Kexin Zhang¹, Bin Lin^2,*, Jixin Chen¹, Xinlong Wu¹, Chao Lu³, Desheng Zheng¹, Lulu Tian⁴

Computer Systems Science and Engineering, Vol.42, No.1, pp. 351-360, 2022, DOI:10.32604/csse.2022.021132 - 02 December 2021

Abstract Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to… More >

Xing Guo^1,#, Yu-Dong Zhang^2,#, Siyuan Lu², Zhihai Lu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 23-71, 2022, DOI:10.32604/cmes.2021.017679 - 29 November 2021

Abstract Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis More >

Debiao Meng^1,2,3,*, Hongtao Wang¹, Shiyuan Yang¹, Zhiyuan Lv¹, Zhengguo Hu¹, Zihao Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 543-558, 2022, DOI:10.32604/cmes.2022.018123 - 29 November 2021

Abstract In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is More >

Jing Lu¹, Yan Wu^2,#, Mingyan Hu¹, Yao Xiong¹, Yapeng Zhou¹, Ziliang Zhao¹, Liutong Shang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 365-377, 2022, DOI:10.32604/cmes.2021.017897 - 29 November 2021

Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based… More >

Wafaa Alsaggaf^1,*, Felwa Abukhodair¹, Amani Tariq Jamal², Sayed Abdel-Khalek³, Romany F. Mansour⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1189-1203, 2022, DOI:10.32604/cmc.2022.022469 - 03 November 2021

Abstract In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents… More >

R. Krishnaswamy^1,*, B. Sivakumar², B. Viswanathan³, Fahd N. Al-Wesabi^4,5, Marwa Obayya⁶, Anwer Mustafa Hilal⁷

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 255-268, 2022, DOI:10.32604/cmc.2022.021995 - 03 November 2021

Abstract Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis.… More >

T. Jeslin^1,*, J. Arul Linsely²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 171-182, 2022, DOI:10.32604/cmc.2022.020255 - 03 November 2021

Abstract Brain cancer is the premier reason for cancer deaths all over the world. The diagnosis of brain cancer at an initial stage is mediocre, as the radiologist is ineffectual. Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful. Therefore, the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules. The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region.… More >

Anurag Jain¹, Kusum Yadav², Hadeel Fahad Alharbi², Shamik Tiwari^1,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 423-438, 2022, DOI:10.32604/cmc.2022.020238 - 03 November 2021

Abstract Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital… More >

Dezheng Zhang^1,2, Qi Jia^1,2, Shibing Yang^1,2, Xinliang Han², Cong Xu³, Xin Liu^1,4, Yonghong Xie^1,2,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 159-170, 2022, DOI:10.32604/cmc.2022.017295 - 03 November 2021

Abstract Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pattern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the… More >

V. R. Vimal^1,*, P. Anandan², N. Kumaratharan³

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 31-43, 2022, DOI:10.32604/iasc.2022.017622 - 26 October 2021

Abstract

Electrocardiogram (ECG) monitoring models are commonly employed for diagnosing heart diseases. Since ECG signals are normally acquired for a longer time duration with high resolution, there is a need to compress the ECG signals for transmission and storage. So, a novel compression technique is essential in transmitting the signals to the telemedicine center to monitor and analyse the data. In addition, the protection of ECG signals poses a challenging issue, which encryption techniques can resolve. The existing Encryption-Then-Compression (ETC) models for multimedia data fail to properly maintain the trade-off between compression performance and signal quality.

… More >