Bo WU

Assistant Professor

Tokyo Institute of Technology

Japan

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Bo Wu is an Assistant Professor at Tokyo University of Technology in Japan. His research focuses on machine learning applications in behavior analysis, as well as the integration of traditional medicine into modern medical practices. Recent publications include studies on cognitive impairment detection, personal authentication systems using deep learning, and community detection methodologies. Additionally, he has explored the use of algorithms in analyzing labor behaviors in agricultural settings.

Articles (10)

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Collaborators (4)

Atsushi OGIHARA

Waseda University

JAPAN

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Qun Jin

Waseda University

JAPAN

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Shoji Nishimura

Professor

Waseda Daigaku Riko Gakujutsuin

JAPAN

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Ran Dong

Lecturer

Chukyo University

JAPAN

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Bo WU

Research Interests

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Articles (10)

Analysis, Evaluation, and Prediction of Machine Learning-Based Animal Behavior Imitation

Integrating Acupuncture and Herbal Medicine into Assisted Reproductive Technology: A Systematic Review and Meta-Analysis of East Asian Traditional Medicine

Community-oriented multi-scale heterogeneous community detection using weighted positives and debiased negatives

A Deep-Learning-Driven Aerial Dialing PIN Code Input Authentication System via Personal Hand Features

A real-time recognition gait framework for personal authentication via image-based neural network: accelerated by feature reduction in time and frequency domains

Pre-braking behaviors analysis based on Hilbert–Huang transform

Behavioral Analysis of Mowing Workers Based on Hilbert–Huang Transform: An Auxiliary Movement Analysis of Manual Mowing on the Slopes of Terraced Rice Fields

A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the “spatial executive process”

Analysis on the Subdivision of Skilled Mowing Movements on Slopes

A New Early Warning Method for Human-Computer Interaction of Alzheimer's Disease Patients Based on Deep Learning

Collaborators (4)

Atsushi OGIHARA

Qun Jin

Shoji Nishimura

Ran Dong

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