Machine Learning and Reasoning: Advanced Machine Intelligence and Applications in Health Informatics

Dear Colleagues,

Artificial intelligence has evolved into a large branch of machine reasoning and learning. The learning component of machine intelligence has propelled the design and application of machine learning algorithms and models. This technique has received significant research interest in various areas, such as medicine, the internet of things, intrusion detection, network innovation, and others. Interestingly, recent studies have prompted the advancement in the design and use of classical machine learning algorithms to obtain high-performing neural network models, popularly called deep learning. Outstanding performance improvements have been achieved using models derived from deep learning networks, which have reported successful classification, detection, localization, and segmentation tasks. However, the handcrafted method used in developing these networks has further revealed latent deficiencies necessitating the use of intelligent algorithms to overcome such challenges. The design and integration of nature-inspired metaheuristic algorithms have been proposed in the literature, which is now being leveraged to optimize neural networks and neural network evolution. While this has yielded some positive research breakthroughs, we perceive a significantly unharnessed innovative combination of these methods, which can further advance research. Moreover, the increasing need to combine and close the divide between machine reasoning and learning is another virgin research area we motivate for discussion in this Special Issue. Considering these research gaps, we encourage further investigation into the research niche that would result in generating quality findings that demonstrate new algorithmic solutions, advancement of machine intelligence, and address challenging problems in medical image processing, abnormality detection, surveillance, drug design, intrusion detection, industrial machine automation, and vehicular and pedestrian automation.

We also encourage submissions to the Special Issue on the design of models, hybrids, algorithms, and implementation of such techniques. Furthermore, we seek to promote the investigation of a novel and innovative combination of existing methods to reveal what might have been overlooked in the literature.

Prof. Dr. Absalom El-Shamir Ezugwu
Dr. Olaide Nathaniel Oyelade
Dr. Laith Abualigah
Guest Editors

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• machine learning
• deep learning
• image processing
• computer vision
• model and image synthesization
• nature-inspired metaheuristic algorithms
• adversarial networks in metaheuristics