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Mathematics
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Advanced Artificial Intelligence Models and Its Applications, 2nd Edition
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Advanced Artificial Intelligence Models and Its Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1718

Special Issue Editor

Prof. Dr. Tao Zhou

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: computer vision; machine learning; medical image analysis; AI in healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of Artificial Intelligence (AI) has experienced tremendous growth since the mid-20th century, as evidenced by its application in a wide range of engineering and science problems. Over the last decade, AI has seen a breakthrough, owing to the introduction of deep learning, which has enabled the utilization of various AI models in a diverse range of domains.

This Special Issue intends to provide a forum for researchers developing and reviewing new AI models in various fields, including science, engineering, industry, education, health, and transportation. We are inviting authors to submit relevant original results, literature reviews, theoretical studies, or papers addressing AI’s real-world applications.

Prof. Dr. Tao Zhou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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. Mathematics 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 2600 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

  • machine learning
  • deep learning
  • pattern recognition
  • computer vision
  • multimedia retrieval and analysis
  • multimodal representation learning
  • statistical learning
  • medical image analysis
  • security applications
  • big data and analysis
  • benchmark dataset

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Published Papers (1 paper)

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Research

31 pages, 7299 KiB  
Article
Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder
by Hasan Alkahtani, Theyazn H. H. Aldhyani, Zeyad A. T. Ahmed and Ahmed Abdullah Alqarni
Mathematics 2023, 11(22), 4698; https://doi.org/10.3390/math11224698 - 20 Nov 2023
Viewed by 1489
Abstract
This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for [...] Read more.
This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for ADHD. The proposed system utilizes a publicly available dataset consisting of raw EEG recordings from 61 individuals with ADHD and 60 control subjects during a visual attention task. The methodology involves meticulous preprocessing of raw EEG recordings to isolate brain signals and extract informative features, including time, frequency, and entropy signal characteristics. The feature selection techniques, including least absolute shrinkage and selection operator (LASSO) regularization and recursive elimination, were applied to identify relevant variables and enhance generalization. The obtained features are processed by employing various machine learning and deep learning algorithms, namely CatBoost, Random Forest Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs). The empirical results of the proposed algorithms highlight the effectiveness of feature selection approaches in matching informative biomarkers with optimal model classes. The convolutional neural network model achieves superior testing accuracy of 97.75% using LASSO-regularized biomarkers, underscoring the strengths of deep learning and customized feature optimization. The proposed framework advances EEG analysis to uncover discriminative patterns, significantly contributing to the field of ADHD screening and diagnosis. The suggested methodology achieved high performance compared with different existing systems based on AI approaches for diagnosing ADHD. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Models and Its Applications, 2nd Edition)
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