Abstract
In India, adultery is punishable by up to five years in prison under Section 497 of the Indian Penal Code, 1860. The first reaction upon seeing reality is one of shock at the State's blatant intrusion into what appear to be private sexual spheres. Specifically, to determine whether there are any moral justifications for making adultery a crime. My focus is on the paper's main argument, which is that the Legislature should repeal Section 497 because it, among other things, enacts detrimental gender segrWearable consumer technology has advanced to a point where it now dominates the healthcare industry. In complex IoT environments, there is a constant need for reliable recognition of diverse human behaviors. Applications in healthcare will subsequently be integrated with the knowledge gained from these recognition models. The four stages of the suggested framework are application creation, performance analysis, deep learning model deployment, and dataset and processing utilization. The study made use of the most recent KU-HAR database, which contained ninety individuals' eighteen distinct activities. Following preprocessing, a hybrid model that combines the architectures of the Gated Recurrent Unit (GRU) and Extreme Learning Machine (ELM) is employed. The robustness of human activity recognition in the Internet of Things environment is then further improved by the inclusion of an attention mechanism. Lastly, the suggested model's performance is assessed and contrasted with that of the traditional LSTM, GRU, ELM, Transformer, and Ensemble algorithms. Ultimately, the Qt framework is used to create an application that can be installed on any consumer device. With an overall accuracy of 96.71%, the suggested ELM-GRUaM model outperformed previous models in identifying multimodal human activitiesegation.
Keywords
Artificial intelligence, consumer electronics, deep learning, healthcare, human activity recognition, IoT, multimodal data
Introduction
Intelligent Decision Support Systems (IDSS) offer practical answers to a number of the problems that the world is currently facing. The extensive use of machine Due to the easier availability of numerous datasets pertaining to different facets of human lives, learning and deep learning techniques have significantly aided the development of IDSS [1]. Furthermore, the IDSS can be used for patient gesture recognition and has been identified as a unique feature in smart healthcare, guaranteeing prompt patient reaction, particularly for remote resource control [2].
Dr. D. J. Samatha Naidu*
S. Guru Tejaswi
10.5281/zenodo.15097225