九州大学 アジア・オセアニア研究教育機構 ブラウンバッグセミナー

Brown Bag Seminar No.
2024

295.

~12:10(wed.) 12:50

12:15-12:40

Introduction
Seminar()12:40-12:50

Q&A

12:10-12:15

Presentation

142

Online(Zoom)
Scan here forRegistration

https://temdec-med-kyushu-u-ac-jp.zoom.us/webinar/register/WN̲Qq1gQnonS2KcFd6oWUO2XASupported by Kyushu University,Q-AOS & TEMDEC

Advances in Temporal Pattern
Recognition and its Effect on AI

Chair: Assoc. Prof. Kim SCHUMACHER (Research Futures Coordinator of Q-AOS)

Key Words

Artificial Intelligence

Pattern Recognition
Time Series

Associate ProfessorTemporal Recognition
Sequences

Brian Kenji Iwana

Department of Advanced Information Technology,
Faculty of Information Science and Electrical Engineering, Kyushu University
Brian Kenji Iwana is an Associate Professor at theDepartment of Advanced Information Science in theGraduate School of Information Science and ElectricalEngineering, Kyushu University. He was born in
Mission Viejo, California, USA. He received a B.S. in
Computer Engineering of the University of California,Irvine, USA. After getting his Bachelor's, Brian Kenji
Iwana worked as a software developer at the National
Aeronautics and Space Administration (NASA) in
Mountain View, California. He returned to academiaand received a Ph.D. from the Graduate School ofInformation Science and Electrical Engineering,
Kyushu University. He was also a graduate from theGraduate Education and Research Training Program inDecision Science for a Sustainable Society, Kyushu
University. Since then, he worked as a Post Doc,Assistant Professor, and then an Associate Professor at
the Graduate School of Information Science andElectrical Engineering, Kyushu University.
Furthermore, he is afﬁliated with the International
Undergraduate Program In English (IUPE), Kyushu
University and the Graduate Program ofInterdisciplinary Policy Analysis and Design (GIPAD),
Kyushu University. He is an Associate Editor for thejournal, Springer Nature Computer Science, and has
served on many international conference programcommittees, such as ICDAR, ICFHR, AAAI, ICPR, andDAS. His research interests include time series
recognition, dynamic programming, artiﬁcial neuralnetworks, document recognition, and naturallanguage processing (NLP).

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 In recent years, the ﬁeld of ar tiﬁcial intelligence (AI) has
witnessed remarkable advancements and has worked its way
into normal daily life. However, despite being a foundation ofmany recent advances, temporal pattern recognition is one
area that is often overlooked. Temporal pattern recognition is
the recognition of time series and sequences.Fur thermore,much like other s truc tur al data domains, such as image
re co gni t i o n a n d gr ap h re co gni t i o n , temp o r a l p a t ter nrecognition has many difﬁcult challenges. Recognition of thisdata type is important across many domains, such as signals,
forecasting, natural language processing (NLP), bioinformatics,s en s or s , an d m ore . T hi s pre s en t a t ion w ill e x p l ain t h eunderlying principles behind temporal pattern recognition
techniques, including recognition using ar tiﬁcial neur aln e t w or k s (A N N), an d de s c r ib e s om e of t h e re cen t
advancements in the ﬁeld. Moreover, it examines the effects
temporal pattern recognition has had on the broader ﬁeld of AI
and their relationship to other ﬁelds and applications.
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