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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 41510

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Dr. Marcos Gestal

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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
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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

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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.

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Keywords

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

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Published Papers (12 papers)

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Editorial

Jump to: Research

2 pages, 176 KiB

Open AccessEditorial

Special Issue on Applied Artificial Neural Networks

by Marcos Gestal

Appl. Sci. 2022, 12(19), 9551; https://doi.org/10.3390/app12199551 - 23 Sep 2022

Cited by 1 | Viewed by 1085

Abstract

Over the years there have been many attempts to understand, and subsequently imitate, the way that humans try to solve problems, so it can help to artificially achieve the same kind of intelligent behavior [...] Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

Research

Jump to: Editorial

21 pages, 1671 KiB

Open AccessArticle

Multimodal Tucker Decomposition for Gated RBM Inference

by Mauricio Maldonado-Chan, Andres Mendez-Vazquez and Ramon Osvaldo Guardado-Medina

Appl. Sci. 2021, 11(16), 7397; https://doi.org/10.3390/app11167397 - 11 Aug 2021

Cited by 6 | Viewed by 2517

Abstract

Gated networks are networks that contain gating connections in which the output of at least two neurons are multiplied. The basic idea of a gated restricted Boltzmann machine (RBM) model is to use the binary hidden units to learn the conditional distribution of one image (the output) given another image (the input). This allows the hidden units of a gated RBM to model the transformations between two successive images. Inference in the model consists in extracting the transformations given a pair of images. However, a fully connected multiplicative network creates cubically many parameters, forming a three-dimensional interaction tensor that requires a lot of memory and computations for inference and training. In this paper, we parameterize the bilinear interactions in the gated RBM through a multimodal tensor-based Tucker decomposition. Tucker decomposition decomposes a tensor into a set of matrices and one (usually smaller) core tensor. The parameterization through Tucker decomposition helps reduce the number of model parameters, reduces the computational costs of the learning process and effectively strengthens the structured feature learning. When trained on affine transformations of still images, we show how a completely unsupervised network learns explicit encodings of image transformations. Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

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22 pages, 2627 KiB

Open AccessArticle

One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study

by Gabriel Antonio Valverde Castilla, José Manuel Mira McWilliams and Beatriz González-Pérez

Appl. Sci. 2021, 11(14), 6241; https://doi.org/10.3390/app11146241 - 6 Jul 2021

Cited by 3 | Viewed by 2706

Abstract

In this work, we applied a stochastic simulation methodology to quantify the power of the detection of outlying mixture components of a stochastic model, when applying a reduced-dimension clustering technique such as Self-Organizing Maps (SOMs). The essential feature of SOMs, besides dimensional reduction into a discrete map, is the conservation of topology. In SOMs, two forms of learning are applied: competitive, by sequential allocation of sample observations to a winning node in the map, and cooperative, by the update of the weights of the winning node and its neighbors. By means of cooperative learning, the conservation of topology from the original data space to the reduced (typically 2D) map is achieved. Here, we compared the performance of one- and two-layer SOMs in the outlier representation task. The same stratified sampling was applied for both the one-layer and two-layer SOMs; although, stratification would only be relevant for the two-layer setting—to estimate the outlying mixture component detection power. Two distance measures between points in the map were defined to quantify the conservation of topology. The results of the experiment showed that the two-layer setting was more efficient in outlier detection while maintaining the basic properties of the SOM, which included adequately representing distances from the outlier component to the remaining ones. Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

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17 pages, 599 KiB

Open AccessArticle

Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children

by Vannessa Duarte, Paul Leger, Sergio Contreras and Hiroaki Fukuda

Appl. Sci. 2021, 11(13), 5961; https://doi.org/10.3390/app11135961 - 26 Jun 2021

Cited by 5 | Viewed by 2865

Abstract

Fetal alcohol spectrum disorder (FASD) is an umbrella term for children’s conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject’s quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer-based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children’s images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. Model obtained obtained an accuracy of 88.46% (psychometric, 74.07% (Antisaccadic), 72.24% (Prosaccadic), 88% (Memory guide saccade), and 75% (DTI). These results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. These results are an improvement on Zhang’s 2019 model, which used the same data with less accuracy level. Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

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15 pages, 2749 KiB

Open AccessArticle

Methods of Constructing Time Series for Predicting Local Time Scales by Means of a GMDH-Type Neural Network

by Łukasz Sobolewski and Wiesław Miczulski

Appl. Sci. 2021, 11(12), 5615; https://doi.org/10.3390/app11125615 - 17 Jun 2021

Cited by 6 | Viewed by 1775

Abstract

Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method. Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

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25 pages, 4073 KiB

Open AccessArticle

Adaptive Modeling of Prediction of Telecommunications Network Throughput Performances in the Domain of Motorway Coverage

by Milorad K. Banjanin, Mirko Stojčić, Dejan Drajić, Zoran Ćurguz, Zoran Milanović and Aleksandar Stjepanović

Appl. Sci. 2021, 11(8), 3559; https://doi.org/10.3390/app11083559 - 15 Apr 2021

Cited by 6 | Viewed by 3071

Abstract

The main goal of this paper is to create an adaptive model based on multilayer perceptron (MLP) for prediction of average downlink (DL) data throughput per user and average DL data throughput per cell within an LTE network technology and in a geo-space that includes a segment of the Motorway 9th January with the access roads. The accuracy of model prediction is estimated based on relative error (RE). With multiple trainings and testing of 30 different variants of the MLP model, with different metaparameters the final model was chosen whose average accuracy for the Cell Downlink Average Throughput variable is 89.6% (RE = 0.104), while for the Average User Downlink Throughput variable the average accuracy is 88% (RE = 0.120). If the coefficient of determination is observed, the results showed that the accuracy of the best selected prediction model for the first variable is 1.4% higher than the accuracy of the prediction of the selected model for the second dependent variable. In addition, the results showed that the performance of the MLP model expressed over R² was significantly better compared to the reference multiple linear regression (MLR) model used. Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

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13 pages, 19025 KiB

Open AccessArticle

Multivariate Statistical Analysis for Training Process Optimization in Neural Networks-Based Forecasting Models

by Jamer Jimenez, Loraine Navarro, Christian G. Quintero M. and Mauricio Pardo

Appl. Sci. 2021, 11(8), 3552; https://doi.org/10.3390/app11083552 - 15 Apr 2021

Cited by 3 | Viewed by 2096

Abstract

Data forecasting is very important for electrical analysis development, transport dimensionality, marketing strategies, etc. Hence, low error levels are required. However, in some cases data have dissimilar behaviors that can vary depending on such exogenous variables as the type of day, weather conditions, and geographical area, among others. Commonly, computational intelligence techniques (e.g., artificial neural networks) are used due to their generalization capabilities. In spite of the above, they do not have a unique way to reach optimal performance. For this reason, it is necessary to analyze the data’s behavior and their statistical features in order to identify those significant factors in the training process to guarantee a better performance. In this paper is proposed an experimental method for identifying those significant factors in the forecasting model for time series data and measure their effects on the Akaike information criterion (AIC) and the Mean Absolute Percentage Error (MAPE). Additionally, we seek to establish optimal parameters for the proper selection of the artificial neural network model. Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

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27 pages, 6082 KiB

Open AccessArticle

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

by Shabir Hussain, Yang Yu, Muhammad Ayoub, Akmal Khan, Rukhshanda Rehman, Junaid Abdul Wahid and Weiyan Hou

Appl. Sci. 2021, 11(8), 3495; https://doi.org/10.3390/app11083495 - 13 Apr 2021

Cited by 72 | Viewed by 8125

Abstract

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19. Full article

(This article belongs to the Special Issue Applied Artificial Neural Networks)

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14 pages, 1868 KiB

Open AccessArticle

Private Label and Macroeconomic Indexes: An Artificial Neural Networks Application

by Eloy Gil-Cordero and Juan-Pedro Cabrera-Sánchez

Appl. Sci. 2020, 10(17), 6043; https://doi.org/10.3390/app10176043 - 31 Aug 2020

Cited by 7 | Viewed by 2817

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 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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23 pages, 6545 KiB

Open AccessArticle

A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area

by Keran Chen, Ping Wang, Xiaojun Yang, Nan Zhang and Di Wang

Appl. Sci. 2020, 10(17), 5808; https://doi.org/10.3390/app10175808 - 22 Aug 2020

Cited by 25 | Viewed by 4083

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 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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15 pages, 10010 KiB

Open AccessArticle

An Integrated System of Artificial Intelligence and Signal Processing Techniques for the Sorting and Grading of Nuts

by Morteza Farhadi, Yousef Abbaspour-Gilandeh, Asghar Mahmoudi and Joe Mari Maja

Appl. Sci. 2020, 10(9), 3315; https://doi.org/10.3390/app10093315 - 10 May 2020

Cited by 17 | Viewed by 3713

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 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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14 pages, 392 KiB

Open AccessArticle

Classical Music Prediction and Composition by Means of Variational Autoencoders

by Daniel Rivero, Iván Ramírez-Morales, Enrique Fernandez-Blanco, Norberto Ezquerra and Alejandro Pazos

Appl. Sci. 2020, 10(9), 3053; https://doi.org/10.3390/app10093053 - 27 Apr 2020

Cited by 5 | Viewed by 3899

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 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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