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  • Open Access

    ARTICLE

    A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

    Maryam Bukhari1, Sadaf Yasmin1, Sheneela Naz2, Mehr Yahya Durrani1, Mubashir Javaid3, Jihoon Moon4, Seungmin Rho5,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1251-1279, 2023, DOI:10.32604/cmc.2023.040329 - 31 October 2023

    Abstract Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed… More >

  • Open Access

    ARTICLE

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

    Guangfei Jia*, Fengwei Guo, Zhe Wu, Suxiao Cui, Jiajun Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 383-405, 2023, DOI:10.32604/sdhm.2023.026885 - 07 September 2023

    Abstract With the development of multi-signal monitoring technology, the research on multiple signal analysis and processing has become a hot subject. Mechanical equipment often works under variable working conditions, and the acquired vibration signals are often non-stationary and nonlinear, which are difficult to be processed by traditional analysis methods. In order to solve the noise reduction problem of multiple signals under variable speed, a COT-DCS method combining the Computed Order Tracking (COT) based on Chirplet Path Pursuit (CPP) and Distributed Compressed Sensing (DCS) is proposed. Firstly, the instantaneous frequency (IF) is extracted by CPP, and the… More > Graphic Abstract

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

  • Open Access

    ARTICLE

    A Time-Varying Parameter Estimation Method for Physiological Models Based on Physical Information Neural Networks

    Jiepeng Yao1,2, Zhanjia Peng1,2, Jingjing Liu1,2, Chengxiao Fan1,2, Zhongyi Wang1,2,3, Lan Huang1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2243-2265, 2023, DOI:10.32604/cmes.2023.028101 - 03 August 2023

    Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this… More >

  • Open Access

    ARTICLE

    Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

    Muhammad Tayyeb1, Muhammad Umer1, Khaled Alnowaiser2, Saima Sadiq3, Ala’ Abdulmajid Eshmawi4, Rizwan Majeed5, Abdullah Mohamed6, Houbing Song7, Imran Ashraf8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1677-1694, 2023, DOI:10.32604/cmes.2023.026535 - 26 June 2023

    Abstract Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals More >

  • Open Access

    ARTICLE

    Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

    C. S. S. Anupama1, Rafina Zakieva2, Afanasiy Sergin3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Chomyong Kim8, Yunyoung Nam8,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321 - 21 June 2023

    Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae… More >

  • Open Access

    ARTICLE

    Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

    Saeed Mohsen1,2,*, Sherif S. M. Ghoneim3, Mohammed S. Alzaidi3, Abdullah Alzahrani3, Ashraf Mohamed Ali Hassan4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758 - 29 April 2023

    Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and… More >

  • Open Access

    ARTICLE

    Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

    Jiali Wang1, Bing Li2, Chengyu Qiu1, Xinyun Zhang1, Yuting Cheng1, Peihua Wang1, Ta Zhou3, Hong Ge2, Yuanpeng Zhang1,3,*, Jing Cai3,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4843-4866, 2023, DOI:10.32604/cmc.2023.037457 - 29 April 2023

    Abstract Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can More >

  • Open Access

    ARTICLE

    Macrophage-derived SHP-2 inhibits the metastasis of colorectal cancer via Tie2-PI3K signals

    XUELIANG WU1,#, SHAOYU GUAN2,#, YONGGANG LU3, JUN XUE4, XIANGYANG YU1, QI ZHANG5,*, XIMO WANG1,5,*, TIAN LI6

    Oncology Research, Vol.31, No.2, pp. 125-139, 2023, DOI:10.32604/or.2023.028657 - 10 April 2023

    Abstract This research aimed to explore the influence of Src homology-2 containing protein tyrosine phosphatase (SHP- 2) on the functions of tyrosine kinase receptors with immunoglobulin and EGF homology domains 2 (Tie2)-expressing monocyte/macrophages (TEMs) and the influence of the angiopoietin(Ang)/Tie2-phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) (Ang/Tie2-PI3K/Akt/mTOR) signaling pathway on the tumor microvascular remodeling in an immunosuppressive microenvironment. In vivo, SHP-2- deficient mice were used to construct colorectal cancer (CRC) liver metastasis models. SHP-2-deficient mice had significantly more metastatic cancer and inhibited nodules on the liver surface than wild-type mice, and the high-level expression… More > Graphic Abstract

    Macrophage-derived SHP-2 inhibits the metastasis of colorectal cancer via Tie2-PI3K signals

  • Open Access

    ARTICLE

    Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

    Srikanth Cherukuvada, R. Kayalvizhi*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4101-4118, 2023, DOI:10.32604/cmc.2023.036207 - 31 March 2023

    Abstract The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of… More >

  • Open Access

    ARTICLE

    Determined Reverberant Blind Source Separation of Audio Mixing Signals

    Senquan Yang1, Fan Ding1, Jianjun Liu1, Pu Li1,2, Songxi Hu1,2,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3309-3323, 2023, DOI:10.32604/iasc.2023.035051 - 15 March 2023

    Abstract Audio signal separation is an open and challenging issue in the classical “Cocktail Party Problem”. Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix More >

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