In the rapidly evolving fields of artificial intelligence (AI) and visual analytics, there is growing recognition of their combined potential to reshape diverse sectors, emerging as a crucial pathway for progressing research and practical applications. The synergy between AI-driven techniques—such as natural language processing, image and video processing, and data analytics—and visual analytics methodologies augments our ability to decipher complex data structures. Such interdisciplinary approaches reveal subtle data patterns, enhancing decision-making processes and establishing more effective predictive mechanisms essential for addressing societal and economic challenges and progress.

This Special Issue endeavors to delve deep into the interplay between AI and visual analytics, seeking contributions highlighting novel methodologies, computational strategies, systematic reviews, and system architectures of real-world applications. Our goal is to foster an interdisciplinary exchange of ideas, extending to areas including analytical decision-making, corporate strategy evolution, AI-driven collaborative frameworks, healthcare analytics, sustainability metrics, and innovative techniques for behavior and trend detection.

In the current era, where data stand as the backbone of contemporary challenges and opportunities, this Special Issue serves as a platform for researchers and professionals to showcase pioneering work, laying the foundation for breakthroughs that cater to the multifaceted challenges of our society. By combining AI and visual analytics, we are able to unlock unprecedented insights from data, driving our global community toward a more resilient and prosperous future.

Topics of interest include, but are not limited to, the following:

• Integrative visual analytics and artificial intelligence systems and approaches;
• AI and visual analytics to support decision-making;
• Techniques and methods in explainable AI;
• Visual analytic solutions for handling big data challenges;
• AI and visual analytics in strategic corporate planning;
• AI and visual analytics in healthcare;
• AI and visual analytics for social well-being and mental health;
• Facilitation of collaborative work through AI interventions;
• Insights into collaborative intelligence mechanisms;
• AI and visual analytics for sustainability and environmental analysis and management;
• AI and visual analytics for policy modeling and political decision support.

Prof. Dr. Kawa Nazemi
Prof. Dr. Nuno Datia
Prof. Dr. Rita Francese
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.

• artificial intelligence (AI)
• visual analytics
• natural language processing
• explainable AI
• big data analytics
• data interpretation