A Comparative Study of Deep Learning Architectures for Activity Recognition
Rajashree Manjulalayam, Bhuman Vyas, Ripalkumar Patel, Amit M. Goswami, Hirenkumar Kamleshbhai Mistry, Chirag Mavani · 2024
This study introduces an advanced deep learning model, showcasing its superior performance against established architectures like CNNs, LSTMs, and Transformers in the realm of predictive analytics. The Proposed model outperforms existing benchmarks, achieving an 84.09% accuracy, 86% precision, and 85% F1-score, while substantially reducing the False Rejection Rate to 16.53%. Its robustness is validated by a consistent Receiver Operating Characteristic score of 0.99. The results advocate for the model’s application in advanced predictive tasks, setting a new standard for accuracy and reliability.