New Trends in Artificial Neural Networks and Its Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 1730

Special Issue Editors


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Guest Editor
Data Analytics Research Center, Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
Interests: machine/deep learning techniques; NLP; explainable AI

E-Mail
Guest Editor
Data Analytics Research Center, Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
Interests: signal processing; biomedical data analysis; brain computer interface
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Special Issue Information

Dear Colleagues,

In recent years, the field of artificial intelligence has witnessed an unprecedented surge in innovation and applications, driven by the remarkable capabilities of artificial neural networks. Inspired by the human brain, neural networks have revolutionized various domains of science and engineering. They have enabled breakthroughs in fields such as medical imaging, computer vision, speech signal processing, robotics, and more.

We are pleased to announce a Special Issue dedicated to "New Trends in Artificial Neural Networks and Its Applications". This Special Issue aims to provide a dynamic platform for researchers to disseminate their latest findings, discuss innovative methodologies, and showcase cutting-edge applications and technologies that leverage neural networks. By bringing together contributions from experts across the field, we intend to enrich and advance the existing body of knowledge, offering fresh insights and perspectives.

We invite submissions on a wide range of topics related to neural networks, including but not limited to:

  • Novel techniques and architectures in neural network design.
  • Recent advancements in training algorithms and optimization methods.
  • Transformers-based models.
  • Multimodal models.
  • Speech and natural language processing.
  • Neural networks-based applications in signal processing.
  • Cross-disciplinary applications of neural networks in various scientific and engineering domains, with particular emphasis on the medical domain.
  • Explainable AI.
  • Green AI.
  • Scalability and efficiency improvements for large-scale neural network deployment.

Dr. Chiara Zucco
Dr. Barbara Calabrese
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. 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.

Published Papers (2 papers)

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68 pages, 1508 KiB  
Article
Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence
by Dovilė Kuizinienė and Tomas Krilavičius
Electronics 2024, 13(8), 1596; https://doi.org/10.3390/electronics13081596 - 22 Apr 2024
Viewed by 761
Abstract
Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of [...] Read more.
Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises. Full article
(This article belongs to the Special Issue New Trends in Artificial Neural Networks and Its Applications)
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14 pages, 27254 KiB  
Article
GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN
by Aroona Ayub and HyungWon Kim
Electronics 2024, 13(7), 1231; https://doi.org/10.3390/electronics13071231 - 26 Mar 2024
Viewed by 421
Abstract
Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the [...] Read more.
Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers have used traditional data augmentation techniques to increase the amount of training data available to them. A recent trend in object detection is to use generative models to automatically create annotated data that can enrich a training set and improve the performance of the target model. This paper presents a method of training the proposed ColorGAN network, which is used to generate augmented data for the target domain of interest with the least compromise in quality. We demonstrate a method to train a GAN with images of vehicles in different colors. Then, we demonstrate that our ColorGAN can change the color of vehicles of any given vehicle dataset to a set of specified colors, which can serve as an augmented training dataset. Our experimental results show that the augmented dataset generated by the proposed method helps enhance the detection performance of a CNN for applications where the original training data are limited. Our experiments also show that the model can achieve a higher mAP of 76% when the model is trained with augmented images along with the original training dataset. Full article
(This article belongs to the Special Issue New Trends in Artificial Neural Networks and Its Applications)
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