Thi Thi Zin, Pyke Tin, H. Hama, T. Toriu（ 担当： 共著 , 範囲: 発案、実験、考察、論文執筆） Springer International Publishing 2014年10月 詳細を見る

2025年3月19日 · Monitoring the daily activity levels of black cattle is a crucial aspect of their well-being. The rapid advancements in artificial intelligence have transformed computer vision applications, including object detection, segmentation, and tracking. This has led to more effective and precise monitoring techniques for livestock.

Thi Thi Zin (Member, IEEE) received the B.Sc. degree (Hons.) in mathematics from Yangon University, Myanmar, in 1995, the M.I.Sc. degree in computational mathematics from the University of Computer Studies, Yangon, Myanmar, in 1999, and the master’s and Ph.D. degrees in information engineering from Osaka City University, Osaka, Japan, in 2004 ...

Dr. Thi Thi Zin is the First Woman Professor in the Faculty of Engineering, University of Miyazaki, Japan. Thi Thi Zin was born in Yangon, Myanmar. She received her Ph.D. degree in Electrical and Information Engineering from Osaka City University, Japan in 2007.

Thi Thi Zin, Saw Zay Maung Maung, Pyke Tin, Yoichiro Horii: Feature Detection and Classification of Cow Motion for Predicting Calving time. GCCE 2020: 305-306

Thi Thi Zin, Moe Zet Pwint, Su Myat Noe, I. Kobayashi. 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech) 564 - 565 2022.3

Thi Thi Zin's 14 research works with 27 citations and 489 reads, including: An Intelligent Method for Detecting Lameness in Modern Dairy Industry

株式会社NTTデータ経営研究所様を代表機関として、株式会社NTTドコモ北海道支社様、ホクレン農業協同組合連合会様、北海道イシダ株式会社様、きたみらい農業協同組合様、訓子府町様の6者と連携した共同プロジェクト「産学官連携によるローカル5Gを用いたスマート農業の実現に向けた実証実験」を開始しました｡. 林田 高典 さんらが、2021年10月12日-15日の4日間、京都市で開催された IEEE GCCE 2021 で Excellent Paper Award Gold Prize を受賞しました！ …

Thi Thi Zin received the B. Sc. and M. I. Sc. degrees from Yangon University and University of Computer Studies, Yangon, Myanmar in 1995 and 1999, respectively. She received the M.E. degree from Osaka City University in 2004. She is a doctoral candidate in …

In this paper, we present a method for detecting lameness in dairy cows using an RGB camera and analyzing their walking behavior. Our proposed technique achieves an accuracy of 84.6% in classifying cows as healthy or lame. We conducted a series of real-life experiments to validate our classification results, comparing them with expert diagnoses.