Computational Intelligence Journal Special Issue on Explainable Machine Learning in Methodologies and Applications
https://onlinelibrary.wiley.com/page/journal/14678640/homepage/special_issue...
This special issue aims to bring together original research articles and review articles that will present the latest theoretical and technical advancements of machine and deep learning models. We hope that this Special Issue will: 1) improve the understanding and explainability of machine learning and deep neural networks; 2) enhance the mathematical foundation of deep neural networks; and 3) increase the computational efficiency and stability of the machine and deep learning training process with new algorithms that will scale. Potential topics include but are not limited to the following: Interpretability of deep learning models Quantifying or visualizing the interpretability of deep neural networks Neural networks, fuzzy logic, and evolutionary based interpretable control systems Supervised, unsupervised, and reinforcement learning Extracting understanding from large-scale and heterogeneous data Dimensionality reduction of large scale and complex data and sparse modeling Stability improvement of deep neural network optimization Optimization methods for deep learning Privacy preserving machine learning (e.g., federated machine learning, learning over encrypted data) Novel deep learning approaches in the applications of image/signal processing, business intelligence, games, healthcare, bioinformatics, and security Important Dates
Deadline for Submissions: March 31, 2022 First Review Decision: May 31,2022 Revisions Due: June 30, 2022 Deadline for 2nd Review: July 31, 2022 Final Decisions: August 31, 2022 Final Manuscript: September 30, 2022