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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 4617

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

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Published Papers (4 papers)

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Research

25 pages, 6059 KiB  
Article
Evaluation of Deformable Convolution: An Investigation in Image and Video Classification
by Andrea Burgos Madrigal, Victor Romero Bautista, Raquel Díaz Hernández and Leopoldo Altamirano Robles
Mathematics 2024, 12(16), 2448; https://doi.org/10.3390/math12162448 - 7 Aug 2024
Viewed by 457
Abstract
Convolutional Neural Networks (CNNs) present drawbacks for modeling geometric transformations, caused by the convolution operation’s locality. Deformable convolution (DCON) is a mechanism that solves these drawbacks and improves the robustness. In this study, we clarify the optimal way to replace the standard convolution [...] Read more.
Convolutional Neural Networks (CNNs) present drawbacks for modeling geometric transformations, caused by the convolution operation’s locality. Deformable convolution (DCON) is a mechanism that solves these drawbacks and improves the robustness. In this study, we clarify the optimal way to replace the standard convolution with its deformable counterpart in a CNN model. To this end, we conducted several experiments using DCONs applied in the layers that conform a small four-layer CNN model and on the four-layers of several ResNets with depths 18, 34, 50, and 101. The models were tested in binary balanced classes with 2D and 3D data. If DCON is used on the first layers of the proposal of model, the computational resources will tend to increase and produce bigger misclassification than the standard CNN. However, if the DCON is used at the end layers, the quantity of Flops will decrease, and the classification accuracy will improve by up to 20% about the base model. Moreover, it gains robustness because it can adapt to the object of interest. Also, the best kernel size of the DCON is three. With these results, we propose a guideline and contribute to understanding the impact of DCON on the robustness of CNNs. 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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18 pages, 421 KiB  
Article
Fuzzy Logic Prediction of Hypertensive Disorders in Pregnancy Using the Takagi–Sugeno and C-Means Algorithms
by Israel Campero-Jurado, Daniel Robles-Camarillo, Jorge A. Ruiz-Vanoye, Juan M. Xicoténcatl-Pérez, Ocotlán Díaz-Parra, Julio-César Salgado-Ramírez, Francisco Marroquín-Gutiérrez and Julio Cesar Ramos-Fernández
Mathematics 2024, 12(15), 2417; https://doi.org/10.3390/math12152417 - 3 Aug 2024
Viewed by 526
Abstract
Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study [...] Read more.
Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research. 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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30 pages, 1876 KiB  
Article
Mental-Health: An NLP-Based System for Detecting Depression Levels through User Comments on Twitter (X)
by Rafael Salas-Zárate, Giner Alor-Hernández, Mario Andrés Paredes-Valverde, María del Pilar Salas-Zárate, Maritza Bustos-López and José Luis Sánchez-Cervantes
Mathematics 2024, 12(13), 1926; https://doi.org/10.3390/math12131926 - 21 Jun 2024
Viewed by 1717
Abstract
The early detection of depression in a person is of great help to medical specialists since it allows for better treatment of the condition. Social networks are a promising data source for identifying individuals who are at risk for this mental disease, facilitating [...] Read more.
The early detection of depression in a person is of great help to medical specialists since it allows for better treatment of the condition. Social networks are a promising data source for identifying individuals who are at risk for this mental disease, facilitating timely intervention and thereby improving public health. In this frame of reference, we propose an NLP-based system called Mental-Health for detecting users’ depression levels through comments on X. Mental-Health is supported by a model comprising four stages: data extraction, preprocessing, emotion detection, and depression diagnosis. Using a natural language processing tool, the system correlates emotions detected in users’ posts on X with the symptoms of depression and provides specialists with the depression levels of the patients. By using Mental-Health, we described a case study involving real patients, and the evaluation process was carried out by comparing the results obtained using Mental-Health with those obtained through the application of the PHQ-9 questionnaire. The system identifies moderately severe and moderate depression levels with good precision and recall, allowing us to infer the model’s good performance and confirm that it is a promising option for mental health support. 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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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://doi.org/10.3390/math12101445 - 8 May 2024
Viewed by 831
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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