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 August 2025 | Viewed by 433
Special Issue Editors
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; environmental applications; statistical methods; computational techniques; advanced process technologies; methodological expertise
Special Issues, Collections and Topics in MDPI journals
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:
- Novel ANN architectures and training algorithms;
- Optimization techniques for neural network performance;
- Applications in environmental monitoring and remediation;
- Predictive modeling for chemical and biological systems;
- Hybrid approaches combining ANNs with other computational methods;
- Real-time monitoring and process control applications;
- Comparative studies of different neural network paradigms;
- 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 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
- 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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