Authors: Deenanath Yadav

Abstract: The integration of artificial intelligence (AI) into educational environments has introduced new possibilities for collaborative learning, transforming the traditional classroom into a more connected and intelligent ecosystem. Collaborative learning, grounded in social constructivist theories, emphasizes knowledge sharing, peer-to-peer engagement, and co-construction of understanding. However, conventional methods often face challenges such as unequal participation, limited personalization, and constraints in real-time feedback. AI technologies have the potential to mitigate these limitations by offering adaptive learning pathways, intelligent tutoring systems, and analytics-driven insights that enhance collaboration. This paper explores the role of AI in advancing collaborative learning, focusing on its ability to build smarter and connected classrooms. The discussion begins with an overview of the theoretical underpinnings of collaborative learning and the emerging applications of AI in education. A literature review synthesizes existing research, highlighting AI-enabled tools that foster interaction, personalization, and equitable participation. Methodologically, the proposed work suggests a hybrid AI framework that leverages natural language processing, machine learning, and learning analytics to create an adaptive collaborative environment. This framework emphasizes inclusivity, knowledge co-creation, and real-time feedback loops to enhance both group and individual learning outcomes. The paper argues that AI not only augments teaching practices but also reshapes classroom dynamics by empowering learners to actively participate in a collective knowledge-building process. Additionally, challenges such as ethical considerations, data privacy, and digital equity are critically examined. The conclusion underscores that AI’s potential in education lies not in replacing teachers but in amplifying human intelligence, creating opportunities for richer collaborative experiences. By embedding AI into the pedagogical fabric of classrooms, educators can foster connected, participatory, and future-ready learning communities. This study contributes to the ongoing discourse on educational innovation, proposing a pathway toward smarter classrooms where AI and human collaboration intersect to enhance learning outcomes.

DOI: http://doi.org/10.5281/zenodo.17800912

Published by: vikaspatanker

Authors: VD Sasank, Vishnuvel Ragavan K E C, Dr. R. Prema

Abstract: Human–Computer Interaction (HCI) is rapidly transitioning from conventional interfaces to intelligent, context-sensitive systems driven by Cognitive Computing and Natural Language Processing (NLP). Traditional input–output interactions lack the capability to understand user intent, emotions, and behavioural patterns. Cognitive computing enables machines to simulate human mental processes such as perception, reasoning, and learning, while NLP supports natural communication through speech and text. This paper presents an integrated cognitive–NLP architecture for adaptive and human-centred interaction. A detailed literature review highlights existing HCI limitations, including lack of emotional understanding, multilingual constraints, system bias, and poor contextual reasoning. A proposed hybrid model is introduced, combining behavioural sensing, cognitive modelling, semantic processing, sentiment analysis, and feedback-driven learning. Applications in healthcare, accessibility, virtual assistants, smart environments, and education are examined. The paper concludes with challenges in ethics, privacy, and data bias, followed by future advancements such as emotion-aware agents, multilingual cognition, and real-time brain–computer interfaces.

DOI: https://doi.org/10.5281/zenodo.17800823

Published by: vikaspatanker

Authors: Priya D. Banerjee

Abstract: The increasing demand for real-time decision analytics in modern enterprises has accelerated the development of edge-to-cloud data pipelines, which integrate distributed computing resources to enable instantaneous insights. Traditional centralized cloud architectures struggle with latency and bandwidth limitations, making them unsuitable for applications requiring immediate decision-making. Edge-to-cloud pipelines overcome these barriers by combining localized data processing with cloud-based intelligence, creating a continuous, adaptive flow of analytical information. This review examines the architectural principles, technological enablers, and analytical impacts of edge-to-cloud data pipelines on real-time decision-making. It explores how distributed processing, stream analytics, and AI-driven orchestration enhance responsiveness, reliability, and scalability across diverse environments. Technologies such as 5G, machine learning, and containerized orchestration platforms are discussed as key drivers of this transformation. The study also identifies challenges including data synchronization, security, interoperability, and energy efficiency at the edge. Addressing these issues is essential for realizing seamless, end-to-end analytics across hybrid ecosystems. Future directions highlight the potential of autonomous, decentralized, and quantum-enhanced data pipelines to deliver self-optimizing intelligence at global scale.Ultimately, this review concludes that edge-to-cloud data pipelines are foundational to achieving context-aware, predictive, and autonomous analytics, enabling organizations to transition from reactive operations to real-time, intelligent decision ecosystems.

DOI: http://doi.org/10.5281/zenodo.17800105

Published by: vikaspatanker

Authors: Ravi C. Menon

Abstract: Cloud computing’s global adoption has revolutionized data management, but it has also intensified concerns regarding data confidentiality and security. Traditional encryption models, characterized by static configurations and fixed cryptographic policies, struggle to address the dynamic threat landscape of modern cloud environments. This review examines the impact of adaptive encryption algorithms—intelligent, context-aware mechanisms capable of dynamically modifying encryption parameters based on real-time risk assessments—on enhancing cloud data confidentiality.The paper explores the architectural principles, operational dynamics, and technological enablers of adaptive encryption, emphasizing its integration with AI-driven analytics, blockchain-based key management, and quantum-resistant cryptography. By analyzing its applications across multi-cloud, hybrid, and edge infrastructures, the review demonstrates how adaptive encryption fosters continuous, context-sensitive data protection.Despite its advantages, adaptive encryption also faces challenges including computational overhead, algorithm transparency, and interoperability across heterogeneous cloud environments. Addressing these barriers requires a balance between automation, explainability, and governance to ensure sustainable adoption. The study concludes that adaptive encryption signifies a pivotal evolution in cloud security—transforming static encryption models into self-learning, resilient, and proactive defense systems capable of anticipating and countering emerging threats.

DOI: http://doi.org/10.5281/zenodo.17800103

Published by: vikaspatanker

Authors: Dr. P.Gowsalya, M.Revathi, K.Palanisamy, S.Saranya

Abstract: The corrosion inhibition efficiency of Aster chinensis extract on mild steel in 1 M HCl was examined using weight loss and electrochemical techniques. The extract significantly reduced the corrosion rate, and inhibition performance increased with increasing inhibitor concentration. Polarization studies confirmed a mixed- type inhibition behaviour, while EIS results showed higher charge transfer resistance, indicating strong adsorption of phytochemical constituents on the steel surface. Adsorption obeyed the Langmuir isotherm, suggesting monolayer formation. Surface characterization by FT-IR and SEM supported the formation of a protective film on the metal surface. The extract also facilitated the reduction of Ag⁺ to Ag⁰ nanoparticles, confirming its dual function as a green corrosion inhibitor and an effective reducing agent. Overall, Aster chinensis demonstrates excellent potential as an eco-friendly and sustainable corrosion inhibitor.

DOI: https://doi.org/10.5281/zenodo.17800101

Published by: vikaspatanker

Authors: Lalitha M. Rao

Abstract: The accelerating adoption of multi-cloud strategies has underscored the critical need for system interoperability, enabling seamless integration, portability, and unified governance across heterogeneous cloud environments. This review examines the pivotal role of cross-cloud orchestration tools in achieving that interoperability by harmonizing operations among diverse providers such as AWS, Azure, and Google Cloud. It explores how orchestration systems, through automation, abstraction, and policy enforcement, mitigate the challenges of fragmentation, vendor lock-in, and operational inconsistency. The paper discusses foundational concepts including Infrastructure as Code (IaC), containerization, service mesh architectures, and API unification, illustrating how these technologies collectively underpin interoperability. It also analyzes the limitations—such as standardization gaps, security concerns, and data latency—that currently impede the realization of seamless multi-cloud integration. Furthermore, the review highlights emerging trends, including AI-driven orchestration, edge-cloud integration, and open-source frameworks, that promise to enhance orchestration intelligence and autonomy. By synthesizing technological insights and practical implications, the study concludes that cross-cloud orchestration not only enables interoperability but also fosters organizational agility, scalability, and resilience in the face of digital complexity. It positions orchestration as a strategic enabler of the next generation of adaptive, intelligent, and secure multi-cloud ecosystems capable of evolving with dynamic enterprise needs.

DOI: http://doi.org/10.5281/zenodo.17799984

Published by: vikaspatanker

Authors: Nitin S. Kurup

Abstract: The accelerating sophistication of cyber threats has driven a paradigm shift from traditional signature-based defenses to intelligent, adaptive security frameworks powered by Artificial Intelligence (AI). Among these innovations, AI-enhanced endpoint protection has emerged as a pivotal mechanism for safeguarding organizational digital assets and ensuring resilience against evolving attacks. This review explores the multifaceted impact of AI-driven endpoint security systems on organizational resilience, emphasizing how machine learning, behavioral analytics, and automated remediation collectively enhance detection accuracy, response speed, and recovery capability. The study begins by examining the fundamentals of AI-based endpoint protection, detailing how technologies such as deep learning, natural language processing, and predictive modeling redefine threat detection and mitigation. It further analyzes how AI-driven security fosters organizational resilience through proactive threat anticipation, self-healing mechanisms, and real-time situational awareness. Comparative evaluations of leading AI-powered solutions—such as CrowdStrike Falcon, SentinelOne, Microsoft Defender, and Sophos Intercept X—illustrate substantial improvements in operational continuity and risk tolerance. Despite these advancements, challenges persist, including data bias, model transparency, adversarial AI, and ethical considerations surrounding automated decision-making. Addressing these issues is critical for sustainable and trustworthy adoption. Future research directions point toward federated learning, explainable AI, and quantum-resilient cybersecurity as pathways to more intelligent and ethical endpoint protection systems.

DOI: http://doi.org/10.5281/zenodo.17799978

Published by: vikaspatanker

Authors: Meena P. Subramanian

Abstract: As global enterprises increasingly rely on artificial intelligence (AI) to drive decision-making, they face growing challenges related to data sovereignty, privacy, and regulatory compliance. Traditional AI models rely on centralized data aggregation, often violating regional data protection laws such as GDPR, PDPB, and China’s Data Security Law. Federated AI—a decentralized learning approach—has emerged as a solution that enables organizations to train AI models collaboratively without transferring raw data across borders. This review explores how federated AI influences data sovereignty in global enterprises by balancing innovation with compliance. It presents the underlying principles of federated learning, detailing its architecture, operational workflow, and privacy-preserving mechanisms. The analysis highlights how federated AI ensures compliance through decentralized data governance, secure aggregation, and encryption-based privacy protection. It further discusses regulatory alignment across jurisdictions and real-world applications in sectors such as healthcare, finance, and telecommunications. The paper also identifies major challenges including communication overhead, data heterogeneity, model inversion risks, and the absence of global interoperability standards. Comparative analysis demonstrates that while centralized AI offers efficiency and simplicity, federated AI provides superior compliance, resilience, and user trust—key attributes for multinational enterprises operating under diverse legal frameworks.

DOI: http://doi.org/10.5281/zenodo.17799859

Published by: vikaspatanker

Authors: Harish V. Reddy

Abstract: In a rapidly digitalizing world, identity verification has become the cornerstone of secure online interaction. Traditional authentication models, which depend on centralized authorities and password-based systems, are increasingly vulnerable to breaches, identity theft, and data manipulation. Blockchain-backed identity systems offer a promising alternative by decentralizing trust, ensuring immutability, and empowering users with self-sovereign control over their credentials. This review explores how blockchain technology enhances authentication reliability through decentralization, cryptographic assurance, and automation. The paper first examines the fundamentals of blockchain-based identity management, including decentralized identifiers (DIDs), verifiable credentials (VCs), and smart contracts that automate credential verification and revocation. It then presents the architectural components of blockchain identity systems, highlighting how cryptographic hashing, distributed consensus, and off-chain storage combine to create secure yet compliant authentication workflows. The analysis demonstrates that blockchain-backed identity frameworks significantly improve authentication reliability by removing single points of failure, enhancing data integrity, and enabling privacy-preserving verification through mechanisms like zero-knowledge proofs. Comparative evaluation with traditional systems reveals that blockchain ensures superior resilience, transparency, and user control, albeit with challenges in scalability, interoperability, and key management.

DOI: http://doi.org/10.5281/zenodo.17799857

Published by: vikaspatanker

Authors: Sneha R. Ghosh

Abstract: In today’s hyperconnected digital environment, cyber threats have evolved in complexity, persistence, and scale, challenging the effectiveness of conventional, reactive defense mechanisms. Traditional cybersecurity tools such as firewalls, intrusion detection systems, and antivirus software largely depend on signature-based or rule-driven models that detect known attacks but fail to identify novel, polymorphic, or zero-day threats. As a result, enterprises increasingly require security systems that not only detect and respond to breaches but also anticipate and prevent them proactively. Predictive Security Analytics (PSA) has emerged as a transformative approach within this context, integrating artificial intelligence (AI), machine learning (ML), big data analytics, and behavioral modeling to forecast potential cyber incidents before they occur. PSA operates by continuously analyzing massive volumes of structured and unstructured data from network traffic, endpoint logs, user behavior, and external threat intelligence to identify anomalies, correlations, and early indicators of compromise. By applying advanced statistical learning and pattern recognition, predictive models can uncover subtle deviations that signify emerging threats, enabling organizations to implement countermeasures preemptively. The incorporation of automation and real-time analytics empowers security teams to respond faster and with greater precision, significantly reducing false positives and improving overall cyber resilience. This review explores the impact of predictive security analytics on mitigating cyber threats, outlining its foundational principles, operational architectures, and major applications in enterprise and cloud environments. It contrasts predictive analytics with traditional reactive defense mechanisms, emphasizing its capacity to enhance situational awareness, optimize incident response, and support risk-based decision-making.

DOI: http://doi.org/10.5281/zenodo.17799756

Published by: vikaspatanker