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  • Open Access

    ARTICLE

    Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

    Muhammad Irfan1, Khalil Ullah2, Fazal Muhammad3,*, Salman Khan3, Faisal Althobiani4, Muhammad Usman5, Mohammed Alshareef4, Shadi Alghaffari4, Saifur Rahman1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 169-181, 2023, DOI:10.32604/iasc.2023.032337

    Abstract The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features from sEMG channels along with… More >

  • Open Access

    ARTICLE

    Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

    Farah Mohammad*, Saad Al-Ahmadi

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 623-639, 2023, DOI:10.32604/cmc.2023.032552

    Abstract

    There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models… More >

  • Open Access

    ARTICLE

    An Unambiguity and Anti-Range Eclipse Method for PD Radar Using Biphase Coded Signals

    Jihong Yan1,2, Weihan Ni1,*, Jianshu Zhai2, Haiyang Dong1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1337-1351, 2023, DOI:10.32604/cmes.2022.021567

    Abstract Target detection is an important research content in the radar field. At present, efforts are being made to optimize the precision of detection information. In this paper, we use the high pulse repetition frequency (HPRF) transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection. In addition, We also apply Walsh matrix and genetic algorithm (GA) to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak, which make the results more accurate. In a radar receiver, data rearrangement of echo signals is performed, and then… More >

  • Open Access

    ARTICLE

    NLRP3 Promotes Glioma Cell Proliferation and Invasion via the Interleukin-1b/NF-kB p65 Signals

    Liping Xue*1, Bin Lu†1, Bibo Gao, Yangyang Shi, Jingqi Xu, Rui Yang, Bo Xu, Peng Ding

    Oncology Research, Vol.27, No.5, pp. 557-564, 2019, DOI:10.3727/096504018X15264647024196

    Abstract Because of the characteristics of high invasiveness, relapse, and poor prognosis, the management of malignant gliomas has always been a great challenge. Nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3) is a crucial component of the NLRP3 inflammasome, a multiprotein complex that can trigger caspase 1/interleukin-1 (IL-1)-mediated inflammatory response once activated and participates in the pathogeny of diverse inflammatory diseases as well as cancers. We examined the function of NLRP3 in the development of glioma. Glioma cells were treated with NLRP3 interference or overexpression vectors, recombinant IL-1 , IL-1 antibody, and NF- B inhibitor. Cell proliferation and invasion were… More >

  • Open Access

    ARTICLE

    EIF5A2 Is Highly Expressed in Anaplastic Thyroid Carcinoma and Is Associated With Tumor Growth by Modulating TGF-β Signals

    Fengyun Hao*1, Qingli Zhu, Lingwei Lu, Shukai Sun, Yichuan Huang§, Jinna Zhang, Zhaohui Liu†#, Yuanqing Miao**, Xuelong Jiao††, Dong Chen††1

    Oncology Research, Vol.28, No.4, pp. 345-355, 2020, DOI:10.3727/096504020X15834065061807

    Abstract Anaplastic thyroid carcinoma (ATC) is resistant to standard therapies and has no effective treatment. Eukaryotic translation initiation factor 5A2 (EIF5A2) has shown to be upregulated in many malignant tumors and proposed to be a critical gene involved in tumor metastasis. In this study, we aimed to investigate the expression status of EIF5A2 in human ATC tissues and to study the role and mechanisms of EIF5A2 in ATC tumorigenesis in vitro and in vivo. Expression of EIF5A2 protein was analyzed in paraffin-embedded human ATC tissues and adjacent nontumorous tissues (ANCT) (n = 24) by immunochemistry. Expressions of EIF5A2 mRNA and protein… More >

  • Open Access

    ARTICLE

    Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

    Ashit Kumar Dutta1,*, Yasser Albagory2, Manal Al Faraj1, Yasir A. M. Eltahir3, Abdul Rahaman Wahab Sait4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1517-1529, 2023, DOI:10.32604/csse.2023.026482

    Abstract The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the SAE-SR technique is carried out… More >

  • Open Access

    ARTICLE

    Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals

    Jian Liu1, Yipeng Du1, Xiang Wang1,*, Wuguang Yue2, Jim Feng3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1995-2011, 2022, DOI:10.32604/cmc.2022.029073

    Abstract Epilepsy is a common neurological disease and severely affects the daily life of patients. The automatic detection and diagnosis system of epilepsy based on electroencephalogram (EEG) is of great significance to help patients with epilepsy return to normal life. With the development of deep learning technology and the increase in the amount of EEG data, the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted approaches. However, the neural architecture design for epilepsy EEG analysis is time-consuming and laborious, and the designed structure is difficult to adapt to the changing EEG collection… More >

  • Open Access

    ARTICLE

    Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

    Souad Larabi-Marie-Sainte1, Eatedal Alabdulkreem2, Mohammad Alamgeer3, Mohamed K Nour4, Anwer Mustafa Hilal5,*, Mesfer Al Duhayyim6, Abdelwahed Motwakel5, Ishfaq Yaseen5

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4589-4601, 2022, DOI:10.32604/cmc.2022.027922

    Abstract Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature… More >

  • Open Access

    ARTICLE

    Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

    Manar Ahmed Hamza1,*, Noha Negm2, Shaha Al-Otaibi3, Amel A. Alhussan4, Mesfer Al Duhayyim5, Fuad Ali Mohammed Al-Yarimi2, Mohammed Rizwanullah1, Ishfaq Yaseen1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4541-4555, 2022, DOI:10.32604/cmc.2022.027048

    Abstract Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of… More >

  • Open Access

    ARTICLE

    Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

    D. Venugopal1, V. Mohan2, S. Ramesh3, S. Janupriya4, Sangsoon Lim5,*, Seifedine Kadry6

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 545-558, 2022, DOI:10.32604/csse.2022.024239

    Abstract In recent times, pattern recognition of communication modulation signals has gained significant attention in several application areas such as military, civilian field, etc. It becomes essential to design a safe and robust feature extraction (FE) approach to efficiently identify the various signal modulation types in a complex platform. Several works have derived new techniques to extract the feature parameters namely instant features, fractal features, and so on. In addition, machine learning (ML) and deep learning (DL) approaches can be commonly employed for modulation signal classification. In this view, this paper designs pattern recognition of communication signal modulation using fractal features… More >

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