Intelligent Training Algorithm for Artificial Neural Network EEG Classifications

Akkar, Hanan A. R.; Jasim, Faris B. Ali

doi:10.5815/ijisa.2018.05.04

Cited by 16 publications

(9 citation statements)

References 21 publications

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“…Training practice research was used, and the relevant data before and after training were compared and analyzed [11].…”

Section: (4) Comparative Analysis Methodsmentioning

confidence: 99%

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Zhang

2021

Mobile Information Systems

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As the world's largest sport, football has affected a wide area and a large number of participants and had a great impact on political economy and culture, which has become the best embodiment of the social function of football. Throughout the experience of football in developed countries in the world, the stable development of youth football is the best way to improve the level of football in a country, and the Chinese Football Association has invested more energy in professional leagues and national teams. The development of youth campus football is basically in a state of no management. Therefore, people gradually realize the concept that “football should serve education.” In order to solve the problem of football players' lack of exercise in multiple subjects, it is particularly important to design systems and make plans for their respective physical characteristics. After the failure of various important competitions, the Chinese national football team reflected on the specific factors of backwardness. Under today's system, no one manages the specific development of youth campus football. Especially for young people, training programs that adapt to their individual characteristics should be formulated according to their growth stage and physical characteristics. It can effectively improve the efficiency of football teaching and training (FTT) by managing football players' training information and coaches' teaching information in an intelligent and informatized way. The different sports in which athletes participate in training are identified through the motion recognition layer. The data generated during the entire exercise process, including exercise time, number of exercises, score settlement, and other data, are stored, and the data are finally uploaded to the server, to carry out scientific analysis and management and generate sports training prescriptions in line with their own characteristics. This paper proposes research methods based on the intelligent integrated system of FTT, including literature retrieval, questionnaire survey, training empirical method, comparative analysis method, interview method, and support vector machine model for action recognition, which are used in football teaching and the design experiment of the intelligent integrated system for training; the overall architecture design of the football teaching intelligent integration system and the specific design of the football teaching intelligent integration system are proposed. The experimental results of this article show that 90.70% of players like the teaching mode of intelligent FTT, and the intelligent FTT system can help improve the enthusiasm of players in training and learning.

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“…Training practice research was used, and the relevant data before and after training were compared and analyzed [11].…”

Section: (4) Comparative Analysis Methodsmentioning

confidence: 99%

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Zhang

2021

Mobile Information Systems

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“…Different classifiers are used with the same dataset, which is divided to three parts -60% for training the model, 30% for validating it and 10% for testing. The confusion matrix is used for comparison between different classifiers [7]. Referring to Table 4, the best classification accuracy is obtained with linear discriminant model, however, fine tree classifier model appeared to be a model with the highest prediction speed and minimum training time.…”

Section: C46mentioning

confidence: 99%

Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

Akkar¹,

Hadi²,

Al-Dosari³

et al. 2021

Communications

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The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance.

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“…Tests showed a higher accuracy of image classification with the proposed model. Akkar et al [50], DelPreto et al [54] presented an interesting complex solution for providing the natural interaction of a person and a robot. Despite the presented conclusions in the study, it is very difficult to conclude how the results were obtained, since the study used the dataset from EEG and EMG.…”

Section: Other Researches Involving Machine Learning For Eeg Analysismentioning

confidence: 99%

Review of Neural Networks in the EEG Signal Recognition

Rakhmatulin

2020

SSRN Journal

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In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn't change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

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Intelligent Training Algorithm for Artificial Neural Network EEG Classifications

Cited by 16 publications

References 21 publications

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

Review of Neural Networks in the EEG Signal Recognition

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