While Big Data offers the great potential for revolutionizing all aspects of our society, harvesting of valuable knowledge from Big Data is an extremely challenging task. The large scale and rapidly growing information hidden in the unprecedented volumes of non-traditional data requires the development of decision-making algorithms. Recent successes in machine learning, particularly deep learning, has led to breakthroughs in real-world applications such as autonomous driving, healthcare, cybersecurity, speech and image recognition, personalized news feeds, and financial markets.

While these models may provide the state-of-the-art and impressive prediction accuracies, they usually offer little insight into the inner workings of the model and how a decision is made. The decision-makers cannot obtain human-intelligible explanations for the decisions of models, which impede the applications in mission-critical areas. This situation is even severely worse in complex data analytics. It is, therefore, imperative to develop explainable computation intelligent learning models with excellent predictive accuracy to provide safe, reliable, and scientific basis for determination.  

Numerous recent works have presented various endeavors on this issue but left many important questions unresolved. The first challenging problem is how to construct self-explanatory models or how to improve the explicit understanding and explainability of a model without the loss of accuracy. In addition, high dimensional or ultra-high dimensional data are common in large and complex data analytics. In these cases, the construction of interpretable model becomes quite difficult and complex. Further, how to evaluate and quantify the explainability of a model is lack of consistent and clear description. Moreover, auditable, repeatable, and reliable process of the computational models is crucial to decision-makers. For example, decision-makers need explicit explanation and analysis of the intermediate features produced in a model, thus the interpretation of intermediate processes is requisite. Subsequently, the problem of efficient optimization exists in explainable computational intelligent models. These raise many essential issues on how to develop explainable data analytics in computational intelligence. 

This Topical Collection 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 Topical Collection 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 

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 

All the papers will go through peer review,  and will be reviewed by at least three reviewers. A thorough check will be completed, and the guest editors will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.

The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue (with at least 30% difference from the original works).

Paper submissions for the special issue should strictly follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).

Manuscripts must be submitted to the journal online system at https://www.editorialmanager.com/ncaa/default.aspx.
Authors should select “TC: Interpretation of Deep Learning” during the submission step ‘Additional Information’.