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Research and Applications of Artificial Neural Network
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Research and Applications of Artificial Neural Network

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

Deadline for manuscript submissions: 20 March 2026 | Viewed by 3189

Special Issue Editors

Prof. Dr. Youness El Hamzaoui

E-Mail Website
Guest Editor
Engineering College, Carmen Autonomous University, Calle 56, 4, Esq. Avenida Concordia, Col. Benito Juárez, Campeche, Mexico
Interests: artificial neural network architectures and optimization; advanced backpropagation algorithm development; statistical modeling for environmental systems; process parameter optimization; machine learning in environmental engineering; neural network development; statistical methods; computational techniques; advanced process technologies; methodological expertise
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Alfredo Hernández Pérez

E-Mail Website
Guest Editor
Center for Research in Engineering and Applied Sciences (CIICAp), Autonomous University of the State of Morelos, Cuernavaca, Mexico
Interests: artificial intelligence and machine learning applications; neural network architectures and optimization; advanced control systems; robotics and automation; process identification and modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in artificial neural networks (ANNs) have revolutionized our approach to complex modeling and prediction tasks across various scientific domains. This Special Issue aims to explore innovative developments in ANN architectures, training methodologies, and their practical applications in solving real-world engineering and scientific challenges.

We are pleased to welcome contributions that advance both the theoretical foundations and practical implementation of ANNs. The scope of this Special Issue includes, but is not limited to, the following topics:

  1. Novel ANN architectures and training algorithms;
  2. Optimization techniques for neural network performance;
  3. Applications in environmental monitoring and remediation;
  4. Predictive modeling for chemical and biological systems;
  5. Hybrid approaches combining ANNs with other computational methods;
  6. Real-time monitoring and process control applications;
  7. Comparative studies of different neural network paradigms;
  8. Statistical validation methods for ANN models.

We welcome the submission of original research articles, comprehensive reviews, and case studies. Priority will be given to works that demonstrate innovative methodological approaches and significant practical applications.

Prof. Dr. Youness El Hamzaoui
Prof. Dr. José Alfredo Hernández Pérez
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 250 words) can be sent to the Editorial Office for assessment.

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

  • artificial neural networks
  • machine learning
  • predictive modeling
  • backpropagation algorithms
  • environmental engineering
  • process optimization
  • statistical validation
  • computational intelligence
  • pattern recognition
  • real-time monitoring

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Published Papers (4 papers)

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Research

26 pages, 14479 KB  
Article
SpeQNet: Query-Enhanced Spectral Graph Filtering for Spatiotemporal Forecasting
by Zongyao Feng and Konstantin Markov
Appl. Sci. 2026, 16(3), 1176; https://doi.org/10.3390/app16031176 - 23 Jan 2026
Abstract
Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends [...] Read more.
Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article
(This article belongs to the Special Issue Research and Applications of Artificial Neural Network)
37 pages, 14686 KB  
Article
Development of an Extreme Machine Learning-Based Computational Application for the Detection of Armillaria in Cherry Trees
by Patricio Hernández Toledo, David Zabala-Blanco, Philip Vasquez-Iglesias, Amelia E. Pizarro, Mary Carmen Jarur, Roberto Ahumada-García, Ali Dehghan Firoozabadi, Pablo Palacios Játiva and Iván Sánchez
Appl. Sci. 2025, 15(22), 11927; https://doi.org/10.3390/app152211927 - 10 Nov 2025
Viewed by 638
Abstract
This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of machine learning (ML) models, using the [...] Read more.
This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of machine learning (ML) models, using the optimal variant among different Extreme Learning Machine (ELM) models. This tool represents a concrete contribution to the use of artificial intelligence in smart agriculture, enabling more efficient and accessible management of cherry tree crops. The overall goal is to evaluate machine learning-based strategies that enable efficient and low-computational-cost detection of the disease, facilitating its implementation on devices with limited resources. The ERICA database is used by following a proper methodology in order to learning and validation stages are completely independent. Preprocessing includes renaming, cropping, scaling, grayscale conversion, vectorization, and normalization. Subsequently, the impact of reducing image resolution is studied, identifying that a size of 63 × 23 pixels maintains a good balance between visual detail and computational efficiency. Six ELM variants are trained: standard, regularized (R-ELM), class-weighted (W1-ELM and W2-ELM), and multilayer (ML2-ELM and ML3-ELM), and classical machine learning approaches are optimized and compared with classical ML approaches. The results indicate that W1-ELM achieves the best performance among tested variants, reaching an accuracy of 0.77 and a geometric mean of 0.45 with a training time in order of seconds. Full article
(This article belongs to the Special Issue Research and Applications of Artificial Neural Network)
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17 pages, 4147 KB  
Article
Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
by Juan Gu and Euihark Lee
Appl. Sci. 2025, 15(14), 7722; https://doi.org/10.3390/app15147722 - 10 Jul 2025
Viewed by 849
Abstract
Box compression strength (BCS) is a critical parameter for assessing the performance of shipping containers during transportation. Traditionally, BCS evaluation relies heavily on physical testing, which is both time-consuming and costly. These limitations have prompted industry to seek more efficient and cost-effective alternatives. [...] Read more.
Box compression strength (BCS) is a critical parameter for assessing the performance of shipping containers during transportation. Traditionally, BCS evaluation relies heavily on physical testing, which is both time-consuming and costly. These limitations have prompted industry to seek more efficient and cost-effective alternatives. This study explores the application of artificial neural networks (ANNs) to estimate BCS at an industry-applicable level. A real-world dataset—covering approximately 90% of the box dimensions commonly used in the industry—was utilized to train a generalized ANN model for BCS prediction. The model achieved a prediction error of approximately 10%. When validated against experimentally measured data from laboratory testing, with single-wall B-flute as a representative, the prediction error was at a much lower level, further demonstrating the model’s reliability. This study offers a novel approach to BCS prediction, providing a cost-effective and scalable alternative to traditional physical testing methods in the packaging industry. Full article
(This article belongs to the Special Issue Research and Applications of Artificial Neural Network)
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26 pages, 7906 KB  
Article
Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores
by Jose W. Naal-Pech, Leonardo Palemón-Arcos and Youness El Hamzaoui
Appl. Sci. 2025, 15(10), 5609; https://doi.org/10.3390/app15105609 - 17 May 2025
Cited by 1 | Viewed by 991
Abstract
Accurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis function (RBF), Bayesian regularized (BR), scaled conjugate [...] Read more.
Accurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis function (RBF), Bayesian regularized (BR), scaled conjugate gradient (SCG), and Levenberg–Marquardt (LM)—to predict UCS from three readily measured variables: water content, interconnected porosity, and real density. Fifty core specimens from the Seybaplaya quarry in Campeche, Mexico, were split into training and testing subsets under uniform preprocessing. Each model’s predictive performance was assessed over 30 independent runs using mean absolute error, root mean squared error, and coefficient of determination, with statistical differences tested via nonparametric hypothesis testing. The RBF network achieved the highest median R2 and significantly outperformed the other variants, while the BR model demonstrated robust generalization. SCG and LM converged faster and efficiently but with slightly lower accuracy. Sensitivity analysis identified interconnected porosity as the primary predictor of UCS. These results establish RBF-based ANNs with appropriate regularization and feature importance assessment as a novel, practical, and reliable framework for UCS prediction in heterogeneous carbonate formations. Full article
(This article belongs to the Special Issue Research and Applications of Artificial Neural Network)
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