Presents the CIS society calendar of upcoming events and meetings.

Presents the front cover for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The employment of machine learning often requires a large amount of data, including our sensitive and personal information, to train models for various applications. With growing worldwide concerns over data privacy, particularly with the recent introduction of the EU’s General Data Protection Regulation (GDPR), it has become an important topic for AI research and application to implement data privacy-preserving

Hi all, To borrow from The Bard, what’s in a name? The world is awash with the hype, the reality, the potential, and the concerns of Artificial Intelligence. But what constitutes AI? That depends on who, and when, you ask. When I was a kid (academically speaking), tasked with teaching a course on AI, I “knew” what it was: systems with propositions consisting of discrete symbols, manipulated through

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Welcome to a new column of the IEEE Computational Intelligence Magazine devoted to Industrial Activities. In a previous communication1, I described the vision for the newly formed IEEE CIS Industrial and Governmental Activities Committee (IGAC). With IGAC, we strive to provide services, products, and offerings of interest to Industry, ranging from webinars to tutorials, conferences, publications, standards

The Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Prof. Yongduan Song, talks about this reputable publication, his life and favorites.

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation, by S. Zhao, X. Yue, S. Zhang, B. Li, H. Zhao, B. Wu, R. Krishna, J. E. Gonzalez, A. L. Sangiovanni-Vincentelli, S. A. Seshia, and K. Keutzer, IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, No. 2, Feb 2022, pp. 473–493.

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Privacy-preserving deep learning with homomorphic encryption (HE) is a novel and promising research area aimed at designing deep learning solutions that operate while guaranteeing the privacy of user data. Designing privacy-preserving deep learning solutions requires one to completely rethink and redesign deep learning models and algorithms to match the severe technological and algorithmic constraints

Motivated by the increasingly powerful computing capabilities of end-user equipment, and by the growing privacy concerns over sharing sensitive raw data, a distributed machine learning paradigm known as federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing

Many real-world multi-objective optimization problems (MOPs) are characterized by a large number of decision variables, where the decision variables are mostly set to zero in the Pareto optimal solutions. Although some multi-objective evolutionary algorithms (MOEAs) have been tailored for large-scale MOPs in recent years, most of them do not consider the sparse nature of Pareto optimal solutions, and

The SARS-CoV-2 pandemic demonstrates the vulnerability of societies in a globalized world. As pathogens spread at exponential rates, rapid development of appropriate medical treatments and distribution of vaccinations are major challenges. Under these circumstances, authorities employ non-pharmaceutical interventions (NPIs) against the spread, which can impact the economy strongly. Hence, there is

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural network predictions. This tutorial provides

Presents the front cover for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Deep learning has been key to remarkable progress in artificial intelligence. Recently, I came across some intriguing applications of deep learning. Salvator Mundi is the most expensive painting ever sold in the world; however, it is still in debate whether the painting should be attributed to Leonardo da Vinci in whole, in part, or not at all. To solve this puzzle, an attorney developed a convolutional

Hi all,

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of IEEE CIS members who were elevated to the status of IEEE Fellow in 2022.

“For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained

In this paper, a prototypical deep evolutionary learning (DEL) process is proposed to integrate deep generative model and multi-objective evolutionary computation for molecular design. Our approach enables (1) evolutionary operations in the latent space of the generative model, rather than the structural space, to generate promising novel molecular structures for the next evolutionary generation, and

Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population’s implicit parallelism to jointly solve a set of tasks, hence creating avenues for skills transfer between them. Despite it being early days, the idea

Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimization is still an open issue due to its pairwise learning style. Maximizing AUC in the large-scale dataset can be considered as a non-convex and expensive

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

As the population in cities continues to increase, large-city problems, including traffic congestion and environmental pollution, have become increasingly serious. The construction of smart cities can relieve large-city problems, promote economic growth, and improve the quality of life for citizens. Intelligent transportation is one of the most important issues in smart cities that aims to make transportation

Presents the CIS society 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.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents the front cover for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Explainability is one of the most discussed AI topics in recent years, and with good reason. The complexity of systems powered by AI has increased to the level that humans are unable to understand how AI systems make decisions, which will prove an obstacle to AI adoption. Explanations behind AI’s decision-making processes can provide us with the trust we need. When AI is asked to show its work, this

Hey all, Well, it looks like I’m the President of the IEEE Computational Intelligence Society for 2022-2023. Talk about your “hard act to follow!” I have great admiration and affection for Bernadette Bouchon-Meunier who led us through the unprecedented time of the COVID-19 pandemic with such grace and acumen. I have been fortunate to have worked with her over the years in several capacities, have had

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the recipients of CIS society 2022 award winners.

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.

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 extract knowledge from data. Actually, AI is identified as a strategic technology and it is already part of our everyday life. The European Commission

The last decades have seen significant advancements in non-invasive neuroimaging technologies that have been increasingly adopted to examine human brain development. However, these improvements have not necessarily been followed by more sophisticated data analysis measures that are able to explain the mechanisms underlying functional brain development. For example, the shift from univariate (single

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 interpretable time series models are complicated for non-engineers to understand. The goal of PIP is to provide time series explanations that are tailored

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—together with the benefits of explanations (e.g., trust, efficiency, and sometimes even persuasion)—have triggered significant interest from researchers in academia

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 applications, it is essential to develop methods that make it better understood and more interpretable. This study proposes a novel approach to explain

Can satisfactory explanations for complex machine learning models be achieved in high-risk automated decision-making? How can such explanations be integrated into a data protection framework safeguarding a right to explanation? This article explores from an interdisciplinary point of view the connection between existing legal requirements for the explainability of AI systems set out in the General

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 comparison of multi-objective evolutionary algorithms is even more difficult since solution sets instead of solutions are evaluated. In this paper, the

Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this context, the objective of this paper is to develop a method for detecting the beginning of hard drive malfunction using streaming SMART data, allowing the user to take actions before the

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: