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Electronics
Special Issues
Intelligent Learning and Health Diagnosis Technologies
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Intelligent Learning and Health Diagnosis Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (10 June 2021) | Viewed by 8562

Special Issue Editors

Prof. Dr. **g **

E-Mail Website
Guest Editor
Department of Automation, East China University of Science and Technology, Shanghai 200237, China
Interests: brain-computer interface (BCI); real-world applications of BCIs in patients and healthy users; cognitive neuroscience; pattern recognition
Prof. Dr. Ali Zemouche

E-Mail Website
Guest Editor
1. CRAN UMR CNRS 7039, University of Lorraine, 54400 Cosnes et Romain, France
2. EPI Inria DISCO, Laboratoire des Signaux et Systèmes, CNRS-CentraleSupélec, 91192 Gif-sur-Yvette, France
Interests: observers design; nonlinear systems; neur adaptive observers; robust control; machine learning; artificial intelligence; robust observers
Special Issues, Collections and Topics in MDPI journals
Dr. Ian Daly

E-Mail Website
Guest Editor
Department of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: BCI; assistive Technology; nonlinear dynamics; machine learning; signal processing; bio-signal analysis; meta-heuristic search techniques
Prof. Dr. Peng Xu

E-Mail Website
Guest Editor
Faculty of Biomedical Engineering, University of Electronics Science & Technology of China, Chengdu 610054, China
Interests: EEG analysis; brain Computer Interface
Dr. Ren Xu

E-Mail Website
Guest Editor
Department of Research and Development, Guger Technologies OG (g.tec), 4521 Schiedlberg, Austria
Interests: biomedical signal processing; brain-computer interface
Prof. Dr. Feng Duan

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nankai University, Tian** 300071, China
Interests: robot technology; myoelectric artificial hand; rehabilitation robot; robot vision; home service robot; human body skills analysis
Prof. Dr. Junfeng Sun

E-Mail Website
Guest Editor
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: neuroimaging (EEG, fMRI, DTI); transcranial ultrasound stimulation and transcranial magnetic stimulation; brain network modelling; cognitive function, stroke, aging and other mental disorders

Special Issue Information

Dear Colleagues,

With the growth of the world’s population and the increasing demand for human health, the diagnosis of a large number of patients has placed great pressure on public health institutions. For some diseases, it is difficult to diagnose them quickly and accurately. In recent years, artificial intelligence technologies have been develo** quickly and have seen successful applications in many areas. Inspired by artificial intelligence technologies, intelligent diagnosis technologies (e.g., medical image recognition, brain–computer interface, neuro-feedback) could help to diagnose some diseases quickly and accurately. To promote the development of intelligent diagnosis technologies, this Special Issue will focus on various new technologies and methods that could help to improve the performance of intelligent diagnosis. In particular, this Special Issue welcomes contributions on topics including but not limited to:

  • Medical imaging analysis and diagnosis methods;
  • Machine learning algorithms for medical diagnosis;
  • Deep learning methods for medical diagnosis;
  • Brain–computer interface technologies for medical applications;
  • Neuro-feedback technologies for medical diagnosis;
  • Applications of various intelligent technologies in medical diagnosis;
  • Review/survey articles.

Prof. Dr. **g **
Prof. Dr. Ali Zemouche
Dr. Ian Daly
Prof. Dr. Peng Xu
Dr. Ren Xu
Prof. Dr. Feng Duan
Prof. Dr. Junfeng Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Pattern recognition
  • Machine learning
  • Human–machine interaction
  • Signal processing
  • Brain–computer interface
  • Disease diagnosis

Published Papers (3 papers)

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Research

15 pages, 3978 KiB  
Communication
Analyzing the Features Affecting the Performance of Teachers during Covid-19: A Multilevel Feature Selection
by Alqahtani Saeed, Raja Habib, Maryam Zaffar, Khurrum Shehzad Quraishi, Oriba Altaf, Muhammad Irfan, Adam Glowacz, Ryszard Tadeusiewicz, Mohammed Ayed Huneif, Alqahtani Abdulwahab, Sharifa Khalid Alduraibi, Fahad Alshehri, Alaa Khalid Alduraibi and Ziyad Almushayti
Electronics 2021, 10(14), 1673; https://doi.org/10.3390/electronics10141673 - 13 Jul 2021
Cited by 3 | Viewed by 2642
Abstract
COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online [...] Read more.
COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM). Full article
(This article belongs to the Special Issue Intelligent Learning and Health Diagnosis Technologies)
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17 pages, 2977 KiB  
Article
Robust Autoregression with Exogenous Input Model for System Identification and Predicting
by Jiaxin **e, Cunbo Li, Ning Li, Peiyang Li, Xurui Wang, Dongrui Gao, Dezhong Yao, Peng Xu, Gang Yin and Fali Li
Electronics 2021, 10(6), 755; https://doi.org/10.3390/electronics10060755 - 22 Mar 2021
Cited by 3 | Viewed by 2904
Abstract
Autoregression with exogenous input (ARX) is a widely used model to estimate the dynamic relationships between neurophysiological signals and other physiological parameters. Nevertheless, biological signals, such as electroencephalogram (EEG), arterial blood pressure (ABP), and intracranial pressure (ICP), are inevitably contaminated by unexpected artifacts, [...] Read more.
Autoregression with exogenous input (ARX) is a widely used model to estimate the dynamic relationships between neurophysiological signals and other physiological parameters. Nevertheless, biological signals, such as electroencephalogram (EEG), arterial blood pressure (ABP), and intracranial pressure (ICP), are inevitably contaminated by unexpected artifacts, which may distort the parameter estimation due to the use of the L2 norm structure. In this paper, we defined the ARX in the Lp (p ≤ 1) norm space with the aim of resisting outlier influence and designed a feasible iteration procedure to estimate model parameters. A quantitative evaluation with various outlier conditions demonstrated that the proposed method could estimate ARX parameters more robustly than conventional methods. Testing with the resting-state EEG with ocular artifacts demonstrated that the proposed method could predict missing data with less influence from the artifacts. In addition, the results on ICP and ABP data further verified its efficiency for model fitting and system identification. The proposed Lp-ARX may help capture system parameters reliably with various input and output signals that are contaminated with artifacts. Full article
(This article belongs to the Special Issue Intelligent Learning and Health Diagnosis Technologies)
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13 pages, 3961 KiB  
Article
Representation Learning for Motor Imagery Recognition with Deep Neural Network
by Fangzhou Xu, Fenqi Rong, Yun**g Miao, Yanan Sun, Gege Dong, Han Li, **cheng Li, Yuandong Wang and Jiancai Leng
Electronics 2021, 10(2), 112; https://doi.org/10.3390/electronics10020112 - 7 Jan 2021
Cited by 9 | Viewed by 2244
Abstract
This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm [...] Read more.
This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm for extracting features and the traditional algorithm for classification. Specifically, we mainly use the convolution neural network (CNN) to extract the features from the training data and then classify those features by combing with the gradient boosting (GB) algorithm. The comprehensive study with CNN and GB algorithms will profoundly help us to obtain more feature information from brain activities, enabling us to obtain the classification results from human body actions. The performance of the proposed framework has been evaluated on the dataset I of BCI Competition III. Furthermore, the combination of deep learning and traditional algorithms provides some ideas for future research with the BCI systems. Full article
(This article belongs to the Special Issue Intelligent Learning and Health Diagnosis Technologies)
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