Special Issue "Deep Neural Network for Intelligent Systems"

Submission Deadline: 29 December 2021 (closed)
Guest Editors
Dr. Kong Fah TEE, University of Greenwich, United Kingdom.
Dr. Abdul Quadir Md, Vellore Institute of Technology (VIT), India.
Dr. Arun Kumar Sivaraman, Vellore Institute of Technology (VIT), India.


Artificial Intelligence has freshly increased irresistible consideration, primarily because to the Deep Learning applications in various fields such as academic and business world. Deep learning methods have meaningfully outclassed obsolete machine learning methods for the establishment of a multipart computational network that can perform the levels of neural networks in correlation and can acquire multifaceted structures in huge datasets.


The composite manner of diverse deep neural networks and the modification of many hyper-parameters to recognize the fundamental information makes it fascinating to learn. From diverse perceptions, deep neural network has been visualized in serval areas and dissimilar hybrid representations have been established. Nevertheless, a lot of progressions needs to be done to enhance the structural and functional characteristics of deep neural networks in diverse fields. This Special Issue will present a stage to scientists and scholars to dispense state of the art resolutions in the domain.


The objective of this Special Issue is to highlight current developments in the domain and to encourage research and expansion happenings in the field of deep neural networks, by dissemination high standard research and review articles in this swiftly rising collaborative domain.


Scope of Special Issue:


- Deep learning for Human Computer Interaction

- Deep Learning for Healthcare application

- Deep Learning for Cyber Security and Internet of Things

- Deep Learning for Machine Intelligence

- Deep Learning for Automation Systems

Deep Learning, Human Computer Interaction, Machine Learning, Computational Intelligence, Internet of Things, Artificial Intelligence

Published Papers
  • Abnormality Identification in Video Surveillance System using DCT
  • Abstract In the present world, video surveillance methods play a vital role in observing the activities that take place across secured and unsecured environment. The main aim with which a surveillance system is deployed is to spot abnormalities in specific areas like airport, military, forests and other remote areas, etc. A new block-based strategy is represented in this paper. This strategy is used to identify unusual circumstances by examining the pixel-wise frame movement instead of the standard object-based approaches. The density and also the speed of the movement is extorted by utilizing optical flow. The proposed strategy recognizes the unusual movement… More
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