Authors: Raegan Alex. A, Jyothi Gupta

Abstract: Aim: This paper reviews urban expansion through space and time in Chennai city (a megapolis of India) based on GIS technology etc, and investigates ways to integrate blue-green infrastructure, i.e., wetlands, riverscapes, greenscapes into the development for climate adaptation, resilient cities & sustainable growth. Research Question: What can GIS show us about Chennai’s ancient land use changes and urban expansion? What are the possibilities of climate-proofing resilience and promoting sustainable urban development with blue-green infrastructure? Methodology: Ten research papers were analyzed, and core concepts were determined as well as gaps identified. The Shapefile/GIS data was obtained and studied to observe the pattern of city growth. For identifying potential areas for BGI and urban resilience interventions, the land use/land cover changes were mapped. Results: GIS based analysis identified speedy urbanization in and around OMR and GST Road at the cost of wetlands, agricultural land, and open spaces. Map of vegetation, water bodies showed areas to reclaim for restoration. The rivers, sewers, sumps and metro lines became avenues for blue-green infrastructure projects. Zones for planning interventions were determined using highway networks and administrative boundaries. A mapping of priority areas for flooding, urban cooling and sustainable development was conducted. Conclusion and Limitation: Urbanization of Chennai has shrunk green and blue spaces, a situation that emphasizes the urgency for incorporating blue-green infrastructure to improve climate resilience. Challenges that people faced around the country included access to up-to-date ward boundaries, transforming shapefiles and high prices of datasets. Sensitve areas for restoration and management had been successfully mapped if imperfectly served by maps

DOI: http://doi.org/10.5281/zenodo.17878089

Published by: vikaspatanker

Authors: Dr. Shahina Khan

Abstract: The integration of Artificial Intelligence (AI) in education has introduced transformative possibilities for enhancing learning experiences, particularly within inclusive educational settings. This study investigates AI-driven personalized learning strategies aimed at supporting diverse learner populations, including students with varying cognitive, physical, and socio-economic needs. By leveraging adaptive learning platforms, intelligent tutoring systems, and assistive technologies, AI enables individualized instructional pathways, real-time feedback, and enhanced learner engagement. Employing a mixed-methods approach, the study collects quantitative data through academic performance metrics and surveys, alongside qualitative insights from interviews and classroom observations. Findings indicate that AI interventions can significantly improve engagement, learning outcomes, and accessibility while highlighting challenges related to algorithmic bias, ethical considerations, and teacher readiness. The research underscores the importance of integrating AI with human-centered pedagogy, promoting hybrid models that balance technological personalization with socio-emotional and ethical dimensions of teaching. These findings offer actionable insights for educators, policymakers, and researchers aiming to implement AI-driven strategies that foster equity, inclusion, and academic success in diverse learning environments.

DOI: http://doi.org/10.5281/zenodo.17802421

Published by: vikaspatanker

Authors: Dr. Sanjeeta Kumari

Abstract: The rapid advancement of Artificial Intelligence (AI) technologies has created new opportunities for innovation in the education sector, particularly in supporting teachers in their professional responsibilities. With increasing demands on educators to balance instructional delivery, administrative work, student engagement, and continuous professional development, workload management has emerged as a critical concern that directly influences teacher well-being. This study explores the impact of AI tools on teacher workload and professional well-being, drawing attention to the ways in which automation, intelligent data processing, and adaptive learning systems are reshaping the daily realities of educators. AI-driven platforms are increasingly being utilized to streamline administrative duties such as grading, attendance tracking, scheduling, and report generation, thereby reducing the time teachers spend on repetitive tasks. In addition, intelligent tutoring systems and learning analytics provide data-driven insights into student progress, enabling teachers to design more targeted instructional strategies. By automating routine responsibilities, AI tools create space for educators to focus on meaningful interactions with students, personalized mentoring, and creative aspects of teaching. However, while the potential benefits are significant, the integration of AI into educational contexts also raises important challenges. Teachers are required to adapt to new digital environments, acquire technical competencies, and adjust to changing classroom dynamics shaped by AI-driven practices. Ethical considerations, such as data privacy, algorithmic bias, and the risk of over-reliance on technology, further complicate the discourse on AI adoption in schools and higher education institutions. The study emphasizes that teacher well-being cannot be understood solely in terms of workload reduction, but must also consider broader dimensions such as professional autonomy, job satisfaction, and psychological resilience. Evidence suggests that when AI tools are thoughtfully integrated within supportive institutional frameworks, they have the capacity to alleviate burnout, improve work-life balance, and promote a sense of professional empowerment among teachers. Conversely, poorly implemented AI systems risk reinforcing existing challenges by increasing dependence on technology without adequately addressing the human-centered needs of educators. Overall, the findings underscore the dual role of AI as both a facilitator of workload reduction and a catalyst for professional transformation. Successful integration requires continuous teacher training, collaborative decision-making, and clear policy guidelines to ensure that AI enhances rather than undermines educational practice. The study concludes that a balanced and ethical approach to AI adoption has the potential to not only reduce workload but also strengthen teacher well-being, thereby contributing to sustainable and inclusive educational development.

DOI: http://doi.org/10.5281/zenodo.17801805

Published by: vikaspatanker

Authors: Balbeer Prasad, Preeti, Shireen-e-Sadaf

Abstract: Artificial Intelligence (AI) is increasingly reshaping the landscape of education by offering innovative tools and personalized learning experiences that were previously unimaginable. This article explores a range of case studies that highlight the transformative potential of AI in various educational settings, from primary schools to higher education institutions. Through these case studies, the research examines how AI-driven technologies, including intelligent tutoring systems, adaptive learning platforms, and AI-based assessment tools, are enhancing student engagement, improving learning outcomes, and supporting educators in their instructional roles. The case studies presented demonstrate that AI facilitates personalized learning pathways by analyzing individual student performance data and tailoring content to meet unique learning needs. For instance, AI-powered platforms can provide immediate feedback, recommend resources, and adjust the complexity of tasks in real time, ensuring that learners progress at an optimal pace. Moreover, AI applications assist teachers in administrative and pedagogical tasks, such as automating grading, identifying knowledge gaps, and predicting students at risk of underperformance, thereby allowing educators to focus more on instructional interactions and mentorship. In addition to academic performance, the case studies reveal AI’s role in fostering inclusivity and accessibility. Tools leveraging natural language processing, speech recognition, and predictive analytics support students with diverse learning needs, including those with disabilities, by offering multimodal content delivery and real-time assistance. Despite the evident benefits, the article also addresses challenges observed across the case studies, including ethical concerns, data privacy issues, and the necessity for teacher training to effectively integrate AI technologies. By critically analyzing successes and limitations, the study underscores the importance of strategic implementation, continuous evaluation, and collaborative engagement between technologists, educators, and policymakers. Overall, the insights drawn from these case studies illustrate that AI is not merely a technological enhancement but a catalyst for reimagining educational experiences. By leveraging AI’s potential thoughtfully, educational institutions can cultivate more adaptive, efficient, and inclusive learning environments that meet the evolving needs of 21st-century learners. The findings serve as a guide for stakeholders seeking to harness AI responsibly and effectively to transform teaching and learning practices globally.

DOI: http://doi.org/10.5281/zenodo.17801455

Published by: vikaspatanker

Authors: Aditya Raj, Kajal Kumari, Krishna Kumar Roy, Ram Kumar Roy

Abstract: The integration of Artificial Intelligence (AI) into education has emerged as one of the most transformative developments of the 21st century, reshaping the ways knowledge is delivered, accessed, and assessed. This case study explores the practical applications, opportunities, and challenges associated with AI in educational contexts, with a particular focus on how intelligent systems influence teaching methodologies, learning experiences, and institutional management. By examining specific use cases such as adaptive learning platforms, automated assessment tools, personalized tutoring systems, and administrative support applications, this study highlights the multifaceted role of AI in fostering innovation within the classroom. One of the central findings of this case study is the capacity of AI to personalize learning experiences based on individual student profiles. Unlike traditional teaching methods, AI-driven platforms can analyze data on student performance, identify areas of strength and weakness, and adapt instructional content accordingly. This dynamic approach not only improves learner engagement but also enhances outcomes by ensuring that educational interventions are more targeted and efficient. Furthermore, AI supports teachers by automating routine tasks such as grading, scheduling, and attendance management, enabling educators to devote greater time to creative and interactive aspects of pedagogy. The case study also underscores the role of AI in promoting inclusivity. For students with diverse learning needs, including those with disabilities, AI-powered assistive technologies provide accessible pathways to education. Speech recognition, text-to-speech converters, and intelligent translation tools help break linguistic and physical barriers, ensuring that learning becomes more equitable. On the institutional side, AI contributes to evidence-based decision-making through predictive analytics, offering insights into student retention, curriculum development, and resource allocation. However, the research also acknowledges several challenges inherent in AI adoption within education. Concerns regarding data privacy, ethical use of student information, and the risk of over-reliance on technology are prominent. Additionally, unequal access to AI resources can exacerbate the digital divide between privileged and underprivileged learners. Hence, while AI presents immense potential to revolutionize education, its successful implementation requires careful planning, ethical safeguards, and an inclusive approach. In conclusion, this case study demonstrates that Artificial Intelligence is not merely a technological tool but a catalyst for educational transformation. By enabling personalization, supporting teachers, enhancing inclusivity, and optimizing institutional processes, AI has the power to redefine the future of learning. Nevertheless, balancing its opportunities with its challenges remains crucial to ensuring that AI serves as a force for equitable and sustainable progress in education.

DOI: http://doi.org/10.5281/zenodo.17801306

Published by: vikaspatanker

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