Special Issue "Applied Artificial Neural Networks"

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

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editor

Dr. Marcos Gestal
E-Mail Website
Guest Editor
Computation Sciences and Information Technologies Department, Faculty of Computer Science, University of A Coruña, 15071, A Coruña, Spain
Interests: Evolutionary Computation; Artificial Neural Networks; Artificial Intelligence; Feature Selection; Machine Learning

Special Issue Information

Dear Colleagues,

Over the years, there have been many attempts to understand, and subsequently imitate, the way humans try to solve problems, in order to help achieve the same kind of intelligent behavior.

Among these attempts, one of them has been especially successful: artificial neural networks, which simplify the functioning of one of the most complex organs in Nature: the brain. Through the interconnection of nodes and a learning process from examples, these networks provide excellent solutions in a diverse range of fields of research.

After overcoming a small bump in recent years, they have been revived under the name of Deep Neural Networks, which have the same basis and take advantage of the emergence of new learning algorithms and greater computational capabilities.

This Special Issue aims to accommodate, on the one hand, the latest theoretical advances in this field, such as new learning paradigms or new architectures, and, on the other hand, those more recent works in the scientific field where the authors have used any of the many types of available neural networks to reach the best results in their area(s): image or video processing, pattern recognition, forecasting, time-series processing, real-time decision systems, etc.

We kindly invite researchers and investigators to contribute their original research or review articles to this Special Issue.

Dr. Marcos Gestal
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 papers will be 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 2000 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

  • artficial neural networks
  • deep neural networks
  • deep learning
  • machine learning
  • pattern matching
  • artificial intelligence
  • learning algorithms
  • applications

Published Papers (4 papers)

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Research

Open AccessArticle
Private Label and Macroeconomic Indexes: An Artificial Neural Networks Application
Appl. Sci. 2020, 10(17), 6043; https://doi.org/10.3390/app10176043 - 31 Aug 2020
Cited by 3 | Viewed by 612
Abstract
Retail companies operate with a private label assortment of 40–45% of their total assortment, which has led to a significant growth of private labels in recent years in their countries of origin; however, when retail companies decide to internationalize, it is important to [...] Read more.
Retail companies operate with a private label assortment of 40–45% of their total assortment, which has led to a significant growth of private labels in recent years in their countries of origin; however, when retail companies decide to internationalize, it is important to know which macroeconomic indicators are more relevant when entering a new country or continent. For that reason, in this study we have as a main objective to establish which are the most transcendental macroeconomic variables for the volume and value of the private label. For this purpose, we have analyzed a total of 1400 samples, creating an artificial neural network (ANN). The results show that the most important macroeconomic indicator that must be taken into consideration above other macroeconomic indicators for retail companies to be successful within a country is the per capita debt. In addition, we have considered in this research that unemployment is not the most important primary indicator for the volume of the private label. Full article
(This article belongs to the Special Issue Applied Artificial Neural Networks)
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Open AccessArticle
A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
Appl. Sci. 2020, 10(17), 5808; https://doi.org/10.3390/app10175808 - 22 Aug 2020
Cited by 1 | Viewed by 550
Abstract
In weather forecasting, numerical weather prediction (NWP) that is based on physical models requires proper post-processing before it can be applied to actual operations. Therefore, research on intelligent post-processing algorithms has always been an important topic in this field. This paper proposes a [...] Read more.
In weather forecasting, numerical weather prediction (NWP) that is based on physical models requires proper post-processing before it can be applied to actual operations. Therefore, research on intelligent post-processing algorithms has always been an important topic in this field. This paper proposes a model output deep learning (MODL) method for post-processing, which can improve the forecast effect of numerical weather prediction. MODL is an end-to-end post-processing method based on deep convolutional neural network, which directly learns the mapping relationship between the forecast fields output by numerical model and the observation temperature field in order to obtain more accurate temperature forecasts. MODL modifies the existing deep convolution model according to the post-processing problem’s characteristics, thereby improving the performance of the weather forecast. This paper uses The International Grand Global Ensemble (TIGGE) dataset from European Centre for Medium-Range Weather Forecasts (ECMWF) and the observed air temperature of 2 m obtained from Tianjin meteorological station in order to test the post-processing performance of MODL. The MODL method applied to temperature in post-processing is compared with the ECMWF forecast, Model Output Statistics (MOS) methods, and Model Output Machine Learning (MOML) methods. The Root Mean Square Error (RMSE) of the temperature field predicted by MODL and the observed temperature field is smaller than the other models and the accuracy of the temperature difference of 2 °C (Acc) is higher, especially where the prediction time is in the first three days. The lightweight nature of MODL also makes it suitable for most operations. Full article
(This article belongs to the Special Issue Applied Artificial Neural Networks)
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Open AccessArticle
An Integrated System of Artificial Intelligence and Signal Processing Techniques for the Sorting and Grading of Nuts
Appl. Sci. 2020, 10(9), 3315; https://doi.org/10.3390/app10093315 - 10 May 2020
Cited by 2 | Viewed by 675
Abstract
The existence of conversion industries to sort and grade hazelnuts with modern technology plays a vital role in export. Since most of the hazelnuts produced in Iran are exported to domestic and foreign markets without sorting and grading, it is necessary to have [...] Read more.
The existence of conversion industries to sort and grade hazelnuts with modern technology plays a vital role in export. Since most of the hazelnuts produced in Iran are exported to domestic and foreign markets without sorting and grading, it is necessary to have a well-functioning smart system to create added value, reduce waste, increase shelf life, and provide a better product delivery. In this study, a method is introduced to sort and grade hazelnuts by integrating audio signal processing and artificial neural network techniques. A system was designed and developed in which the produced sound, due to the collision of the hazelnut with a steel disk, was taken by the microphone placed under the steel disk and transferred to a PC via a sound card. Then, it was stored and processed by a program written in MATLAB software. A piezoelectric sensor and a circuit were used to eliminate additional ambient noise. The time-domain and wavelet domain features of the data were extracted using MATLAB software and were analyzed using Artificial Neural Network Toolbox. Seventy percent of the extracted data signals were used for training, 15% for validation, and the rest of the data was used to test the artificial neural network (Multilayer Perceptron network with Levenberg-Marquardt Learning algorithm). The model optimization and the number of neurons in the hidden layer were conducted based on mean square error (MSE) and prediction accuracy (PA). A total of 2400 hazelnuts were used to evaluate the system. The optimal neural network structure for sorting and grading hazelnuts was 4-21-3 (four neurons in input layers, 21 neurons in the hidden layer, and three outputs which are the desired classification). This neural network (NN) was used to classify hazelnut as big, small, hollow, or damaged. Results showed 96.1%, 89.3%, and 93.1% accuracy for big/small, hollow, or damaged hazelnuts were obtained, respectively. Full article
(This article belongs to the Special Issue Applied Artificial Neural Networks)
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Open AccessArticle
Classical Music Prediction and Composition by Means of Variational Autoencoders
Appl. Sci. 2020, 10(9), 3053; https://doi.org/10.3390/app10093053 - 27 Apr 2020
Viewed by 955
Abstract
This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this representation to make predictions of [...] Read more.
This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this representation to make predictions of the future note events of the musical piece. This approach was trained with different songs of Handel. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions on unseen data. Full article
(This article belongs to the Special Issue Applied Artificial Neural Networks)
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