Efficiency and Scalability of Advanced Machine Learning and Optimization Methods for Real-World Applications

Dear Colleagues,

Compared to synthetic mathematical problems, real-world problems pose distinct challenges and involve complex systems with interdependent components, which require advanced modelling and analysis techniques. Nonlinear behaviour, dependencies, and uncertainties further complicate the task, and require advanced machine learning methods that are capable of capturing such intricacies. Real-world applications must also grapple with massive datasets, leading to computational and memory constraints. To address these limitations, emerging methodologies such as transfer learning, federated learning, and quantum machine learning, must ensure their efficiency and scalability while processing extensive volumes of data. This Special Issue seeks submissions that not only present advanced techniques in this area, but also demonstrate improvements in their scalability and efficiency compared to existing approaches. The deployment of machine learning methods in real-world environments is of particular interest in this Issue. Case studies detailing the practical benefits and implementation challenges of these methods are invited. Discussions on the societal, ethical, and regulatory implications of deploying advanced machine learning systems are encouraged. Additionally, this Special Issue emphasizes techniques that accommodate the computational demands of large-scale datasets; benchmark datasets and evaluation metrics play a crucial role in addressing complex real-world problems.

Dr. Mehdi Neshat
Guest Editor

Manuscript Submission Information

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• artificial intelligence
• machine learning
• supervised learning
• reinforcement learning
• domain adaptation
• scalability
• optimisation
• evolutionary intelligence
• swarm optimisation methods
• multiobjective optimisation methods
• real-world problems