Category Archives: Uncategorized

Authors: Anirudh Bora

Abstract: The proliferation of the Internet of Things (IoT) within the modern corporate landscape has necessitated the development of wireless communication models that are not only high-performing but also inherently secure and capable of massive scaling. This review article investigates the architectural evolution of wireless IoT frameworks designed for integration with cloud-enabled enterprise applications. We analyze the taxonomy of current communication protocols, ranging from short-range mesh topologies like Zigbee and Thread to Low-Power Wide-Area Networks (LPWAN) and 5G cellular IoT, evaluating their trade-offs in terms of power consumption, range, and data throughput. Central to this study is a structured three-tier architecture that utilizes edge gateways and cloud-native orchestration platforms such as SAP Business Technology Platform or AWS IoT Core to manage data ingestion, protocol translation, and digital twin synchronization. The article highlights critical strategies for scalability, including zero-touch automated provisioning and hierarchical spectrum management, which are essential for managing global device fleets. Furthermore, we address the rigorous security requirements of the enterprise perimeter, advocating for a zero-trust architecture and application-layer end-to-end encryption to mitigate the risks associated with decentralized wireless nodes. By synthesizing current implementation methodologies with emerging trends, such as 6G-enabled ambient IoT and quantum-resistant cryptography, this research provides a strategic roadmap for organizations aiming to build resilient, hyper-connected ecosystems. Ultimately, the study demonstrates that the synergy between robust wireless hardware and elastic cloud backends is the foundational requirement for maintaining operational agility and data integrity in the age of digital transformation.

DOI: http://doi.org/10.5281/zenodo.18229125

Published by: vikaspatanker

Authors: Prithan Deka

Abstract: Financial uncertainty has become a constant in the global economy, rendering traditional, static ERP reporting insufficient for strategic steering. This article proposes an "Intelligent Framework" that integrates the transactional robustness of SAP S/4HANA with advanced Machine Learning (ML) models to manage and mitigate financial risks proactively. By leveraging SAP Business Technology Platform (BTP) for model deployment and SAP Analytics Cloud (SAC) for multi-scenario simulations, the framework allows for real-time stress testing and predictive cash flow management. We explore the application of Deep Learning (LSTM) for volatility forecasting and Gradient Boosting for credit risk assessment, emphasizing the importance of Explainable AI (XAI) for regulatory compliance. The study demonstrates that by moving from deterministic to stochastic modeling, organizations can significantly reduce liquidity buffers and improve the accuracy of rolling forecasts. We conclude by addressing the ethical implications of AI in finance and the emerging role of Generative AI in automating risk reporting. This framework provides a strategic roadmap for CFOs to transform their finance organizations into resilient, data-driven intelligence hubs.

DOI: http://doi.org/10.5281/zenodo.18229084

Published by: vikaspatanker

Authors: Kavyansh Nath

Abstract: The exponential growth of data volume and complexity in the enterprise and healthcare sectors has rendered traditional data warehousing techniques insufficient for high-performance analytics. This review article investigates the emergence of AI-assisted data warehousing as a transformative paradigm for modern data management. We evaluate the integration of machine learning across the entire data lifecycle, specifically focusing on AI-driven ETL processes for automated schema mapping and the ingestion of unstructured clinical data. The study examines advanced performance optimization techniques, including reinforcement learning for autonomous query tuning and predictive resource scaling. In the context of healthcare, we analyze how these techniques facilitate longitudinal patient records, real-time clinical decision support, and accelerated drug discovery. Furthermore, we address the critical domains of security and compliance, highlighting AI-based data masking and anomaly detection for fraud prevention. By discussing emerging trends such as self-driving warehouses and generative AI interfaces, this article provides a strategic framework for organizations seeking to implement resilient, intelligent, and high-speed analytical cores. Ultimately, we demonstrate that AI-assisted warehousing is the essential foundation for turning massive datasets into actionable strategic and clinical intelligence.

DOI: http://doi.org/10.5281/zenodo.18229082

Published by: vikaspatanker

Authors: Reyvik Taluk

Abstract: The modern healthcare landscape is defined by the critical need to optimize operational efficiency while mitigating complex financial risks. Traditional on-premise systems are increasingly inadequate for handling the high-velocity data required for real-time institutional decision-making. This review article investigates the role of Cloud-Based Decision Support Systems (CDSS) as a transformative solution for managing healthcare operations and financial stability. We examine how cloud architectures, utilizing standards like HL7 and FHIR, enable the integration of disparate data sources—from electronic health records to supply chain logs. The study explores analytical models for patient flow optimization, staffing resource management, and revenue cycle enhancement, demonstrating their impact on institutional throughput and cash flow. Furthermore, we address the significant hurdles of data privacy (HIPAA/GDPR), cybersecurity, and the ethical requirement for Explainable AI. By synthesizing current research with emerging trends like digital twins and generative AI for executive briefings, this article provides a strategic roadmap for healthcare leaders. Ultimately, we demonstrate that the synergy between cloud scalability and proactive data analytics is the essential foundation for building resilient, sustainable, and patient-centric healthcare organizations in a digitally connected age.

DOI: http://doi.org/10.5281/zenodo.18229034

Published by: vikaspatanker

Authors: Ishvik Reddy

Abstract: Traditional credit scoring methods often fail to capture the multi-dimensional complexities of modern financial risks, particularly in volatile markets and for borrowers with limited credit histories. This review article investigates the integration of Machine Learning (ML)-based credit scoring models within the SAP financial and banking ecosystem. We evaluate the transition from legacy logistic regression scorecards to advanced ensemble methods like XGBoost and Random Forests, implemented through the SAP HANA Predictive Analytics Library (PAL) and SAP Business Technology Platform (BTP). The study highlights how the "embedded" and "side-by-side" architectural patterns in SAP S/4HANA enable real-time, data-driven credit decisioning by processing transactional data at the source. Furthermore, the article addresses the critical requirement for Explainable AI (XAI) using SHAP and LIME to meet regulatory standards like Basel IV and GDPR. We explore diverse use cases, including retail loan automation, dynamic corporate credit limit management, and SME financing via alternative data. The study concludes by discussing the future impact of Generative AI and Quantum Machine Learning on credit risk reporting and simulation. By synthesizing technical implementation strategies with financial risk theory, this paper provides a strategic roadmap for banks aiming to deploy transparent, accurate, and high-performance scoring systems within their enterprise landscape.

DOI: http://doi.org/10.5281/zenodo.18229000

Published by: vikaspatanker

Authors: Aarush Naidu

Abstract: The burgeoning growth of the Internet of Things (IoT) in healthcare has created a massive influx of data that traditional cloud-based architectures struggle to process with the required speed. Latency in medical monitoring can be catastrophic, leading to delayed responses in life-critical situations such as cardiac events or falls. This exploratory study investigates fog computing as a decentralized solution for reducing latency in IoT-based healthcare systems. We evaluate a three-tier architecture that positions a fog layer between medical sensors and the cloud to enable real-time data filtering, anomaly detection, and immediate localized alerting. The article explores key latency-reduction strategies, including dynamic resource allocation and intelligent computation offloading, which prioritize emergency traffic and minimize network congestion. Furthermore, we address the critical domains of security and privacy, highlighting the use of mutual authentication and local data anonymization to protect sensitive patient records. Through various case studies, we demonstrate that fog architectures can reduce response times by up to 95% compared to cloud-only models. The study concludes by identifying open research challenges in mobility management and interoperability, providing a strategic vision for the future of low-latency, resilient healthcare infrastructures.

DOI: http://doi.org/10.5281/zenodo.18228957

Published by: vikaspatanker

Authors: Charvik Konda

Abstract: The rapid transformation of the global healthcare industry from a reactive, hospital-centric model to a proactive, continuous, and patient-centered paradigm is driven by the convergence of wearable technology, artificial intelligence, and enterprise-grade data management. This review article explores the development and implementation of smart monitoring systems that utilize AI-driven analytics integrated within the SAP ecosystem to provide high-fidelity, real-time patient care. By bridging the technical gap between medical-grade biosensors and the SAP Business Technology Platform, healthcare providers can now harness the in-memory computing power of SAP HANA to process massive streams of physiological data. The study investigates how advanced machine learning algorithms, including deep learning for predictive modeling and anomaly detection, transform raw sensor data into actionable clinical insights. These capabilities enable early detection of critical conditions such as sepsis or cardiac distress while minimizing false alerts through intelligent context-aware filtering. We examine diverse clinical applications ranging from post-operative recovery and chronic disease management to elderly care and clinical trials demonstrating significant improvements in patient outcomes and institutional resource optimization. Furthermore, the article addresses the multifaceted challenges of large-scale deployment, specifically focusing on data privacy under HIPAA and GDPR, the technical complexity of ERP integration, and the necessity of explainable AI for clinical trust. By discussing emerging trends such as edge intelligence and the integration of generative AI for enhanced patient engagement, this review provides a strategic framework for health systems. Ultimately, the synergy between wearable hardware and SAP-integrated analytics represents a cornerstone for a more accessible, personalized, and resilient digital healthcare infrastructure.

DOI: http://doi.org/10.5281/zenodo.18228874

Published by: vikaspatanker

Authors: Narasimhamurthy K N, Ashok G V, Ashwini S

Abstract: In view of this, Indoor as well as outdoor radon concentration measurement has been carried out in specific residential and schools located in Mandya, Karnataka using well known SSNTD technique. The indoor radon level is predicted in the same selected dwellings using the suitable model which is based on the mass balance equation and the results are compared with the measured values. Annual mean values of 222Rn in selected houses and schools were found to be 19.68 Bq m-3 respectively. Annual mean values in some other survey for 222Rn and 220Rn concentrations was found to be 22.4 and 24.1 Bq m-3 respectively. The total annual effective dose received by the general public due to radon and thoron is found to be 1.1 mSv y-1, which is close to the Indian average value of 1.11 mSv y-1. The doses to different organs and tissues were calculated using the ICRP model of the respiratory tract and inter comparison was discussed.

DOI: https://doi.org/10.5281/zenodo.18228616

 

Published by: vikaspatanker

Authors: Pranesh Mudiraj

Abstract: The integration of advanced machine learning models into SAP ERP systems has revolutionized the traditional landscape of financial planning and analysis by shifting organizational focus from reactive reporting to proactive forecasting. This review article evaluates the transition from manual, spreadsheet-based accounting toward automated predictive frameworks that leverage the in-memory computing power of SAP S/4HANA. We examine a diverse taxonomy of algorithms, ranging from classical time-series analysis and ensemble methods to sophisticated deep learning architectures, and their specific applications in revenue projection, cash flow management, and risk mitigation. The study details the technical synergy between the SAP Business Technology Platform and embedded analytical engines, emphasizing the importance of data preprocessing and feature engineering in a complex enterprise environment. Furthermore, we provide a comparative analysis between traditional and machine-learning-based forecasting, highlighting improvements in accuracy, cycle time, and scalability. The paper concludes by discussing emerging trends such as generative AI and real-time predictive accounting, offering a strategic roadmap for financial leaders aiming to implement data-driven decision-making processes. By synthesizing current methodologies and practical use cases, this study demonstrates how predictive analytics serves as a cornerstone for the modern intelligent enterprise.

DOI: http://doi.org/10.5281/zenodo.18221566

Published by: vikaspatanker

Authors: Nivaan Varma

Abstract: As global business environments become increasingly volatile, traditional enterprise risk management frameworks struggle to keep pace with high-velocity, interconnected disruptions. This review article investigates the integration of artificial intelligence into risk management lifecycles to enhance business resilience. We examine how machine learning, natural language processing, and predictive analytics transform the stages of risk identification, assessment, and mitigation from reactive to proactive processes. The study highlights the role of AI in critical domains such as cybersecurity, supply chain elasticity, and financial stability, while also addressing the theoretical shift toward the anticipate-absorb-recover-adapt cycle of resilience. Furthermore, the article explores the significant challenges associated with AI adoption, including model opacity, data bias, and the urgent need for explainable AI and human-in-the-loop governance. By synthesizing current research with emerging trends like generative AI and quantum-resistant modeling, we provide a strategic roadmap for organizations aiming to build antifragile systems. This study concludes that the synergy between human strategic judgment and machine intelligence is the fundamental requirement for maintaining long-term survivability in the digital age.

DOI: http://doi.org/10.5281/zenodo.18221560

Published by: vikaspatanker