Developing applications for real-world problems using machine learning and IoT
Graduate School of Science and Engineering
Department of Engineering
SHIGEI Noritaka
Background and objectives of activities
Developing applications that utilize machine learning, such as deep learning, and IoT to address various problems in the real world, with the goal of contributing to maintaining a safe, secure, comfortable, and convenient society, is the research topic of the Master's thesis in the Department of Engineering and the Senior Thesis in the Department of Advanced Engineering. Through this development, we aim to contribute to society by devising effective methods for building applications that utilize machine learning and the IoT to address various problems, and to train human resources that can work in the real world as systems engineers and other IT engineers.
Summary of Activities
Security-related themes include "Personal identification system without recognition actions," "Person tracking camera with personal identification," "Automatic operation of model car for indoor patrol," "Secure multiparty computation," "Land use classification and hazard estimation from aerial photos and map images," "Ground strength estimation using machine learning," and "Non-contact interface," "Automated appearance evaluation of weather-resistant steel," and "Automated classification of customer inquiries," among others. He is the author of "Soil Strength Estimation Using Machine Learning" as a topic related to disaster prevention, and "Non-contact Interface," "Automated Appearance Evaluation of Weather-Resistant Steel," and "Automated Classification of Customer Inquiries" as other topics related to disaster prevention. For his research presentations, he has received nine awards from the Information Processing Society of Japan, the Kyushu Branch of the Japan Society for Fuzzy Intelligent Informatics, the Western Branch of the Japan Society of Civil Engineers, and IAENG.
Expected Benefits
Security related themes are expected to contribute to the realization of a safe living environment and the improvement of the working environment. Disaster prevention topics are expected to help reduce landslide disasters. Automatic classification of inquiries is expected to contribute to improving the working environment. Evaluation of the appearance of weathering steel is expected to contribute to more efficient maintenance and inspection of bridge infrastructure.