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

作     者：Alaa Omran Almagrabi 

作者机构：Department of Information SystemsFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第76卷第8期

页      面：2079-2093页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Funded by Institutional Fund Projects under Grant No.IFPIP:236-611-1442 by Ministry of Education and King Abdulaziz University Jeddah Saudi Arabia(A.O.A.) 

主　　题：Cyber-physical system internet of things feature extraction,classification CNN principal component analysis mel spectrograms monitoring deep learning 

摘      要：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 *** existing work solely focuses on classifying the audio system of CPS without utilizing feature *** study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within *** 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 *** has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient ***,the data is augmented and ***,the mel spectrogram features are extracted through two-way feature ***,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature ***,the classification is performed using the CNN-LSTM *** methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification *** study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural *** dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.