-tier architecture: the Perception, Network, Cloud, and Application layers. Each layer is analyzed to identify the "invisibility triggers" that obscure threat vectors from administrative oversight. Furthermore, the paper evaluates contemporary risk assessment methodologies, advocating for a transition from static monitoring to dynamic observability through the use of Bayesian Networks, Digital Twins, and Chaos Engineering. We propose a proactive management strategy anchored by three pillars: Zero Trust Architecture (ZTA), AI-driven Automated Governance, and Edge Intelligence. The framework aims to bridge the "transparency gap" in Cloud-IoT environments, providing researchers and practitioners with a structured roadmap to identify, quantify, and mitigate hidden threats. Finally, the article discusses future directions, including the role of blockchain for provenance and quantum-resistant cryptography, emphasizing that the future of Cloud-IoT security depends on our ability to make the invisible visible.

DOI: http://doi.org/10.5281/zenodo.18159648

Published by: vikaspatanker

Authors: Navya Kulshreshtha

Abstract: The rapid evolution of Industry 4.0 has necessitated a transition from traditional administrative Enterprise Resource Planning (ERP) to "Intelligent ERP" systems that leverage real-time operational data. This review article investigates the optimization of ERP performance through the integration of Machine Learning (ML)–based Predictive Maintenance (PdM) models. While traditional maintenance strategies within ERP namely reactive and preventive often lead to unplanned downtime or resource wastage, ML-based PdM offers a data-driven alternative that predicts equipment failure before it occurs. This study synthesizes current literature regarding the architectural integration of Industrial Internet of Things (IIoT) sensors with ERP modules, such as Asset Management, Production Planning, and Materials Management. We categorize the predominant ML methodologies including Supervised Learning for fault classification, Deep Learning (LSTM and GRU) for Remaining Useful Life (RUL) estimation, and Unsupervised Anomaly Detection evaluating their specific contributions to enterprise-level efficiency. The review highlights how PdM-driven insights directly optimize ERP Key Performance Indicators (KPIs) by reducing maintenance costs, streamlining spare parts inventory through Just-in-Time (JIT) procurement, and enhancing Overall Equipment Effectiveness (OEE). Furthermore, the article addresses critical implementation challenges, such as data silos, scalability, and the "black box" nature of AI models. By analyzing the synergy between predictive analytics and resource orchestration, this review provides a roadmap for researchers and practitioners to build resilient, self-optimizing industrial ecosystems. The findings suggest that the integration of ML-PdM is no longer a peripheral technical upgrade but a core strategic necessity for modern enterprise resource management, enabling a shift from descriptive reporting to prescriptive action.

DOI: http://doi.org/10.5281/zenodo.18159637

Published by: vikaspatanker

Authors: Shaurya Tomar

Abstract: The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has birthed a new generation of Clinical Decision Support Systems (CDSS) capable of real-time physiological monitoring. This review article examines the architectural and methodological shift from rule-based alerts to predictive AI engines that process high-frequency data from connected medical devices. We investigate the core pipeline of these systems—from signal denoising at the Edge to deep learning-based feature extraction in the Cloud—and evaluate how these technologies address the "data deluge" currently overwhelming clinical staff. The article provides a detailed taxonomy of AI methodologies, including Supervised Learning for diagnosis, Reinforcement Learning for treatment optimization, and the rising role of Explainable AI (XAI) in fostering clinician trust. Key clinical use cases are explored, ranging from early sepsis detection in the ICU to the management of chronic conditions like diabetes through closed-loop artificial pancreas systems. Furthermore, we address the critical barriers to adoption, specifically focusing on data quality, clinical alarm fatigue, and the "interoperability gap" between siloed medical systems. Finally, the review analyzes the 2025 regulatory landscape, including the impact of the EU AI Act and the FDA's evolving SaMD guidelines. We conclude that while AI-powered CDSS offers unprecedented potential for proactive care, its success depends on maintaining a "Human-in-the-Loop" approach, ensuring that AI augments rather than replaces clinical expertise.

DOI: http://doi.org/10.5281/zenodo.18159591

Published by: vikaspatanker

Authors: Prisha Malviya

Abstract: The rapid proliferation of Internet of Things (IoT) devices, coupled with the high-performance analytical capabilities of Cloud Computing, has created an interdependent ecosystem that relies heavily on wireless network infrastructures. However, this integration introduces significant security vulnerabilities, as the broadcast nature of wireless communication leaves data susceptible to jamming, eavesdropping, and sophisticated man-in-the-middle attacks. This review article systematically investigates the current landscape of Risk-Aware Cloud Computing Frameworks designed to secure IoT communications. We propose a multi-dimensional taxonomy that categorizes these frameworks based on their architectural distribution (Cloud-to-Edge), their risk-assessment methodologies (Probabilistic vs. AI-driven), and their decision-making logic (Reactive vs. Proactive). The article provides a deep dive into the "Resource-Security Paradox," analyzing how risk-aware models optimize the trade-off between cryptographic overhead and device longevity. Furthermore, we provide a comparative analysis of state-of-the-art frameworks, evaluating them against key performance metrics such as detection accuracy, latency, and energy efficiency. Significant attention is given to the role of Software-Defined Networking (SDN) and Trust Management Systems in providing real-time mitigation of wireless threats. Finally, the article identifies critical research gaps and discusses emerging trends, including Zero Trust Architectures (ZTA), Quantum-Resistant Cryptography, and the impact of 6G on IoT security. This review aims to provide a comprehensive reference for researchers and practitioners working to build resilient, self-adaptive security infrastructures for the future of the interconnected world.

DOI: http://doi.org/10.5281/zenodo.18159497

Published by: vikaspatanker

Authors: Rudra Narayan

Abstract: The exponential growth of data in healthcare and financial sectors presents unique challenges in storage, processing, and real-time analytics. High-velocity data streams—originating from electronic health records (EHRs), IoT medical devices, stock trading systems, and payment networks require sophisticated frameworks capable of handling large volumes with minimal latency. SAP HANA, an in-memory, columnar database platform, offers real-time processing capabilities that allow organizations to integrate advanced analytics and machine learning (ML) directly into transactional and operational data environments. By leveraging AI and ML, healthcare institutions can predict patient outcomes, optimize treatment plans, and enhance diagnostic accuracy, while financial organizations can detect fraud, assess risk, and execute high-frequency trading strategies efficiently. This review article explores the convergence of AI/ML techniques with SAP HANA for high-velocity data analytics, emphasizing both technical implementation and domain-specific applications. We provide an overview of SAP HANA’s architecture, predictive analytics libraries, and integration approaches with external ML frameworks such as Python, R, TensorFlow, and PyTorch. The article also examines real-time data pipelines, model deployment strategies, and key challenges, including data privacy, scalability, and model interpretability. Case studies in healthcare demonstrate predictive modeling for patient management, disease diagnosis, and imaging analytics, while financial applications highlight fraud detection, real-time risk assessment, and market analytics. Furthermore, the review discusses benefits such as reduced latency, improved decision-making, and operational efficiency, alongside limitations that include heterogeneous data integration, regulatory compliance, and model transparency. Finally, future research directions are outlined, including deep learning integration, edge computing for real-time analytics, hybrid cloud deployments, and explainable AI methodologies. This review serves as a comprehensive resource for researchers, practitioners, and decision-makers seeking to understand the potential of AI and ML integration within SAP HANA for processing and analyzing high-velocity healthcare and financial data efficiently and effectively.

DOI: http://doi.org/10.5281/zenodo.18159474

Published by: vikaspatanker