Quantum Water Strider Algorithm with Hybrid-Deep-Learning-Based Activity Recognition for Human–Computer Interaction
Source abstract
Abstract
Human action and activity recognition are clues that alleviate human behavior analysis. Human action recognition (HAR) becomes a significant challenge in various applications involving human computer interaction (HCI) and intellectual video surveillance for enhancing security in distinct fields. Precise action recognition is highly challenging because of the variations in clutter, backgrounds, and viewpoint. The evaluation method depends on the proper extraction and learning of data. The achievement of deep learning (DL) models results in effectual performance in several image-related tasks. In this view, this paper presents a new quantum water strider algorithm with hybrid-deep-learning-based activity recognition (QWSA-HDLAR) model for HCI. The proposed QWSA-HDLAR technique mainly aims to recognize the different types of activities. To recognize activities, the QWSA-HDLAR model employs a deep-transfer-learning-based, neural-architectural-search-network (NASNet)-based feature extractor to generate feature vectors. In addition, the presented QWSA-HDLAR model exploits a QWSA-based hyperparameter tuning process to choose the hyperparameter values of the NASNet model optimally. Finally, the classification of human activities is carried out by the use of a hybrid convolutional neural network with a bidirectional recurrent neural network (HCNN-BiRNN) model. The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. The experimental values reported the supremacy of the QWSA-HDLAR model over recent DL approaches.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Context-Aware Activity Recognition Systems · Non-Invasive Vital Sign Monitoring · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Mecca 24382, Saudi Arabia
Department of Information Systems, College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
Department of Information and technology Systems, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Research Centre, Future University in Egypt, New Cairo 11845, Egypt
Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Funding
King Khalid University · Taif University · Umm Al-Qura University
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