Special Issue "Deep Learning Trends in Intelligent Systems"

Submission Deadline: 15 December 2020
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Guest Editors
Dr. Gopal Chaudhary, Guru Gobind Singh Indraprastha University, India.
Dr. Manju Khari, Guru Gobind Singh Indraprastha University, India.
Dr. Bharat Rawal, Gannon University, USA.


Machine learning (ML) and artificial intelligence (AI) are turning out to be effective critical thinking procedures in numerous regions of research and industry, not least as a result of the ongoing accomplishments of deep learning (DL). They supplement one another, and the next advancement lies in pushing every one of them as well as in joining them. Various research disciplines, from computer science to medical science, pattern recognition, forensics science, and cyber-physical systems, as well as numerous organizations, accept that data-driven and “intelligent” solutions are essential to take care of a large number of their key issues. The vast use of these intelligent systems is due to its intelligent decision-making algorithms and techniques. These systems incorporate machine learning, deep learning, transfer learning, and neuro-fuzzy inference techniques, AI-based solutions that are material in the industrial Internet of Things, and machine-to-machine interfaces. The present pattern is to combine data from different sorts of sensors to have an increasingly gainful and progressively robust framework like assistive frameworks using adaptive learning and decision making.


Within this framework, this Special Issue tries to bring together all the latest developments in the area of “Deep Learning trends in Intelligent Systems.” It aims at promoting the recent advances in this research field while highlighting the main real-world challenges.

Potential topics include, but are not limited to, the following:
• Activity recognition: object recognition and pose estimation for assistive robotics, and emotion recognition
• Intelligent autonomous systems
• Deep learning-based intelligent control
• Intelligent modeling, identification and optimization
• Applications in vision, audio, speech, natural language processing, robotics, neuroscience, or any other field
• Deep network compression/acceleration in pattern recognition applications
• Deep neural network in safety-critical or low-cost pattern recognition
• Developing new models for multimodal deep learning
• Signal processing for intelligent systems
• Artificial intelligence for intelligent systems
• Big Data for intelligent sensors systems
• Low-cost solutions for intelligent systems
• Hardware design and solutions for intelligent systems
• Intelligent systems in the biomedical context
• New trends and applications for intelligent systems