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Feature extraction is performed using SSCET, capturing key signal dynamics for radar, biomedical and speech-related tasks. The system supports cross-environment recognition while preserving user privacy and improving generalization. Classification is handled by EPTANN, whose parameters are optimized by the Bitterling Fish Optimization Algorithm (BFOA). Implemented in Python, the framework is evaluated against CNN, ANN, and GNN-based baselines. Experimental results show that EPTANN-BFOA achieves 95\u2013100% accuracy, significantly outperforming state-of-the-art models.<\/jats:p>","DOI":"10.1142\/s0218194025500561","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T08:30:33Z","timestamp":1757665833000},"page":"487-503","source":"Crossref","is-referenced-by-count":0,"title":["Advanced IoT-Driven Human Activity Recognition and Real-Time Localization using Deep Neural Networks: A Comprehensive Approach for Intelligent and Context-Aware Systems"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5966-2349","authenticated-orcid":false,"given":"M. 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