Hansung Lee

Assistant Professor

Andong National University

South Korea

Research Interests

Explore related searches

Professors in South Korea Professors in Computer Vision Computer Vision professors in South Korea

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Hansung Lee is an Assistant Professor at Andong National University in South Korea. His research areas include machine learning applications in medical diagnosis, particularly focusing on stroke disease prediction and elderly health monitoring systems. Recent articles authored by Dr. Lee explore advanced methodologies in image summarization, object detection, and bio-signal analysis to enhance healthcare solutions.

Articles (11)

Year:

2025

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Collaborators (5)

Kang Hee Cho

Chungbuk National University

SOUTH KOREA

View details

Se Jin Park

Korea Research Institute of Standards and Science

SOUTH KOREA

View details

Chee Meng Benjamin Ho

Korea Research Institute of Standards and Science

SOUTH KOREA

View details

Yang Sun Lee

Assistant Professor

Hanshin University

SOUTH KOREA

View details

Jang-Hee Yoo

Professor

University of Science and Technology

SOUTH KOREA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Hansung Lee

Research Interests

Explore related searches

Contact this professor

About

Articles (11)

MultiSHTM: Multi-Level Attention Enabled Bi-Directional Model for the Summarization of Chart Images

The Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection

Attack Graph Generation with Machine Learning for Network Security

AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals

A Novel on Conditional Min Pooling and Restructured Convolutional Neural Network

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost

An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale

Face Recognition at a Distance for a Stand-Alone Access Control System

Collaborators (5)

Kang Hee Cho

Se Jin Park

Chee Meng Benjamin Ho

Yang Sun Lee

Jang-Hee Yoo

Make your next move.