Alaa Khalid Alduraibi^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1295-1309, 2022, DOI:10.32604/iasc.2022.026601

Abstract With technological advancements, deep machine learning can assist doctors in identifying the brain mass or tumor using magnetic resonance imaging (MRI). This work extracts the deep features from 18-pre-trained convolutional neural networks (CNNs) to train the classical classifiers to categorize the brain MRI images. As a result, DenseNet-201, EfficientNet-b0, and DarkNet-53 deep features trained support vector machine (SVM) model shows the best accuracy. Furthermore, the ReliefF method is applied to extract the best features. Then, the fitness function is defined to select the number of nearest neighbors of ReliefF algorithm and feature vector size. Finally, the particle swarm optimization algorithm… More >

Jiahui Pan^1,6,*, Jianhao Zhang¹, Fei Wang^1,6, Wuhan Liu², Haiyun Huang^3,6, Weishun Tang³, Huijian Liao⁴, Man Li⁵, Jianhui Wu¹, Xueli Li², Dongming Quan², Yuanqing Li^3,6

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 53-71, 2021, DOI:10.32604/iasc.2021.015970

Abstract In this paper, an automatic sleep scoring system based on electroencephalogram (EEG) and electrooculogram (EOG) signals was proposed for sleep stage classification and depression detection. Our automatic sleep stage classification method contained preprocessing based on independent component analysis, feature extraction including spectral features, spectral edge frequency features, absolute spectral power, statistical features, Hjorth features, maximum-minimum distance and energy features, and a modified ReliefF feature selection. Finally, a support vector machine was employed to classify four states (awake, light sleep [LS], slow-wave sleep [SWS] and rapid eye movement [REM]). The overall accuracy of the Sleep-EDF database reached 90.10 ± 2.68% with… More >

Zihao Shen^1,2, Hui Wang^1,*, Kun Liu¹, Peiqian Liu¹, Menglong Ba¹, MengYao Zhao³

Computer Systems Science and Engineering, Vol.37, No.1, pp. 121-133, 2021, DOI:10.32604/csse.2021.014988

Abstract The rapid progress of the Internet has exposed networks to an increased number of threats. Intrusion detection technology can effectively protect network security against malicious attacks. In this paper, we propose a ReliefF-P-Naive Bayes and softmax regression (RP-NBSR) model based on machine learning for network attack detection to improve the false detection rate and F1 score of unknown intrusion behavior. In the proposed model, the Pearson correlation coefficient is introduced to compensate for deficiencies in correlation analysis between features by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm is used… More >

Bolun Chen^{1, 2}, Guochang Zhu^{1, *}, Min Ji¹, Yongtao Yu¹, Jianyang Zhao¹, Wei Liu³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1039-1049, 2020, DOI:10.32604/cmc.2020.010583

Abstract Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities are collected by a sliding… More >