Dr. Vijayaraja V

Scopus Publications

IoT-Based Smart Home Energy Management System (SHEMS) using Networking and Automation
Arokia Martin N, Kirubakaran N, Senthil Kumar G, Selvaganesan C, Jegadeeshwari P, et al.
2025 International Conference on Data Science and Business Systems Icdsbs 2025, 2025
Physical energy meter reading concept is outdated and it is not efficient, leading to lots of wastage of manpower and errors, btaining accurate real-time readings is challenging and complex to achieve consistently. Current implementations of IoT technology allow real-time monitoring and energy management capabilities with the integration of IoT ecosystems, Digital Communication Technology networks, and Machine Learning algorithms to further analyze data as well as enhance user convenience. However, despite their promising potential, these systems face ongoing challenges, including adaptability, scalability, interoperability and enhanced cyber security resilience, particularly in large-scale deployments. This review examines present SHEMS and their capabilities, from strengths such as real-time tracking of energy usage, optimization and consumption of energy at offpeak hours by the application of predictive analytics using Machine Learning. This also elucidates areas for improvement, such as an improved integration of renewable energy technologies, simplification of the complexities surrounding large-scale deployment and sustainability over the long term. Addressing these deficits, this paper further promotes advancements in the design of SHEMS that are more secure, scalable and inter-operable, increasing communication across IoT ecosystems and by using smart grid technology, which could improve effective management of energy and a potential wider uptake. These suggestions are aimed at fine-tuning existing technologies to be easily integrated into modern, automated energy systems for better energy management.
E-Learning Privacy in the Context of Machine Learning
N. Kirubakaran, Varadha Rajan S, R. Parijatham, Ramya Bharathi K, P. Jegadeeshwari, et al.
2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025
The present paper discusses the privacy issues associated with the usage of machine learning in e-learning systems. Although ML is a promising technology that increases personalization and engagement, it also poses significant security concerns in terms of gathering and using sensitive data. E-learning platforms are vulnerable to problems such as breaches, unauthorized access, and misutilization of personal information. The paper addresses two major concerns: recommendation engines and predictive performance systems, which emphasize the weaknesses and shortcomings in current data privacy and protection measures. In an attempt to address these challenges, the paper develops a privacy-preserving framework based on federated learning and data anonymization technologies. Together, these technologies work in the decentralization of data storage and reduction of exposure yet provide personalized experiences. There is a comparative study that will analyze whether such solutions could help improve data privacy and at the same time maintain functionality in ML. Finally, a balance between security and user experience is brought into discussion, but this time it puts emphasis on the need to have personalization without sacrificing privacy. The final result is a framework for privacy that supports the fulfillment of laws like GDPR, while in the meantime maintaining the best of the personalization that this brings with the ML approach so that an effective solution towards modern e-learning systems can be availed.
ADVANCED LIGHTWEIGHT ST-TCN FRAMEWORK USING UAV MULTI-SPECTRAL REMOTE SENSING FOR SURVEILLANCE AND CONTROL OF PINE NEMATODE DISEASE
Journal of Theoretical and Applied Information Technology, 2024
Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT
V. Gokula Krishnan, D. Siva, S. Hemamalini, N. Sivakumar, V. Vijayaraja
International Journal on Recent and Innovation Trends in Computing and Communication, 2023
Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial for farmers to implement effective and technical solutions to raise production, lessen the impact of issues associated to animal husbandry, and improve agricultural yields. Because of technological developments in computers and data storage, huge volumes of information are now available. The difficulty of extracting useful information from this mountain of data has prompted the development of novel approaches and tools, such as data mining, that can help close the informational gap. To evaluate data mining methods and put them to use in the Animal database to create meaningful connections was the goal of the suggested system. The study's primary objective was to develop an IoT-based Integrated Animal Health Care System. Various sensors were used as the research tool to collect physical and environmental data on the animals and their habitats. Temperature, heart rate, and air quality readings were the types of information collected. This research contributes to the field of health monitoring by introducing an Optimised Bidirectional Gated Recurrent Neural Network approach. The BiGRNN is an improved form of the Gated Recurrent Unit (GRU) in which input is sent both forward and backward through a network and the resulting outputs are connected to the same output layer. Since the BiGRNN method employs a number of hyper-parameters, it is optimised by means of the Sparrow Search Algorithm (SSA). The originality of the study is demonstrated by the development of an SSA technique for hyperparameter optimisation of the BiGRNN, with a focus on health forecasting. Hyperparameters like momentum, learning rate, and weight decay may all be adjusted with the SSA method. In conclusion, the results demonstrate that the suggested tactic is more effective than the current methods.
Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction
Dr. V. Gokula Krishnan, Dr. M. V. Vijaya Saradhi, Dr. S. Sai Kumar, G. Dhanalakshmi, P. Pushpa, et al.
International Journal of Electrical and Electronics Research, 2023
The prevalence of cardiovascular diseases (CVD) makes it one of the leading reasons of death worldwide. Reduced mortality rates may result from early detection of CVDs and their potential prevention or amelioration. Machine learning models are a promising method for identifying risk variables. In order to make accurate predictions about cardiovascular illness, we would like to develop a model that makes use of transfer learning. Our proposed model relies on accurate training data, which was generated by careful Data Collecting, Data Pre-processing, and Data Transformation procedures.
Prediction & Forecasting of Flood Through Rainfall Measurement Using Support Vector Machine
SSSV Gopala Raju, J. Rajesh, Vijayaraja V, R. Thiagarajan, R. Krishnamoorthy, et al.
8th International Conference on Smart Structures and Systems Icsss 2022, 2022
It is been discovered that, as fake neural neighborhood styles, SVM also experiences over-fitting and underneath turning out to be issues and the over-getting is bigger unsafe than under-turning out to be. This paper outlines that a phenomenal inclination among a monstrous wide assortment of various enter combos and boundaries is a genuine endeavor for any modelers in the usage of SVMs. A differentiation with a couple benchmarking models has been made, for example trade capacity, pattern and Naive styles. It exhibits that SVM is fruitful of outperform everyone of them in the investigate records arrangement, at the cost of an immense volume of time and exertion. Dislike going before posted impacts, this paper demonstrates that direct and nonlinear part focuses (for example RBF) can yield top-quality exhibitions towards each uncommon under exceptional circumstances in a similar catchment. The investigate furthermore shows an exciting convey about the SVM response to incredible precipitation inputs, wherein lighter rainfalls may create particular reactions with high level which are tougher method when compared to other models to reveal the adaptation to SVM approach.
Retracted: Prediction & Forecasting of Flood Through Rainfall Measurement Using Support Vector Machine (2022 8th International Conference on Smart Structures and Systems (ICSSS) DOI: 10.1109/ICSSS54381.2022.10703401)
SSSV Gopala Raju, J. Rajesh, Vijayaraja V, R. Thiagarajan, R. Krishnamoorthy, et al.
8th International Conference on Smart Structures and Systems Icsss 2022, 2022
It is been discovered that, as fake neural neighborhood styles, SVM also experiences over-fitting and underneath turning out to be issues and the over-getting is bigger unsafe than under-turning out to be. This paper outlines that a phenomenal inclination among a monstrous wide assortment of various enter combos and boundaries is a genuine endeavor for any modelers in the usage of SVMs. A differentiation with a couple benchmarking models has been made, for example trade capacity, pattern and Naive styles. It exhibits that SVM is fruitful of outperform everyone of them in the investigate records arrangement, at the cost of an immense volume of time and exertion. Dislike going before posted impacts, this paper demonstrates that direct and nonlinear part focuses (for example RBF) can yield top-quality exhibitions towards each uncommon under exceptional circumstances in a similar catchment. The investigate furthermore shows an exciting convey about the SVM response to incredible precipitation inputs, wherein lighter rainfalls may create particular reactions with high level which are tougher method when compared to other models to reveal the adaptation to SVM approach.
Performance evaluation of energy efficient power models for digital cloud
Soumya Ranjan Jena, V. Vijayaraja, Aditya Kumar Sahu
Indian Journal of Science and Technology, 2016
Background: To improve quality of service energy-efficiency is one of the key parameters of Cloud service providers. Every year huge amounts of electrical energy consume by Cloud data center which leads to more expense in costs and emission of CO2 to the environment which is unhealthy for us. In this case the need of Green Cloud computing solutions to minimize emission of carbon footprints as well as operational costs is the utmost desire. Objectives: In our research work we have implemented four different power models such as linear model, cubic model, square model and square root model on an Infrastructure-as-a-Service (IaaS) Cloud environment to find out the best one. Methods: Here we considered the CPU utilization and power consumption by enabling virtual machine migration. Then to validate the accuracy of these power models R-squared, Mean Square Error (MSE) have been performed. Finding: We found out that the cubic polynomial model is the most efficient one and consume less power in comparison to the other three models. Application: Hence this model can be used in energy saving applications over Cloud data centers.
Sifting undesirable substance in online interpersonal organization in light of MLSOFT classifier
Arpn Journal of Engineering and Applied Sciences, 2015
Multi agent system based upstream congestion control in wireless sensor networks
European Journal of Scientific Research, 2011
Optimizing parallel concentric circle itinerary based KNN query processing in wireless sensor networks
J. Chempavathy, V. Vijayaraja
Proceedings of the 2nd International Conference on Trendz in Information Sciences and Computing Tisc 2010, 2010