Hung-Hsiang Wang¹, Chih-Ping Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630

Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent… More >

Shuai Chen¹, Yinwei Ma², Zhongshu Wang², Zongmei Xu³, Song Zhang¹, Jianle Li¹, Hao Xu¹, Zhanjun Wu^1,*

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 125-141, 2024, DOI:10.32604/sdhm.2024.042594

Abstract The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life. To this end, distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages, such as lightweight and ease of embedding. However, identifying the precise location of damage from the optical fiber signals remains a critical challenge. In this paper, a novel approach which namely Modified Sliding Window Principal Component Analysis (MSWPCA) was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors. The proposed method is able to extract signal… More > Graphic Abstract

Yizhao Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270

Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related to impulsive ground-shaking. Thirdly, a… More >

Changfu Wan^1,2, Wenqiang Li^1,2,*, Sitong Ling^1,2, Yingdong Liu^1,2, Jiahao Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 321-348, 2024, DOI:10.32604/cmes.2023.029053

Abstract Regarding the spatial profile extraction method of a multi-field co-simulation dataset, different extraction directions, locations, and numbers of profiles will greatly affect the representativeness and integrity of data. In this study, a multi-field co-simulation data extraction method based on adaptive infinitesimal elements is proposed. The multi-field co-simulation dataset based on related infinitesimal elements is constructed, and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction. Based on the fireworks algorithm, the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive… More > Graphic Abstract

Alaa Omran Almagrabi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2079-2093, 2023, DOI: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… More >

Tianyu He¹, Xiaolong Xu^1,*, Zhichen Hu¹, Qingzhan Zhao², Jianguo Dai², Fei Dai³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3657-3673, 2023, DOI:10.32604/iasc.2023.036831

Abstract With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of private patient information, which must… More >

Mohandu Anjaneyulu, Mohan Kubendiran^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1641-1656, 2023, DOI:10.32604/cmc.2023.035056

Abstract Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed… More >

Hammad Rustam¹, Muhammad Muneeb¹, Suliman A. Alsuhibany², Yazeed Yasin Ghadi³, Tamara Al Shloul⁴, Ahmad Jalal¹, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2331-2346, 2023, DOI:10.32604/cmc.2023.028712

Abstract Hand gesture recognition (HGR) is used in a numerous applications, including medical health-care, industrial purpose and sports detection. We have developed a real-time hand gesture recognition system using inertial sensors for the smart home application. Developing such a model facilitates the medical health field (elders or disabled ones). Home automation has also been proven to be a tremendous benefit for the elderly and disabled. Residents are admitted to smart homes for comfort, luxury, improved quality of life, and protection against intrusion and burglars. This paper proposes a novel system that uses principal component analysis, linear discrimination analysis feature extraction, and… More >

Vaibhav Rupapara¹, Furqan Rustam², Abid Ishaq², Ernesto Lee³, Imran Ashraf^4,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1931-1949, 2023, DOI:10.32604/iasc.2023.028257

Abstract Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization. During the last few years, an alarming increase is observed worldwide with a 70% rise in the disease since 2000 and an 80% rise in male deaths. If untreated, it results in complications of many vital organs of the human body which may lead to fatality. Early detection of diabetes is a task of significant importance to start timely treatment. This study introduces a methodology for the classification of diabetic and normal people using an ensemble machine learning model… More >

Jee-Sic Hur¹, Hyeong-Geun Lee¹, Shinjin Kang², Yeo Chan Yoon³, Soo Kyun Kim^1,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6213-6227, 2023, DOI:10.32604/cmc.2023.035344

Abstract In this paper, we proposed a combined PCA-LPP algorithm to improve 3D face reconstruction performance. Principal component analysis (PCA) is commonly used to compress images and extract features. One disadvantage of PCA is local feature loss. To address this, various studies have proposed combining a PCA-LPP-based algorithm with a locality preserving projection (LPP). However, the existing PCA-LPP method is unsuitable for 3D face reconstruction because it focuses on data classification and clustering. In the existing PCA-LPP, the adjacency graph, which primarily shows the connection relationships between data, is composed of the e-or k-nearest neighbor techniques. By contrast, in this study,… More >