Daily Archives: January 13, 2026

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