Vol. 11 No. 7s (2023): Advancements in Machine Learning for Computer Science and Decision Support Systems

Introduction:
The rapid development of machine learning techniques has revolutionized various domains, including computer science and decision support systems. Machine learning algorithms have demonstrated their effectiveness in solving complex problems, enabling intelligent decision-making, and enhancing system performance. This special issue aims to explore the latest advancements, methodologies, and applications of machine learning in the fields of computer science and decision support systems. The collection of papers in this special issue aims to provide a comprehensive understanding of the current state-of-the-art and future directions in this exciting area.

Topics of Interest:
- Machine learning algorithms and models for computer science applications
- Deep learning techniques for computer science and decision support systems
- Supervised, unsupervised, and semi-supervised learning in decision support systems
- Reinforcement learning for optimizing computer science processes
- Transfer learning and domain adaptation in computer science applications
- Ensemble learning methods for computer science and decision support systems
- Explainability and interpretability of machine learning models in decision support systems
- Integration of machine learning with computer science theories and methodologies
- Natural language processing and machine learning for decision support systems
- Big data analytics and machine learning for computer science applications
- Case studies and applications of machine learning in computer science and decision support .

Published: 01.07.2023

Research Article