Daily Archives: December 6, 2025

Authors: Mohammed Khan, Jyoti Gupta

Abstract: This study conducts a geospatial analysis of Channapatna’s urban fabric, focusing on the spatial distribution and interrelationship of green spaces, water bodies (blue infrastructure), and transportation networks. Leveraging Google Maps and other mapping tools, the paper identifies the placement and accessibility of parks, urban lakes, river systems, and transit corridors within the town. Findings reveal a landscape shaped by both ecological assets—such as Shettahalli and Kudlur lakes—and robust connectivity via road and rail, highlighting critical roles in urban quality, economic activity, and environmental sustainability. This research presents a comprehensive geospatial analysis of Channapatna’s green spaces, water bodies, and transportation infrastructure, using Google Maps and other spatial mapping tools to generate a nuanced urban profile. The study systematically maps the distribution and accessibility of public parks, open areas, lakes, and rivers, assessing their impact on land use, environmental quality, and urban well-being. Through NDVI and Air Quality Index analysis, the research highlights disparities in green space allocation, emphasizing their role in city resilience, ecological health, and recreation. The examination of Channapatna’s blue infrastructure uncovers significant deterioration: key water bodies like Shettahalli and Kudlur Lakes, once lifelines for agriculture and community use, now face acute pollution and encroachment. Extensive sewage inflow, lack of Underground Drainage (UGD) systems, encroachment, and unregulated dumping threaten water quality, agricultural productivity, and public health. The study reviews recent policy interventions and ongoing planning efforts—including proposals for a dedicated Sewage Treatment Plant (STP) and expansion of UGD—framing these within the broader context of sustainable urban management.

DOI: http://doi.org/10.5281/zenodo.17876819

Published by: vikaspatanker

Authors: Buddhika YPAS

Abstract: This conceptual paper discusses the role of HR digitalization in the performance of SMEs in the context of agility, efficiency, and innovation. Combining the Resource-Based View, Dynamic Capabilities Theory and Technology Acceptance Model, the framework defines the HR digitalization as a strategic resource that converts human capital into competitive advantage. The research hypothesizes eight research propositions which connect HR digitalization with performance results via mediating variables of employee engagement and organizational agility and are modulated by digital leadership, resource limitation, and institutional contexts. The results provide a theoretical understanding and practical recommendations to SME leaders and policymakers to use digital HR systems to benefit their sustainable growth and competitiveness in the digital economy.

Published by: vikaspatanker

Authors: Ramesh L. Subedi

Abstract: The integration of Artificial Intelligence (AI) and automation into Unix and Linux system governance has revolutionized how system administrators manage, monitor, and optimize infrastructure. These traditionally command-driven systems are now empowered with intelligent tools capable of adaptive learning, self-optimization, and proactive management. AI enhances performance monitoring, predictive maintenance, anomaly detection, and resource allocation, while automation streamlines repetitive administrative tasks, reducing human error and improving efficiency. Together, they establish a self-sustaining governance model that adapts dynamically to workloads and evolving cybersecurity challenges. This review explores the foundational concepts of AI integration, automation frameworks, and adaptive governance mechanisms within Unix and Linux environments. Furthermore, it examines their impact on performance, scalability, and compliance, alongside discussing real-world implementations and future perspectives. As enterprises shift towards AI-driven operations, the adaptive governance of Unix and Linux systems emerges as a vital frontier, blending intelligence with reliability to build resilient, autonomous computing infrastructures.

DOI: http://doi.org/10.5281/zenodo.17840389

Published by: vikaspatanker

Authors: Chathura Weerasinghe

Abstract: The integration of machine learning (ML) into multi-cloud architectures has revolutionized the way organizations manage and allocate resources dynamically. Traditional static allocation models often fail to address the variability and unpredictability of workloads across heterogeneous cloud environments. ML-driven systems enable proactive, data-driven decisions that optimize cost, performance, and reliability. By leveraging predictive analytics, reinforcement learning, and adaptive algorithms, resource utilization can be adjusted in real time to meet service-level agreements (SLAs) efficiently. Moreover, ML enhances automation, reduces human intervention, and mitigates latency or overprovisioning issues. This review explores the methodologies, frameworks, and benefits of ML-based resource allocation within multi-cloud infrastructures, highlighting the evolving role of artificial intelligence in managing distributed computing environments. It also discusses major challenges, including data privacy, model interpretability, and cross-cloud interoperability, while outlining future research directions aimed at building intelligent, self-optimizing multi-cloud systems.

DOI: http://doi.org/10.5281/zenodo.17840359

Published by: vikaspatanker

Authors: Aditi Ramanathan

Abstract: Federated learning (FL) has emerged as a transformative framework for building artificial intelligence (AI) models without directly sharing raw data among servers or organizations. Traditional cloud-based AI architectures rely on centralized data aggregation, where sensitive information is collected from multiple users and stored in one location for model training. This process, while effective in producing high-performance models, exposes critical vulnerabilities in data security, privacy, and ownership. Federated learning addresses these challenges through decentralized model training—allowing multiple devices or silos to collaboratively learn a shared model while keeping the raw data localized. Each participant trains the global model using its local dataset and transmits only model parameters or gradients to a central aggregator. This mechanism reduces the risk of data leakage or misuse and aligns with rising privacy regulations like GDPR and HIPAA. The approach is especially valuable in healthcare, finance, and telecommunications, where data privacy is not only ethical but legally enforced. Advances in encryption, secure aggregation, and differential privacy augment FL’s resilience against adversarial attacks. However, challenges still persist, including communication overhead, system heterogeneity, and the threat of malicious model updates. Integrating FL with cloud infrastructures introduces new paradigms for balancing computational efficiency and regulatory compliance. This synergy transforms traditional centralized machine learning pipelines into privacy-preserving distributed ecosystems. The evolution of FL also influences edge computing, enabling low-latency, privacy-aware learning closer to data sources. With ongoing research in adaptive aggregation protocols and homomorphic encryption, FL stands poised to redefine the standards of privacy-preserving AI. Its adoption marks a significant step toward responsible AI ecosystems where intelligence develops collaboratively without compromising the confidentiality of user data.

DOI: http://doi.org/10.5281/zenodo.17840325

Published by: vikaspatanker

Authors: Lakshmi Narayanan

Abstract: Container security solutions have become a critical element in modern software development practices, particularly within DevOps lifecycle management. As organizations increasingly adopt containerization to accelerate application delivery and enhance scalability, the need to secure container environments from development to deployment has intensified. Container security encompasses a range of practices designed to protect containerized applications and their underlying infrastructure from vulnerabilities, misconfigurations, and runtime threats. Integrating robust container security protocols into the DevOps lifecycle not only mitigates risks but also streamlines workflows through the adoption of DevSecOps principles, where security is ingrained early and continuously throughout the pipeline. This proactive stance addresses challenges such as image vulnerabilities, unauthorized access, and runtime compromises that traditional security models often overlook due to the ephemeral and dynamic nature of containers. Moreover, effective container security enables resilience by allowing swift rollback and containment of insecure components without disrupting ongoing operations. The impact of these solutions extends beyond risk reduction; they facilitate faster, more reliable software releases by embedding automated security checks, runtime monitoring, and access controls within continuous integration and continuous delivery (CI/CD) workflows. As a result, organizations can maintain both high velocity and strong security postures in competitive DevOps environments. This article thoroughly examines the multifaceted influence of container security solutions on DevOps lifecycle management, exploring current best practices, technological frameworks, challenges, and the evolving role of security automation in achieving secure, agile software development. It aims to provide a comprehensive overview for practitioners and decision-makers seeking to harness container security to improve operational efficiency and safeguard modern application ecosystems.

DOI: http://doi.org/10.5281/zenodo.17840280

Published by: vikaspatanker

Authors: Mehreen Alam Siddiqui

Abstract: The Border Gateway Protocol (BGP) and Open Shortest Path First (OSPF) are among the most critical routing protocols used in modern network infrastructures. Their combined redundancy mechanisms play a pivotal role in enhancing network availability and fault tolerance. BGP ensures stable routing between autonomous systems (inter-domain), while OSPF maintains reliable intra-domain communication through hierarchical design and link-state updates. When these protocols are configured with redundancy—using multiple routers, diverse paths, and failover systems—they minimize downtime, improve load distribution, and provide seamless recovery from link or node failures. This review explores how redundancy within BGP and OSPF can strengthen the resiliency of enterprise and service provider networks. It discusses architectural designs, convergence mechanisms, implementation strategies, and comparative performance in fault-prone environments. Furthermore, it highlights how integrating both protocols with redundancy optimizes large-scale, multi-domain networks to achieve near-continuous connectivity and operational stability

DOI: http://doi.org/10.5281/zenodo.17840229

Published by: vikaspatanker

Authors: Nirmal Singh Rathore

Abstract: Automation pipelines have become central to improving the efficiency, reliability, and scalability of continuous integration and continuous deployment (CI/CD) practices within modern software development environments. The rapid evolution of agile methodologies and DevOps culture has intensified the demand for faster, more consistent delivery cycles that maintain high standards of software quality. Automated pipelines orchestrate processes from code integration to final deployment by minimizing manual intervention, mitigating human errors, and ensuring reproducibility. These systems allow for seamless integration of version control, testing frameworks, configuration management, and deployment mechanisms. Such efficiency translates into shorter release cycles, enhanced collaboration, improved response to changes, and reduced operational overheads. Automation pipelines facilitate adaptive scaling, where integration and deployment tasks respond dynamically to workload variations, optimizing both resource usage and system performance. They also embed compliance and security checks into workflows, promoting governance without delaying delivery. Furthermore, continuous monitoring within automated pipelines enables predictive issue detection and proactive maintenance, supporting the stability of deployed applications. This paper explores the multifaceted impact of automation pipelines on CI/CD efficiency, emphasizing measurable improvements in deployment frequency, lead time reduction, change failure rates, and mean time to recovery (MTTR). Through an examination of design principles, toolchain economics, architectural integration, and cultural adaptation, it offers a holistic analysis of how automation shapes continuous delivery ecosystems. Finally, it discusses the future trajectory where artificial intelligence, machine learning-driven analytics, and infrastructure-as-code (IaC) models will deepen the automation of integration and deployment, paving the way for autonomous software delivery systems capable of enhanced decision-making and self-optimization.

DOI: http://doi.org/10.5281/zenodo.17840156

Published by: vikaspatanker

Authors: Farhana Yasmin

Abstract: The growing complexity of hybrid infrastructures, combining on-premises and cloud systems, demands advanced monitoring frameworks capable of handling dynamic, large-scale environments. Traditional rule-based monitoring solutions often fail to detect subtle or novel anomalies that emerge in such heterogeneous ecosystems. Artificial Intelligence (AI)-enhanced system monitoring has revolutionized anomaly detection by integrating machine learning, predictive analytics, and automation into network and system surveillance. This review explores the mechanisms, benefits, and challenges of AI-driven anomaly detection in hybrid infrastructures. It discusses how AI techniques such as deep learning, unsupervised clustering, and neural networks improve accuracy, speed, and contextual understanding in detecting irregular patterns. Furthermore, the paper evaluates hybrid monitoring architectures, data-driven models, and predictive capabilities that support proactive maintenance and security resilience. The review concludes by emphasizing AI's transformative role in achieving intelligent, adaptive, and self-healing IT operations within hybrid environments.

DOI: http://doi.org/10.5281/zenodo.17840111

Published by: vikaspatanker

Authors: Rashmi K. Nair

Abstract: Artificial intelligence (AI) has rapidly become the linchpin for modern cloud management, especially in the orchestration of hybrid cloud environments that span both public and private infrastructures. AI-driven orchestration leverages advanced algorithms, including machine learning and predictive analytics, to transform traditional, manually operated workflows into dynamically optimized, autonomous cloud ecosystems. This paradigm shift addresses persistent challenges such as operational complexity, resource inefficiency, and the need for real-time decision-making. By intelligently automating workload distribution, scaling resources predictively, enhancing security through anomaly detection, and enabling self-healing of cloud infrastructure, AI fundamentally redefines resource utilization across hybrid cloud platforms. Organizations adopting AI-driven orchestration experience not only improved performance and reduced costs but also increased responsiveness and operational reliability. Through continuous analysis of historical and real-time data, AI delivers actionable insights for optimal resource allocation, reduces human error, and positions businesses to respond proactively to fluctuating demands and evolving threats in the cloud. This article delves into the mechanisms and impacts of AI-powered orchestration, exploring its transformative potential for efficiency, scalability, and security in heterogeneous cloud environments. Key implementation strategies, challenges, and future directions are examined, illustrating how AI-driven orchestration is shaping the future of cloud computing for enterprises worldwide.

DOI: http://doi.org/10.5281/zenodo.17840060

Published by: vikaspatanker