A section of Electronics (ISSN 2079-9292).
The Artificial Intelligence section mainly covers topics of interest within hardware-based deep learning AI and algorithmic deep learning AI using machine learning. The purpose of this section is to bring together researchers and engineers, from both academia and industry, to present novel ideas and solid research on the hardware and algorithmic aspects of the industrial applications of deep learning-based AI.
The primary focus of this section is hardware-based deep learning AI. This section also focuses on the black box nature of deep neural networks and shallow NNs, transparency, interpretability and explainability of deep neural networks (DNNs), and algorithms and/or methods for the conversion of CNN into decision trees (DTs) and random forest.
Note that papers on hardware-based deep learning AI using FPGA, and others, are handled by the Editor-in-Chief for the Hardware subsection. Papers on algorithmic deep learning are handled by the Editor-in-Chief for the Artificial Intelligence section. Simple combinations of evolutionary computation, fuzzy logic, and deep learning hardware are also of interest.
Subject areas of interest include, without being limited to:
- Subsection for hardware-based deep learning AI
- Subsection for algorithmic deep learning AI using machine learning
- Algorithms and/or methods for conversion of CNN into DTs and random forest
- Explainable AI (XAI)
- Rule extraction algorithms/methods for shallow NNs
- Black box nature of DNNs versus the transparency of DNNs
Following special issues within this section are currently open for submissions:
- Challenges and Opportunities of Artificial Intelligence for Electronic Design: Theory and Applications (Deadline: 31 August 2019)
- Hardware-Driven Neuromorphic System: Theories, Components, and Integrated Circuits (Deadline: 30 November 2019)
- Application of Artificial Intelligence and Deep Learning in Wireless Communications Systems (Deadline: 30 November 2019)
- Applications and Methodologies of Artificial Intelligence in Big Data Analysis (Deadline: 30 November 2019)
- IoT Technologies for Smart Cities (Deadline: 30 November 2019)
- Electronic Solutions for Artificial Intelligence Healthcare (Deadline: 30 November 2019)
- Electronics and Dynamic Open Innovation (Deadline: 30 December 2019)
- Recent Machine Learning Applications to Internet of Things (IoT) (Deadline: 31 December 2019)
- Intelligent Modelling and Control in Renewable Energy Systems (Deadline: 31 December 2019)
- Deep Learning Applications with Practical Measured Results in Electronics Industries (Deadline: 31 December 2019)
- Artificial Neural Network Applications in Power Electronics, Communication Networks and IoT (Deadline: 31 December 2019)
- Machine Learning Techniques for Assistive Robotics (Deadline: 31 December 2019)
- Face Recognition and Its Applications (Deadline: 31 December 2019)
- Analog and Digital Circuit Design Techniques and Systems for Machine Learning (Deadline: 31 January 2020)
- Big Data Analytics for Smart Cities (Deadline: 29 February 2020)
- Deep Neural Networks and Their Applications (Deadline: 31 March 2020)
- Intelligent Electronic Devices (Deadline: 31 March 2020)
- Computational Intelligence in Healthcare (Deadline: 30 April 2020)
- Recent Advances in Virtual Reality and Augmented Reality (Deadline: 31 May 2020)
- Applications of Machine Learning in Big Data (Deadline: 31 May 2020)
- AI Enabled Communication on IoT Edge Computing (Deadline: 31 December 2020)