Manuscript Topics

The rapid growth of big data and the advancement of scientific programming methods have attracted considerable attention. We have witnessed the impressive performance of machine learning (ML) in dealing with big data; a branch of artificial intelligence (AI) that focuses on the use of data and algorithms to build models for predicting useful results. Due to the growth of computational power, ML has become the most prominent subset of AI and has many real-world applications. Different new techniques in machine learning and data mining provide efficient tools to assist systems such as medical diagnostics and patient monitoring. These techniques are widely used as a screening tool or as an aid to diagnosis, allowing for fast and informed decisions, especially those with big data sets from multiple sources. However, such large amounts of training examples lead to more complex models that are not easy to interpret and reproduce, leaving much room for improvement.

The aim of this Special Issue is to bring together academic and industrial practitioners to exchange and discuss the latest mathematical modelling techniques for machine learning and their applications. Original research and review articles are welcome.

Potential topics include but are not limited to the following:
• Machine learning and its optimization for big data analytics
• Theoretical analyses of machine learning algorithms
• Novel machine learning techniques
• Machine learning for daily living activities
• Neural network-based systems
• Optimization Algorithms in machine learning
• Robustness in Machine Learning
• Machine learning techniques for pattern recognition
• Data mining and knowledge discovery techniques for scientific design
• Machine learning techniques for IoT applications
• Deep learning model optimization

Keywords: machine learning; optimization; data mining; pattern recognition; IoT; deep learning

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