Authors: Saroj Singh

Abstract: The integration of Artificial Intelligence (AI) into teaching methodology is reshaping traditional classroom strategies, opening new pathways for innovation, personalization, and efficiency in education. AI technologies such as adaptive learning platforms, intelligent tutoring systems, automated assessment tools, and data-driven analytics are gradually transforming how teachers design, deliver, and evaluate learning experiences. Unlike conventional methods that often rely on uniform approaches, AI introduces the capacity to customize learning content according to individual student needs, learning pace, and preferred styles, thereby fostering inclusivity and enhancing engagement. Teachers are increasingly able to shift their roles from knowledge transmitters to facilitators and mentors, using AI-generated insights to guide interventions, provide targeted support, and cultivate higher-order thinking skills. The transformative impact of AI in classroom strategies is visible across multiple dimensions. Firstly, AI supports differentiated instruction by offering personalized pathways that address the strengths and weaknesses of diverse learners. Secondly, real-time feedback and automated grading save valuable instructional time, enabling teachers to focus more on interactive, student-centered activities. Thirdly, predictive analytics help identify at-risk students early, empowering educators to implement timely interventions. Additionally, AI-driven immersive tools, including virtual reality and natural language processing applications, enrich learning environments and make complex concepts more accessible. However, the integration of AI into teaching also raises critical challenges such as data privacy, ethical considerations, teacher preparedness, and equitable access to digital resources. This article explores how AI is redefining teaching methodologies by aligning technological innovation with pedagogical goals. It emphasizes the dual role of AI as both a supportive assistant for teachers and a personalized guide for students. The discussion highlights examples of AI applications in curriculum delivery, assessment, and classroom management, while also acknowledging limitations and areas for future research. By transforming classroom strategies, AI not only enhances the effectiveness of teaching but also repositions education as a dynamic, learner-centered process. The study concludes that while AI cannot replace the human element in teaching, it can significantly complement and enrich the educational experience when thoughtfully integrated into pedagogy.

DOI: http://doi.org/10.5281/zenodo.17814824

Published by: vikaspatanker

Authors: Ritesh Kumar

Abstract: The integration of Artificial Intelligence (AI) in education has transformed traditional instructional methods by enabling real-time data-driven personalization of learning. This qualitative case study investigates the implementation of an AI-powered personalized learning platform within a blended learning environment at a private secondary school in Bengaluru, India. The study aims to explore how AI supports personalized learning in practice, the experiences of students and teachers using the system, and the broader implications for pedagogy, curriculum, and educational equity. Blended learning—combining face-to-face instruction with digital platforms—has gained traction in recent years, especially with the rise of hybrid learning post-COVID-19. Within this context, AI promises a transformative potential to analyze individual learning patterns and provide customized pathways for student progress. However, the successful integration of AI tools into everyday teaching remains a challenge, particularly in diverse educational contexts. This study adopts a qualitative case study design to provide in-depth insight into how AI can both support and complicate the goals of personalized learning. Data were collected through semi-structured interviews with six secondary school students, three teachers, and one administrator; classroom observations during AI-facilitated sessions; and analysis of related documents such as lesson plans and platform analytics. Thematic analysis was used to code and interpret qualitative data, focusing on key themes such as learner engagement, teacher adaptation, infrastructural readiness, and ethical concerns around data use. Findings indicate that AI facilitated adaptive learning, increased learner autonomy, and allowed for differentiated instruction that better met the needs of both high-achieving and struggling students. Teachers reported a shift in their roles—from content deliverers to learning facilitators—which many found empowering but also challenging due to limited professional development. While students appreciated the gamified and interactive nature of the platform, some experienced anxiety when faced with continuous feedback or algorithm-driven performance tracking. Several barriers to effective implementation were identified, including inconsistent access to digital devices, unreliable internet connectivity, and concerns over student data privacy. Furthermore, the importance of aligning AI outputs with curriculum objectives and local pedagogical practices was emphasized. Ethical considerations, particularly the opaque nature of algorithmic decisions and the lack of digital literacy among students, emerged as critical areas needing attention. This study concludes that while AI can significantly enhance personalized learning within blended environments, it is not a one-size-fits-all solution. The findings offer valuable implications for educators, policymakers, and ed-tech developers committed to responsible and inclusive use of AI in education.

DOI: http://doi.org/10.5281/zenodo.17814800

Published by: vikaspatanker

Authors: Priyadharshini P, M.Gayathri

Abstract: This research paper presents a comprehensive implementation and evaluation of the Single Image Denoising Diffusion Model (SinDDM) for sophisticated image editing and synthesis tasks using only a single training image. Unlike conventional diffusion-based generative models that rely on extensive datasets, SinDDM employs an innovative multi-scale training strategy to learn hierarchical priors from a single input image. The model supports a wide range of image manipulation tasks, including artistic style transfer, semantic image harmonization, region-of-interest (ROI) guided editing, and CLIP-based text-guided content generation. Experimental results demonstrate that SinDDM consistently produces coherent, high-quality, and semantically aligned outputs without requiring extensive training data or pre-trained encoders, making it particularly suitable for personalized applications and data-efficient computational scenarios. This paper provides detailed architectural insights, implementation methodologies, comparative analysis, and potential applications of the proposed framework

DOI: http://doi.org/10.5281/zenodo.17862616

Published by: vikaspatanker

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