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Intelligent Information Environment Lab
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A lab that uses machine learning to extract meaning from data and applies it to areas such as information recommendation.
AboutAbout the lab
Extracting the meaning and patterns in data with machine learning.
The Intelligent Information Environment Lab studies how to use machine learning and deep learning to extract meaning and patterns from data, and apply them to information sharing, search, and recommendation.
The lab originally studied frameworks for realizing "multi-agent" systems, in which multiple software agents, each acting on behalf of a person, work together cooperatively, along with their applications.
Recently, it has worked on analyzing learners' reflective writing, analyzing movement data such as that from buses, and recommending products and places.
On the lab's website, it is presented under the name "Mine Lab."
Key terms
- Multi-agent system
- A mechanism in which multiple software agents, each acting on behalf of a person, work together cooperatively to solve problems or carry out tasks.
- Probe data
- Driving data that records a vehicle's position, date and time, speed, acceleration, and so on.
- Large vision-language model
- A large AI model that handles images and language together.
NewsLab news
A master's student won the Excellent Presentation Award at DICOMO2026
A master's student won the Encouragement Award at the SMASH25 Summer Symposium
A master's student won the Excellence Award at the SMASH25 Winter Symposium
A doctoral student received the 2024 IEEE Fukuoka Section Student Research Encouragement Award (24th)
ResearchResearch themes
Depending on the type of data, the lab studies learning semantic representations of data with machine learning and deep learning, extracting meaning and patterns, and making recommendations. It mainly works with real-world data, tackling a wide variety of data such as education, mobility, text, and images with language.
*Illustrative imageEstimating learning status and supporting learning
This research estimates learning status and changes in grades from learners' reflective writing and instructors' reports, and explains the reasons. In joint research with the National Center for University Entrance Examinations, the lab is also working on automatic scoring of short-answer responses.
*Illustrative imageUsing movement data from buses and taxis
From probe data collected by taxis, buses, and dashcams, the lab estimates travel time and delay between bus stops, driver characteristics, dangerous driving, and more. For route buses entering Ito Campus, it also estimates waiting time at Kyudai-Gakkentoshi Station.
*Illustrative imageRecommendation and text mining
The lab studies estimating emotions such as sadness, joy, and anger from a speaker's text, and extracting expressions such as elements and amounts from patent documents. It also studies finding and recommending lesser-known places from data on places people have visited.
*Illustrative imageUsing vision-language models
This research proposes a representation learning method that simultaneously mitigates fairness bias and vulnerability to input noise in large vision-language models, which handle images and language together. The lab also studies generating humorous expressions that fit an image.
MembersFaculty
The members of the Intelligent Information Environment Lab are faculty and students in the doctoral, master's, and undergraduate programs.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: “Big Data ( )” Learntek / CC0 (cropped)
- Estimating learning status and supporting learning: “Students they're writing the examination in the hall” Hauwasysy / CC BY-SA 4.0 (cropped)
- Using movement data from buses and taxis: “Bus und Taxi in London” Pedemann / CC BY-SA 4.0 (cropped)
- Recommendation and text mining: “Word Cloud DPD” MissLunaRose12 / CC BY-SA 4.0 (cropped)
- Using vision-language models: “Ceci n'est pas une banane by Max Gruber” Max Gruber / CC BY 4.0 (cropped)
- Related lab (Behavior Informatics Lab): “The scope of embodied cognition fr” John J. Madrid / CC BY 4.0 (cropped)
- Related lab (Humanophilic Systems Lab): “Iot, Internet of Things Colorful Typography 01” Chiplanay / CC BY-SA 4.0 (cropped)
- Related lab (Hacking Reality Lab): “InclusiveGameLab Person-Playing-Virtual-Reality 12 CC-BY-SA” InclusiveGameLab / CC BY-SA 4.0 (cropped)
- Related lab (Intelligent Software Engineering Lab): “Programmers” Lbronn / CC BY-SA 4.0 (cropped)
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