N. Thillaiarasu’s research while affiliated with Reva University and other places

Demand Response (DR) has become a key strategy for enhancing energy system sustainability and reducing costs. Deep Learning (DL) has emerged as crucial for managing DR's complexity and large data volumes, enabling near real-time decision-making. DL techniques can effectively tackle challenges such as selecting responsive users, understanding consumption behaviours, optimizing pricing, monitoring and controlling devices, engaging more consumers in DR schemes, and determining fair remuneration for participants. This research work presents an integrated architecture for smart grid energy management, combining a Deep Attention-Enhanced Sequence-to-Sequence Model (AES2S) with Energy-Aware Optimized Reinforcement Learning (EAORL). The objective is to design a system that performs non-intrusive load monitoring and optimizes demand response to enhance energy efficiency while maintaining user comfort. The AES2S module accurately performs appliance state identification and load disaggregation using convolutional layers, Enhanced Sequence-to-Sequence Model networks, and an attention mechanism. The EAORL module employs a multi-agent system, where each agent uses a Deep Q-Learning Network to learn optimal policies for adjusting energy consumption in response to grid conditions and user demand. The system uses an Iterative Policy Update mechanism, where agents update their policies sequentially, ensuring stable and effective learning. The integration ensures seamless data flow, with AES2S outputs enhancing EAORL state representations. Validated in a simulated smart grid environment, the architecture dynamically adjusts energy consumption, demonstrating significant improvements in energy efficiency, cost reduction, and user comfort. Evaluation metrics confirm the system's effectiveness, making AES2S-EAORL a robust solution for smart grid energy management and demand response optimization.

Deep learning-based handwriting personality prediction uses neural networks (CNNs) to consider and predict personality characters from handwritten text to improve applications such as adaptive technology and automated data access. This method requires high computational resources and widely classified data, and its accuracy may suffer from signature variation and overfitting. This study introduces a novel optimizeddeep learning for Hand Written Personality Prediction. First the input data is collected from givendataset and an enhanced Gaussian filter is used in pre-processing to improve image quality by reducing noise and increasing contrast.After pre-processing, features are extracted by PCA-based normalized GIST, which standardizes the features to improve image representation.These extracted features are generated using an adaptive horse herd optimization algorithm to select the most important features. Based on selected features Improved Generative Adversarial Networks with artificial hummingbird optimization (IGAN_AHb) enables fast and efficient convergence of GANs. This reduce the mode collapse and ensures training with consistently fine-tuned parameters.The selected features are considered by characters such as openness, conscientiousness, extroversion, agreeableness and neuroticism, and finally classified using the IGAN_AHb classifier. In the result section, the proposed model is compared with various models with the matrix precision, recall, F1-score. The proposed model attained the value of 97.3% of precision and a recall rate of 96%, respectively. By comparing with other models, the proposed model attained highest values.

Automated identification of human activities remains a complex endeavor, particularly in unique settings like temple environments. This study focuses on employing machine learning and deep learning techniques to analyze human activities for intelligent temple surveillance. However, due to the scarcity of standardized datasets tailored for temple surveillance, there is a need for specialized data. In response, this research introduces a pioneering dataset featuring Eight distinct classes of human activities, predominantly centered on hand gestures and body postures. To identify the most effective solution for Human Activity Recognition (HAR), a comprehensive ablation study is conducted, involving a variety of conventional machine learning and deep learning models. By integrating YOLOv4’s robust object detection capabilities with ConvLSTM’s ability to model both spatial and temporal dependencies in spatio-temporal data, the approach becomes capable of recognizing and understanding human activities in sequences of images or video frames. Notably, the proposed YOLOv4-ConvLSTM approach emerges as the optimal choice, showcasing a remarkable accuracy of 93.68%. This outcome underscores the suitability of the outlined methodology for diverse HAR applications in temple environments.