Artificial Intelligence

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

Subject areas of interest include, without being limited to: 

1. Subsection for hardware-based deep learning AI 
2. 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