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Intelligent Information Environment Lab
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The lab analyzes real-world data with machine learning and applies the results to areas such as information recommendation.
AboutAbout the lab
Extracting meaning from data and putting it to use.
The Intelligent Information Environment Lab uses machine learning and deep learning to extract meaning and patterns from real-world data, and applies them to recommendation and other tasks.
The lab mainly handles real data such as learners' reflection texts, bus travel records, and product reviews.
On the lab's website, the lab is introduced as "Mine Lab," and it operates together with Arakawa Lab and Fukushima Lab as the Human Information Systems Research Group.
Key terms
- Machine learning
- A method in which a computer learns rules and patterns from data and uses them for tasks such as estimation and recommendation.
- Probe data
- Driving data that records a vehicle's position, date and time, speed, acceleration, and so on.
- Named entity
- A word or phrase defined as a target to extract from documents. In patent documents, examples include elements and amounts.
NewsLab news
A master's student received the Outstanding Presentation Award at DICOMO2026
A master's student received the Graduate School of Integrated Frontier Sciences Award
A master's student received the Encouragement Award at the SMASH25 Summer Symposium
A master's student received the Excellence Award at the SMASH25 Winter Symposium
A doctoral student received the 2024 IEEE Fukuoka Section Student Research Encouragement Award
ResearchResearch themes
Depending on the data, the lab uses machine learning and deep learning to learn the meaning of the data, studies how to use it, and extracts meaning and patterns to make recommendations. All projects share the application of machine learning or deep learning to data, and the data are mainly real-world data.
*Illustrative imageEstimating learning status and supporting learning
From learners' reflection texts and instructors' report texts, the lab estimates learning status, learning ability, and changes in grades along with the reasons for them. The lab also works on automatic generation of advice text and on automatic scoring of short-answer responses, in joint research with the National Center for University Entrance Examinations.
*Illustrative imageEstimating driving conditions from movement data
Using probe data from route buses and drive recorders, the lab estimates travel time and delay time between bus stops, driver characteristics, and dangerous-driving situations. The lab also examined regional movement patterns from the usage history of on-demand buses.
*Illustrative imageEmotion estimation, patent information extraction, and recommendation
The lab studies the estimation of speakers' emotions and the extraction of named entities (such as elements and amounts) from patent documents. It also covers estimating the number of people who found a review helpful, predicting which products will be clicked, and recommending places that people visit.
*Illustrative imageBuilding a foundation for analyzing Diet proceedings
The lab collects data on Diet proceedings and political fund income and expenditure reports from the National Diet Library database and builds a foundation for analysis. Based on the Diet proceedings, the lab will go on to identify biased news reporting and disinformation on social media, and to identify political issues and trends.
MembersFaculty
The members of the Intelligent Information Environment Lab consist of faculty and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorTsunenori Mine
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Fediverse smartphone apps (photo by Elena Rossini)” Elexfedi / CC BY-SA 4.0 (cropped)
- Estimating learning status and supporting learning: “Learning-management-system.schon” Sandra Schon / CC BY 2.0 (cropped)
- Estimating driving conditions from movement data: “GPS Satellite NASA art-iif” NASA / Public domain (cropped)
- Emotion estimation, patent information extraction, and recommendation: “Text-mining process” Sahil Sewani / CC BY-SA 4.0 (cropped)
- Building a foundation for analyzing Diet proceedings: “Diet of Japan Kokkai 2009” Wiiii / CC BY-SA 3.0 (cropped)
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