Machine Learning and Soft Computing: Current Trends and Applications

Dear Colleagues,

In contemporary times, the adoption of machine learning and soft computing techniques has established itself as a customary practice across research domains and industries such as healthcare, finance, manufacturing, energy, education, business management, and beyond. These methodologies have significantly bolstered decision-making capabilities and facilitated the automation of numerous processes. In order to stay abreast of the prevailing approaches within these realms, this Special Issue is dedicated to examining contributions that elucidate the latest trends and contemporary methodologies, within both industry and academia. Contributions encompass, though are not confined to, the following:

• Advanced Machine Learning Algorithms
• Deep learning architectures.
• Reinforcement learning.
• Ensemble methods.
• Transfer learning.

• Soft Computing Paradigms
• Fuzzy systems.
• Genetic algorithms.
• Neural networks.
• Swarm intelligence.

• Natural Language Processing
• Sentiment analysis.
• Topic modeling.
• Named entity recognition.

• Interdisciplinary Applications
• Healthcare informatics.
• Financial prediction and analysis.
• Image and speech recognition.

• Explainability and Interpretability
• Interpretable machine learning models.
• Explainable AI approaches.

• Ethical Considerations in Machine Learning
• Bias and fairness.
• Transparency and accountability.

We welcome both original research articles and comprehensive reviews that contribute to the understanding and development of machine learning and soft computing. Researchers are encouraged to present innovative solutions, share practical experiences, and highlight challenges and opportunities in this rapidly evolving field.

Yours sincerely,

Dr. Ansel Yoan Rodríguez González
Dr. Miguel Ángel Álvarez Carmona
Guest Editors

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• advanced machine learning algorithms
• deep learning architectures
• reinforcement learning
• ensemble methods
• transfer learning soft computing paradigms
• fuzzy systems
• genetic algorithms
• neural networks
• swarm intelligence natural language processing
• sentiment analysis
• topic modeling
• named entity recognition interdisciplinary applications
• healthcare informatics
• financial prediction and analysis
• image and speech recognition explainability and interpretability
• interpretable machine learning models
• explainable AI approaches ethical considerations in machine learning
• bias and fairness
• transparency and accountability