Advances in Machine Learning, Deep Learning and Computer Vision

Dear Colleagues,

Engineering applications are benefiting greatly from machine learning, deep learning, artificial intelligence, and its learning and adaptation paradigms. The development of machine learning and artificial intelligence paradigms with reference to data analytics and optimization has been a major contributor to numerous research streams in the era of cutting-edge research. As a result, the rapid advancement of computer science research has increased the requirement for an in-depth convergence of computing paradigms for deep learning, machine learning and artificial intelligence. Furthermore, it is practical and sound to use such systems for engineering and data analytics applications.

In order to raise awareness about the relevance and value of numerous engineering applications, this Special Issue integrates deep learning, machine learning and artificial intelligence computer vision, sophisticated data analytics and optimization prospects. It is also crucial to note that computing vision for machine learning and artificial intelligence, as well as their prediction and optimization methodologies, have not been sufficiently examined from the standpoint of biomedical engineering, remote sensing, autonomous vehicles, safe cities and the connected research challenges. In addition, there are a number of important interconnected challenges that must be addressed in the context of deep learning, machine learning and artificial intelligence systems, including the composition of biomedicine, medical imaging modalities, biologically inspired learning and adaptation in the self-configuration of network services, etc. Clearly, these difficulties are opening up a world of possibilities for scholars.

The following areas of interest, among others, are welcome in this Special Issue:

• Pattern recognition;
• Machine learning;
• Face recognition;
• Deep learning;
• CT/MRI imaging for cancer detection;
• Biomedical imaging;
• Computer vision;
• Safe cities;
• Sensors;
• Remote sensing;
• Autonomous vehicles;
• Object detection. 

Dr. Karri Chiranjeevi
Prof. Dr. Ram Bilas Pachori
Dr. Lalit Garg
Guest Editors

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