Daily Archives: July 22, 2025

Authors: Andrei Nikolayevich Petrovski, Ekaterina Leonidovna Sokolova, Vladislav Dmitrievich Morozov, Irina Sergeyevna Volkova

Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating prompt and accurate risk detection for timely intervention. This research presents a scalable artificial intelligence (AI) infrastructure designed to support real-time cardiovascular risk detection using streaming medical data. The proposed architecture integrates distributed data ingestion, edge AI processing, and cloud-based model orchestration to ensure both low-latency diagnostics and high system reliability. Using a combination of convolutional neural networks (CNNs) for ECG signal analysis and gradient-boosted trees for patient history correlation, the system demonstrates improved predictive accuracy. Performance benchmarks show efficient scaling across multiple nodes, enabling high-throughput analysis essential for deployment in emergency and critical care settings. The paper evaluates model deployment on Kubernetes, real-time data flow with Apache Kafka, and compliance with healthcare data privacy regulations. The study concludes with recommendations for integrating this AI infrastructure into hospital networks and telemedicine platforms.

DOI: https://doi.org/10.5281/zenodo.16317726

Published by: vikaspatanker

Authors: Andrei Nikolayevich Petrovski, Ekaterina Leonidovna Sokolova, Vladislav Dmitrievich Morozov, Irina Sergeyevna Volkova

Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating prompt and accurate risk detection for timely intervention. This research presents a scalable artificial intelligence (AI) infrastructure designed to support real-time cardiovascular risk detection using streaming medical data. The proposed architecture integrates distributed data ingestion, edge AI processing, and cloud-based model orchestration to ensure both low-latency diagnostics and high system reliability. Using a combination of convolutional neural networks (CNNs) for ECG signal analysis and gradient-boosted trees for patient history correlation, the system demonstrates improved predictive accuracy. Performance benchmarks show efficient scaling across multiple nodes, enabling high-throughput analysis essential for deployment in emergency and critical care settings. The paper evaluates model deployment on Kubernetes, real-time data flow with Apache Kafka, and compliance with healthcare data privacy regulations. The study concludes with recommendations for integrating this AI infrastructure into hospital networks and telemedicine platforms.

Published by: vikaspatanker

Authors: Guram Shalvovich Danelia, Nino Giorgievna Kalandadze, Levan Besarionovich Mchedlidze, Salome Iraklievna Tsereteli

Abstract: The convergence of artificial intelligence (AI) and health informatics has the potential to revolutionize clinical decision-making, disease surveillance, and personalized medicine. This study explores the integration of AI workflows with existing health informatics pipelines, examining both the transformative opportunities and the critical challenges associated with such integration. By analyzing case studies from electronic health record (EHR) systems, bioinformatics pipelines, and radiological imaging networks, we identify architectural patterns that enable seamless AI integration. Additionally, the research addresses the barriers posed by data heterogeneity, workflow fragmentation, regulatory compliance, and algorithm interpretability. The findings suggest that while AI offers immense benefits in improving healthcare outcomes and operational efficiency, a strategic, interoperable, and ethically grounded approach is necessary for scalable implementation in health informatics infrastructures.

DOI: https://doi.org/10.5281/zenodo.16315871

Published by: vikaspatanker

Authors: Sergey Artyomovich Mamedov, Yelena Ramizovna Isayeva, Anar Fikret oglu Mahmudov, Kamilla Rauf qizi Veliyeva

Abstract: Biomedical data centers serve as the backbone of modern healthcare analytics, precision medicine, and hospital informatics. As the volume of healthcare data surges, the need for scalable, secure, and efficient computing infrastructure becomes paramount. Server virtualization has emerged as a critical enabler in this space, offering resource abstraction, fault tolerance, and operational flexibility. This study performs a comparative assessment of leading server virtualization techniques—namely hypervisor-based (e.g., KVM, VMware ESXi), container-based (e.g., Docker, LXC), and hybrid models—based on key parameters such as performance, scalability, resource utilization, latency, and compliance with biomedical data handling norms. Benchmarks using real-world datasets, including EHRs and PACS workloads, reveal that no single approach dominates across all metrics, emphasizing the need for context-driven infrastructure design.

DOI: https://doi.org/10.5281/zenodo.16314536

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Authors: Rahmatulloh Tohirovich Saidov, Mavjuda Gafurovna Khudoerzoda, Akmal Ziyodulloevich Kholov, Shahrukh Nasrulloevich Nazarov, Dilnoza Mahmadaliyevna Qalandarova

Abstract: As genomic data repositories expand rapidly with the growing need for precision medicine and population-scale genomics, managing the integrity, security, and regulatory compliance of these repositories has become a paramount concern. This paper presents a compliance-centric approach to automating server infrastructure specifically tailored for genomic data management. We explore the integration of Unix-based server automation tools with security-first policies and standards such as HIPAA, GDPR, and ISO 27001. The study outlines how configuration management, access control automation, logging, and continuous compliance auditing are implemented to ensure operational resilience and regulatory alignment. By leveraging scripting, cron-based scheduling, and policy-as-code frameworks, genomic data infrastructures can be both scalable and secure. The proposed automation model reduces human error, enhances traceability, and allows for real-time response to compliance deviations, making it a critical foundation for modern biomedical computing environments.

DOI: https://doi.org/10.5281/zenodo.16313840

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Authors: Elen Rafayelovna Sargsyan, Hayk Vahagnovich Ghazaryan,, Anzhela Viktorovna Grigoryan, Karen Samvelovich Melikyan, Tatevik Aramovna Harutyunyan

Abstract: The explosive growth of genomic and proteomic datasets has propelled bioinformatics into the enterprise computing domain, demanding scalable, secure, and high-performance infrastructure. Traditional physical server models have proven inadequate for managing the dynamic and compute-intensive nature of bioinformatics workflows. In response, AI-powered virtualization models are emerging as transformative solutions, combining intelligent workload orchestration with flexible virtual environments. This paper investigates how artificial intelligence enhances virtualization strategies in enterprise bioinformatics settings by enabling predictive resource allocation, automated fault detection, and real-time optimization. Through architectural analysis and case study evaluation, the research presents a practical framework for deploying AI-integrated virtual infrastructure that meets the evolving needs of large-scale biological computation.

DOI: https://doi.org/10.5281/zenodo.16312734

Published by: vikaspatanker

Authors: Natalia Ivanovna Baranova, Dmitry Alekseevich Tikhonov, Yulia Sergeevna Pankratova, Ivan Mikhailovich Rogozin

Abstract: Electronic Health Records (EHRs) are foundational to modern healthcare systems, but they are also lucrative targets for cyberattacks due to the sensitivity of medical data. Ensuring the confidentiality, integrity, and availability of EHRs requires robust server-level defenses. This study investigates the implementation of adaptive Unix server hardening models tailored for healthcare environments. It outlines a layered approach to security that integrates dynamic configuration baselines, continuous monitoring, and compliance mapping to standards like HIPAA, HITRUST, and NIST. Through adaptive hardening strategies, including automated shell scripts, auditing frameworks, and anomaly detection, we propose a defense-in-depth model that significantly enhances EHR security posture. Real-world use cases and benchmarks validate its practicality in live healthcare infrastructures.

DOI: https://doi.org/10.5281/zenodo.16312367

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Authors: Niloofar Farrukhzoda Rajabova, Daler Bahromovich Toshmatov, Sherzod Mahmudzoda Nasimov, Aziza Akbarzoda Komilova

Abstract: As enterprises increasingly migrate to cloud-native platforms like Salesforce, the security of multi-tenant environments becomes paramount, particularly in regulated industries. Salesforce’s multi-tenancy architecture provides scalability and cost-efficiency, but also raises concerns around data isolation, regulatory compliance, and shared infrastructure risks. This article offers a compliance-oriented examination of Salesforce security in multi-tenant clouds, exploring the architecture, built-in controls, shared responsibility models, and strategies for adhering to regulations such as GDPR, HIPAA, and SOC 2. By aligning platform capabilities with compliance mandates, organizations can ensure secure operations without sacrificing agility and innovation.

DOI: https://doi.org/10.5281/zenodo.16312240

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Authors: Nargiz Eldar qizi Aliyeva, Kamran Vidadi oglu Mustafayev, Lala Elshan qizi Mammadova, Emil Rovshan oglu Gurbanov

Abstract: – In the era of hyper-personalized customer engagement, businesses are increasingly turning to cloud-based Business Intelligence (BI) systems integrated with Cognitive Customer Relationship Management (CRM) models to gain competitive advantage. Cognitive CRM extends traditional CRM by embedding AI capabilities such as natural language processing, machine learning, and sentiment analysis to generate deeper insights from structured and unstructured data. This article explores the practical application of Cognitive CRM within cloud-based BI ecosystems, focusing on architecture, integration strategies, real-time analytics, and decision automation. It highlights case studies where companies have successfully leveraged these models to optimize customer retention, improve service personalization, and boost operational efficiency, while also addressing challenges like data privacy, system complexity, and model governance.

DOI: https://doi.org/10.5281/zenodo.16311390

 

Published by: vikaspatanker

Authors: Professor Nikita Bante, Professor Uday Mahure, Professor Prajakta Helonde, Professor Radha Yete, Professor Aachal Aakre

Abstract: The advent of Agentic AI systems—AI entities that possess autonomy, contextual awareness, and adaptive learning capabilities—has revolutionized the landscape of software development. Unlike traditional rule-based automation tools, agentic AI can perform high-level cognitive functions, including code generation, optimization, debugging, and collaborative task execution without constant human oversight. This paper explores the role of agentic AI in automating various phases of the software development lifecycle (SDLC), from requirements gathering to deployment and maintenance. The research highlights the growing integration of Large Language Models (LLMs), multi-agent systems, and self-improving codebases. It discusses how these intelligent agents enhance developer productivity, reduce time-to-market, and minimize manual coding errors. Through a blend of empirical evidence, recent technological advancements, and case studies, the study showcases the operational and strategic implications of adopting agentic AI. It further identifies potential challenges, such as security risks, interpretability, over-reliance, and ethical dilemmas. The goal is to contribute to a better understanding of how agentic systems are reshaping software engineering practices and to offer practical recommendations for integrating these tools in development workflows responsibly and efficiently.

DOI: https://doi.org/10.5281/zenodo.16316781

Published by: vikaspatanker