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Mathematics
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Application of Artificial Intelligence, Machine Learning and Data Science in...
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Application of Artificial Intelligence, Machine Learning and Data Science in Industrial and Medical Domains

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

Deadline for manuscript submissions: 29 March 2025 | Viewed by 910

Special Issue Editors

Dr. Gustavo Arroyo-Figueroa

E-Mail Website
Guest Editor
Instituto Nacional de Electricidad y Energias Limpias, Cuernavaca 62490, Mexico
Interests: applied artificial intelligence; data analytics; smart grid
Prof. Dr. Juan Humberto Sossa-Azuela

E-Mail Website
Guest Editor
Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, Col. San Pedro Zacatenco, Mexico City 07738, Mexico
Interests: computer vision; pattern recognition; image analysis; neural networks
Prof. Dr. Osslan Osiris Vergara Villegas

E-Mail Website
Guest Editor
Departamento de Ingeniería Industrial y Manufactura, Universidad Autónoma de Ciudad Juaréz, Ciudad Juárez, Mexico
Interests: computer vision; augmented reality; mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to contribute to this Special Issue on the "Application of Artificial Intelligence, Machine Learning and Data Science in Industrial and Medical domains". This Special Issue aims to foster collaboration and discussion on the applications of AI, ML and DS mathematical models and their impact on real-world domains.

AI algorithms and ML techniques allow organizations to extract valuable insights from massive amounts of data, enabling businesses to make data-driven, operational and strategic decisions and gain a competitive advantage.

This Special Issue seeks original manuscripts that report novelty results about the design, development and applied solutions of intelligent systems in areas such as Process Automation and Optimization, Predictive Analytics and Forecasting, Medical Diagnosis, Education, Robotics, Cybersecurity, Data-Driven Decision Systems and Industry, especially manuscripts that emphasize the description of the mathematical models involved.

Dr. Gustavo Arroyo-Figueroa
Prof. Dr. Juan Humberto Sossa-Azuela
Prof. Dr. Osslan Osiris Vergara Villegas
Guest Editors

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

  • applied artificial intelligence
  • applied machine learning
  • data science
  • engineering applications of AI
  • generative AI
  • intelligent decision support systems
  • computer vision

Published Papers (1 paper)

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Research

18 pages, 3200 KiB  
Article
Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network
by Andrés García-Floriano and Elías Ventura-Molina
Mathematics 2024, 12(10), 1445; https://0-doi-org.brum.beds.ac.uk/10.3390/math12101445 - 8 May 2024
Viewed by 170
Abstract
Computer-based pre-diagnosis of diseases through medical imaging is a task worked on for many years. The so-called fundus images stand out since they do not have uniform illumination and are highly sensitive to noise. One of the diseases that can be pre-diagnosed through [...] Read more.
Computer-based pre-diagnosis of diseases through medical imaging is a task worked on for many years. The so-called fundus images stand out since they do not have uniform illumination and are highly sensitive to noise. One of the diseases that can be pre-diagnosed through fundus images is age-related macular degeneration, which initially manifests as the appearance of lesions called drusen. Several ways of pre-diagnosing macular degeneration have been proposed, methods based entirely on the segmentation of drusen with prior image processing have been designed and applied, and methods based on image pre-processing and subsequent conversion to feature vectors, or patterns, to be classified by a Machine-Learning model have also been developed. Finally, in recent years, the use of Deep-Learning models, particularly Convolutional Networks, has been proposed and used in classification problems where the data are only images. The latter has allowed the so-called transfer learning, which consists of using the learning achieved in the solution of one problem to solve another. In this paper, we propose the use of transfer learning through the Xception Deep Convolutional Neural Network to detect age-related macular degeneration in fundus images. The performance of the Xception model was compared against six other state-of-the-art models with a dataset created from images available in public and private datasets, which were divided into training/validation and test; with the training/validation set, the training was made using 10-fold cross-validation. The results show that the Xception neural network obtained a validation accuracy that surpasses other models, such as the VGG-16 or VGG-19 networks, and had an accuracy higher than 80% in the test set. We consider that the contributions of this work include the use of a Convolutional Neural Network model for the detection of age-related macular degeneration through the classification of fundus images in those affected by AMD (drusen) and the images of healthy patients. The performance of this model is compared against other methods featured in the state-of-the-art approaches, and the best model is tested on a test set outside the training and validation set. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence, Machine Learning and Data Science in Industrial and Medical Domains)
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