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Scope

The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.

TETCI is an electronics only publication. TETCI publishes six issues per year.

Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.

Impact Score

TETCI Impact Score 2023

The values displayed for the journal bibliometrics fields in IEEE Xplore are based on the Journal Citation Report from Clarivate from the 2022 report released in June 2023. Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals.  Find out more about IEEE Journal Rankings.


Featured Paper

Skeletal Video Anomaly Detection Using Deep Learning: Survey, Challenges, and Future Directions

tetci
Authors: Pratik K. Mishra, Alex Mihailidis, and Shehroz S. Khan
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 8, Issue 2 – April 2024

Abstract: The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground.


Read more on IEEE Xplore: https://ieeexplore.ieee.org/document/10453042