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This work deals with classifying dog behavior using motion sensors, leveraging a transformer-based Deep Neural Network (DNN) model. Through experimentation and optimization, our model demonstrates superior performance with an impressive accuracy rate of 98.5%, outperforming time-series DNN models. The model’s efficiency is further highlighted by its reduced computational complexity, lower latency, and smaller size, making it well-suited for real-time deployment. Preprint, IEEE,
PeDistNet: Revolutionizing Distance Estimation for Pedestrian using Smartphone: A Deep Dive into Advanced LSTM and Convolutional Model Architecture with SOTA performance. Walking while smartphone in the pocket for 493 meters with only 0.06% error. Medium post
Transfer learning is the process of using skills and knowledge, that have been learned in one situation to solve a different, related problem. Medium post
AI-based voice synthesis technologies and their increasing influence on cybersecurity practices. Medium post
ChatGPT is the most popular LLM, compared to Bard, Copilot, Bing AI, and Claude.