The performance of a newly designed evolutionary algorithm is usually evaluated by computational experiments in comparison with existing algorithms. However, comparison results depend on experimental setting; thus, fair comparison is difficult. Fair co…
Since recommender systems play an important role in our online experience today and are involved in a wide range of decisions, multiple stakeholders are requesting explanations for the corresponding algorithmic predictions. These demands—togethe…
A Survey of Stochastic Computing Neural Networks for Machine Learning Applications, by Y. Liu, S. Liu, Y. Wang, F. Lombardi, and J. Han, IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 7, July 2021, pp. 2809–2824.
While Explainable Artificial Intelligence (XAI) is increasingly expanding more areas of application, little has been applied to make deep Reinforcement Learning (RL) more comprehensible. As RL becomes ubiquitous and used in critical and general public …
The byline of “Meaningful Big Data Integration for a Global COVID-19 Strategy” in November 2020 issue of IEEE Computational Intelligence Magazine is corrected as follows:
Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.
In the era of the Internet of Things and Big Data, data scientists are required to extract valuable knowledge from the given data. They first analyze, cure and pre-process data. Then, they apply Artificial Intelligence (AI) techniques to automatically …
Time series classifiers are not only challenging to design, but they are also notoriously difficult to deploy for critical applications because end users may not understand or trust black-box models. Despite new efforts, explanations generated by other…
Presents the CIS calendar of upcoming events and meetings.
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.