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 152
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
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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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