Version 1
: Received: 1 July 2022 / Approved: 5 July 2022 / Online: 5 July 2022 (04:22:49 CEST)
How to cite:
Syed, A.; Aldhahri, E. A.; Iqbal, M. M.; Ali, A.; Muthanna, A.; Jamil, H.; Jamil, F. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning. Preprints2022, 2022070056. https://doi.org/10.20944/preprints202207.0056.v1
Syed, A.; Aldhahri, E. A.; Iqbal, M. M.; Ali, A.; Muthanna, A.; Jamil, H.; Jamil, F. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning. Preprints 2022, 2022070056. https://doi.org/10.20944/preprints202207.0056.v1
Syed, A.; Aldhahri, E. A.; Iqbal, M. M.; Ali, A.; Muthanna, A.; Jamil, H.; Jamil, F. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning. Preprints2022, 2022070056. https://doi.org/10.20944/preprints202207.0056.v1
APA Style
Syed, A., Aldhahri, E. A., Iqbal, M. M., Ali, A., Muthanna, A., Jamil, H., & Jamil, F. (2022). Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning. Preprints. https://doi.org/10.20944/preprints202207.0056.v1
Chicago/Turabian Style
Syed, A., Harun Jamil and Faisal Jamil. 2022 "Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning" Preprints. https://doi.org/10.20944/preprints202207.0056.v1
Abstract
In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN from accomplishing remarkable evaluation. In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. After implementation, the more detailed and deeper charting for training in each circle of space-time fusion. The training model further enhances the results after handling complicated evaluations of models. The video classification model is used in this implemented model. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning is introduced to further understand space-time association in human endeavors. In the implementation of the result, the well-known dataset, i.e., UCF101 to, evaluates the performance of the proposed hybrid technique. The results beat the proposed hybrid technique that substantially beats the initial 3D CNNs. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%.
Keywords
deep learning; convolutional neural networks; classification; machine learning; IoT
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.