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Applied Sciences
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Deep Learning for Natural Language Processing (NLP) and Image Classification...
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Deep Learning for Natural Language Processing (NLP) and Image Classification (IC)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 580

Special Issue Editors

Prof. Dr. Abdelhamid Tayebi Tayebi

E-Mail Website
Guest Editor
Computer Science Department, University of Alcala, 28801 Alcalá de Henares, Spain
Interests: artificial intelligence; deep learning; design and optimization of antennas; electromagnetic radiation and scattering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Josefa Gómez

E-Mail Website
Guest Editor
Computer Science Department, University of Alcala, 28801 Alcalá de Henares, Spain
Interests: artificial intelligence; deep learning; optimization and analysis of antennas; design of graphical user interfaces; propagation for mobile communications or wireless networks
Prof. Dr. Carlos Delgado

E-Mail Website
Guest Editor
Computer Science Department, University of Alcala, 28801 Alcalá de Henares, Spain
Interests: artificial intelligence; deep learning; electromagnetic radiation and scattering

Special Issue Information

Dear Colleagues,

This Special Issue will be an assemblage of the latest advancements in deep learning, particularly focusing on its applications in Natural Language Processing (NLP) and Image Classification (IC). The goal is to present a diverse range of novel ideal and empirical results, covering both theoretical foundations and practical implementations. This encompasses the utilization of artificial intelligence, machine learning, and deep learning for processing large datasets obtained from various sources such as satellites, scientific explorations, sensor networks, and medical diagnostics.

A part of this Special Issue is dedicated to the comparative of deep learning models. This involves a critical evaluation of these models in terms of their efficiency and accuracy across diverse datasets and real-world scenarios. The process aims to provide insights into the generalizability and interpretability of these models, thereby fostering advancements in their practical applicability.

Topics of interest include, but are not limited to, the following:

  • Artificial intelligence tools and applications;
  • Natural Language Processing;
  • Speech recognition;
  • Comparative of deep learning models;
  • Intelligent system.

Prof. Dr. Abdelhamid Tayebi Tayebi
Prof. Dr. Josefa Gómez
Prof. Dr. Carlos Delgado
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. Applied Sciences 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

  • machine learning
  • deep learning
  • artificial intelligence
  • natural language processing
  • speech recognition
  • image classification
  • comparative models
  • algorithm efficiency
  • real-world applications

Published Papers (1 paper)

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Research

26 pages, 8034 KiB  
Article
Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
by Carlos J. Hellín, Alvaro A. Olmedo, Adrián Valledor, Josefa Gómez, Miguel López-Benítez and Abdelhamid Tayebi
Appl. Sci. 2024, 14(8), 3419; https://doi.org/10.3390/app14083419 - 18 Apr 2024
Viewed by 384
Abstract
The field of image analysis with artificial intelligence has grown exponentially thanks to the development of neural networks. One of its most promising areas is medical diagnosis through lung X-rays, which are crucial for diseases like pneumonia, which can be mistaken for other [...] Read more.
The field of image analysis with artificial intelligence has grown exponentially thanks to the development of neural networks. One of its most promising areas is medical diagnosis through lung X-rays, which are crucial for diseases like pneumonia, which can be mistaken for other conditions. Despite medical expertise, precise diagnosis is challenging, and this is where well-trained algorithms can assist. However, working with medical images presents challenges, especially when datasets are limited and unbalanced. Strategies to balance these classes have been explored, but understanding their local impact and how they affect model evaluation is still lacking. This work aims to analyze how a class imbalance in a dataset can significantly influence the informativeness of metrics used to evaluate predictions. It demonstrates that class separation in a dataset impacts trained models and is a strategy deserving more attention in future research. To achieve these goals, classification models using artificial and deep neural networks implemented in the R environment are developed. These models are trained using a set of publicly available images related to lung pathologies. All results are validated using metrics obtained from the confusion matrix to verify the impact of data imbalance on the performance of medical diagnostic models. The results raise questions about the procedures used to group classes in many studies, aiming to achieve class balance in imbalanced data and open new avenues for future research to investigate the impact of class separation in datasets with clinical pathologies. Full article
(This article belongs to the Special Issue Deep Learning for Natural Language Processing (NLP) and Image Classification (IC))
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