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A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

Alaa Omran Almagrabi*

Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Alaa Omran Almagrabi. Email: email

Computers, Materials & Continua 2023, 76(2), 2079-2093. https://doi.org/10.32604/cmc.2023.039683

Abstract

A potential concept that could be effective for multiple applications is a “cyber-physical system” (CPS). The Internet of Things (IoT) has evolved as a research area, presenting new challenges in obtaining valuable data through environmental monitoring. The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction. This study employs a deep learning method, CNN-LSTM, and two-way feature extraction to classify audio systems within CPS. The primary objective of this system, which is built upon a convolutional neural network (CNN) with Long Short Term Memory (LSTM), is to analyze the vocalization patterns of two different species of anurans. It has been demonstrated that CNNs, when combined with mel-spectrograms for sound analysis, are suitable for classifying ambient noises. Initially, the data is augmented and preprocessed. Next, the mel spectrogram features are extracted through two-way feature extraction. First, Principal Component Analysis (PCA) is utilized for dimensionality reduction, followed by Transfer learning for audio feature extraction. Finally, the classification is performed using the CNN-LSTM process. This methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments, resulting in high classification accuracy. The study highlights that this CNN-LSTM approach enables cost-effective and resource-efficient monitoring of large natural regions. The dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

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APA Style
Almagrabi, A.O. (2023). A deep cnn-lstm-based feature extraction for cyber-physical system monitoring. Computers, Materials & Continua, 76(2), 2079-2093. https://doi.org/10.32604/cmc.2023.039683
Vancouver Style
Almagrabi AO. A deep cnn-lstm-based feature extraction for cyber-physical system monitoring. Comput Mater Contin. 2023;76(2):2079-2093 https://doi.org/10.32604/cmc.2023.039683
IEEE Style
A.O. Almagrabi, "A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring," Comput. Mater. Contin., vol. 76, no. 2, pp. 2079-2093. 2023. https://doi.org/10.32604/cmc.2023.039683



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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