Artificial Intelligence

The Section Artificial Intelligence mainly covers topics of interest within unique 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 advanced applications of deep-learning-based AI.

The primary focus of this Section is only unique 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 Gradient Boosting Decision Tree (GBGT) (e.g., XGBoost) and Decision Forest, e.g., Random Forest, into a single decision tree (DT) .

Note that papers on hardware-based deep learning AI using FPGA, and other popular techniques, are handled by the Editor-in-Chief for the hardware related subsections. Papers on algorithmic deep learning are handled by the Editor-in-Chief for the Section Artificial Intelligence. Unique combinations of evolutionary computation, fuzzy logic, and deep learning are also of interest.

Subject Areas

Subject areas of interest include, without being limited to:

• Subsection for unique hardware-based deep learning AI;
• Subsection for algorithmic deep learning AI using machine learning;
  • Algorithms and/or methods for conversion of GBGT and Decision Forest into a single DT;
  • Explainable AI (XAI);
  • Rule extraction;
  • White box machine learning and AI;
  • AI finance (credit scoring, fraud detection, Peer-to-Peer (P2P) social lending).