A Vital Research and State-of-the-Art Application in Artificial Intelligence into Smart Grids A. Rehash Rushmi Pavitra, V. Vijayalakshmi AI and Blockchain in Smart Grids Fundamentals Methods and Applications, 2025 The modernization of energy systems could potentially be revolutionized by incorporating artificial intelligence (AI) into smart grids. Moreover, distinct parameters such as efficient, accurate, sustainable uses of AI in smart grids are examined in the proposed research. In addition to this, emerging technologies that impact smart grids in the future, including neural networks, machine learning, deep learning, Internet of Things, and enhancing domain like AI are addressed respectively. Simultaneously, proposed research illustrates the revolutionary potential of AI in energy management and distribution through case studies and other real-time applications. Also discussed, an exciting future of smart and flexible and sustainable energy ecosystems ready to confront the challenges of an environment evolving in a rapid manner which is suggested towards the advancement of intelligent technologies in power systems automation and utilization of AI in smart grids. Finally, the development trend of intelligent technology for power systems automation is addressed along with distributed energy resources and intelligent real-time control system.
A Blockchain-Based Architecture for Secure and Decentralized Agricultural Supply Chain Management V. Vijayalakshmi, A. Rehash Rushmi Pavitra AI and Blockchain in Smart Grids Fundamentals Methods and Applications, 2025 Blockchain technology offers transformative potential for the agricultural sector by providing a decentralized, transparent, and secure system for recordkeeping and transaction verification. By eliminating the need for intermediaries, blockchain can enhance various aspects of agriculture, including supply chain transparency, food safety, and financial transactions. The technology’s inherent properties – such as immutability, enhanced security, and consensus mechanisms – make it well-suited to address challenges in modern farming, from reducing food-borne illnesses and fraud to improving financial transactions and market accessibility for farmers. Despite these advantages, the adoption of blockchain in agriculture faces significant barriers, including the need for extensive training, reliable information infrastructure, and the integration of IoT devices. This paper explores the historical development of blockchain, its applications in agriculture, the opportunities and benefits it offers, and the challenges that must be addressed to realize its full potential.
An AI-driven Approach for Tailoring Java Programming Instructions Gomathi Thiyagarajan, Vijayalakshmi V 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025 AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java instructions. ByteBuddy's framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes.
Powering Up e-Health: AI and Blockchain for a Smarter, Safer e-Health System R. Radha, S. Sharanya, V. Vijayalakshmi AI and Blockchain in Smart Grids Fundamentals Methods and Applications, 2025 Digitally preserved medical records that include details about an individual’s health are called electronic health records, or EHRs. Since EHRs are typically shared by various healthcare stakeholders, they can encounter issues including power outages, improper use of data, deprivation of discretion, protection, and audit trails, amid additional issues. The current principles behind handling medical data, their sharing mechanism, dispensation, analysis, and cataloguing conferring to results are being improved by using blockchain and artificial intelligence (AI) to produce creative and cutting-edge solutions. Blockchain is a reforming technology that made healthcare sector in collaboration with the Internet of Things (IoT) improve to a large extent in terms of elevated efficiency, control over resource access, technical advancement, secrecy protection, and safekeeping of active data operations. The unchangeability of data stored on a blockchain, among other features of blockchain technology, is of importance to the healthcare business. Many intriguing Internet of Things applications are being investigated. Internet of Things–based blockchain and AI has speed up the process involved in biological and clinical research and significantly improved biomedical and healthcare data ledgers. These assessments are predicated on fundamental characteristics of AI and blockchain technology, like improved security, resilience, decentralized administration, unchangeable audit trails, and above all, the reinstatement of charterers’ rights. Healthcare personnel will be able to view patient medical records on the blockchain. Artificial intelligence (AI) makes use of a wide range of suggested protocols, policymaking power, and enormous extents of data. While blockchain made it feasible to pileup the cryptographic traces that AI makes use of to make efficient and effective solutions. Thus, the systems that make use of medical records might experience an elevated service efficacy, decline in costs, and democratized healthcare by integrating the most contemporary developments in these technologies.
An Intelligent Framework to Monitor and Predict Land Temperatures Using Learning Algorithms S. Sharanya, D. Rajalakshmi, Rehash Rushmi Pavithra A., V. Vijayalakshmi, R. Radha Recent Advances in Georesources Intelligent Materials and Environmental Science, 2025 The Earth's climate is rapidly changing, and it has been projected that the global climate will continue to change even beyond this century. The magnitude of change directly relies on the quantity of greenhouse gases emitted by the mankind and the earth's sensitivity towards the emissions. The climate hazards will continue triggering interactive and accumulated impacts on the natural systems. Hence, it is quintessential to mitigate its adverse effect on the natural systems. An improved understanding about the ecosystem can be observed by predicting the global warming temperature and land surface warning. This work focuses on building an effective framework that leverages GRU cells for predicting the mean temperature of a region. The proposed work develops a comprehensive AI system for predicting the temperature by using previous and historic data. This system would benefit the community by accurately predicting the land temperature which is very crucial for wide variety of human activities.
Achieving green sustainability in Computing devices in machine learning and deep learning techniques S. Sharanya, V. Vijayalakshmi, R. Radha Computational Intelligence for Green Cloud Computing and Digital Waste Management, 2024 The accelerated growth in artificial intelligence, internet of devices, machine learning (ML), and deep learning at breakneck speed has attracted the attention of researchers in developing novel green solutions for reclaiming the green society. The intersection of these technologies with green sustainability will greatly impact the deployment of cutting-edge technologies with green solutions. Leveraging ML technologies to improve engineering techniques to reduce the toxins released in the environment in various forms is discussed in this work. The predominant area of focus is applying is developing green AI-based solutions with sustainability measures and metric in mind. The primary contribution of this work is the holistic analysis of the employment of green ML and deep learning techniques in fostering a sustainable environment. The potential scope of this research is to benefit the research community in developing novel ML and deep learning technologies for improving green sustainability.
Expert System based on Feature Selection for Classification of Self-Care Problems in Children and Youth People 13th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2022, 2022
Group based neural collaborative filtering for e- learning recommender system International Journal of Scientific and Technology Research, 2019
Educational data mining tools: A survey from 2001 to 2016 K. Venkatachalapathy, V. Vijayalakshmi, V. Ohmprakash Proceedings 2017 2nd International Conference on Recent Trends and Challenges in Computational Models Icrtccm 2017, 2017
