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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ICSS, IPSJ-SPT | 2024-03-22 11:20 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
Evaluation of Feature Inference Risk from Explainable AI metrics LIME and Shapley Values Ryotaro Toma, Hiroaki Kikuchi (Meiji Univ.)  ICSS2023-88 |
Explainability has gained attention to ensure fairness and transparency in machine learning models, providing users with... [more] | ICSS2023-88 pp.137-144 |
| SIS | 2024-03-14 14:30 |
Kanagawa | Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
Explainability for Graph-based Fake News Detection using Topic and Propagation-aware Visualization Kayato Soga, Soh Yoshida, Mitsuji Muneyasu (Kansai Univ.) SIS2023-49 |
Based on the observation that the structure of information propagation networks differs from that of real news, methods ... [more] | SIS2023-49 pp.21-26 |
| SIP, IT, RCS | 2024-01-19 14:30 |
Miyagi | (Primary: On-site, Secondary: Online) |
Infant Detection in Passenger Vehicles Using Millimeter Wave FMCW-MIMO Radar and CFAR Algorithm Kotone Sato, Steven Wandale, Koichi Ichige (Yokohama National Univ.), Kazuya Kimura, Ryo Sugiura (Murata Manufacturing) IT2023-71 SIP2023-104 RCS2023-246 |
This paper implements several proposed features using the CFAR algorithm, then constructs a concise decision tree model ... [more] | IT2023-71 SIP2023-104 RCS2023-246 pp.223-228 |
| DE, IPSJ-DBS | 2023-12-26 14:00 |
Tokyo | Institute of Industrial Science, The University of Tokyo | Interpretation of unsupervised clustering based on XAI Yu Sasaki, Fumiaki Saitoh (CIT) DE2023-28 |
Explainable Artificial Intelligence (XAI) aims to introduce transparency and interpretability into the decision-making o... [more] | DE2023-28 pp.1-6 |
| EMCJ | 2023-11-24 13:25 |
Tokyo | Kikai-Shinko-Kaikan (Primary: On-site, Secondary: Online) |
A Study on Explainability of Convolutional Neural Network Predicting Electric Characteristics of Automotive Wire Harness Based on Score Regression Activation Mapping (Score-RAM) Syumpei Ebina, Tadatoshi Sekine, Shin Usuki, Kenjiro T. Miura (Shizuoka Univ.) EMCJ2023-74 |
In this report, we propose score regression activation mapping (Score-RAM) based on explainable artificial intelligence.... [more] | EMCJ2023-74 pp.13-18 |
| KBSE, SC | 2023-11-18 10:20 |
Miyagi | Sento Kaikan | Towards Standardized Data Model for Service Recommendation Based on User Needs Takuya Nakata, Sinan Chen (Kobe Univ.), Sachio Saiki (Kochi Univ. of Tech.), Masahide Nakamura (Kobe Univ.) KBSE2023-44 SC2023-27 |
Due to the internet's proliferation, digital devices, and COVID-19's impact, online service use has soared, driving dema... [more] | KBSE2023-44 SC2023-27 pp.57-62 |
| MI | 2023-09-08 11:20 |
Osaka | (Primary: On-site, Secondary: Online) |
A Study on Identifying Gender Differences Using Deep Learning from Retinal Fundus Images Shota Tsutsui (Waseda Univ.), Ichiro Maruko, Moeko Kawai (TWMU), Yoichi Kato, Jun Ohya (Waseda Univ.) MI2023-17 |
Previous studies show that a properly designed and trained deep learning algorithm is capable to identify the gender of ... [more] | MI2023-17 pp.8-11 |
| DE | 2023-06-16 09:10 |
Tokyo | Musashino University (Primary: On-site, Secondary: Online) |
A POI recommendation method with explanatory nature for user's purpose based on online review information Hajjime Katayama, Taketoshi Ushiama (Kyushu Univ.) DE2023-2 |
In this study, we propose a method in which the purpose of searching for a POI is entered as a query, and a POI suitable... [more] | DE2023-2 pp.7-12 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 17:00 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Explainable Deep Clustering for Wafer Defect Pattern Classification Yuki Okazaki, Hiroki Takahashi (The Univ. of Electro-Communications) PRMU2022-115 IBISML2022-122 |
Classification of specific defect patterns on semiconductor wafers is important in manufacturing processes. Recently, ma... [more] | PRMU2022-115 IBISML2022-122 pp.299-304 |
| IBISML | 2022-12-23 10:50 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Interpretable Deep Image Classifier with Class-distinguishable Concept Text Kazuhiro Saito, Kazuto Fukuchi (Univ.Tsukuba), Jun Sakuma (Univ.Tsukuba/RIKEN) IBISML2022-55 |
(To be available after the conference date) [more] | IBISML2022-55 pp.86-93 |
| SIP | 2022-08-26 14:26 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Generation method of Adversarial Examples using XAI Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) SIP2022-72 |
With the advancement of AI technology, AI can be applied to various fields. Therefore the accountability for the decisio... [more] | SIP2022-72 pp.115-120 |
| PRMU, IPSJ-CVIM | 2022-05-13 10:30 |
Aichi | Toyota Technological Institute | Visualization of Decision Rationale Using Social and Physical Attention Mechanisms in Human Trajectory Prediction Model Masahiro Kato, Norimichi Ukita (TTI) PRMU2022-3 |
There is a great deal of interest in explainable AI that clarifies the basis of decisions, such as why a model makes a p... [more] | PRMU2022-3 pp.12-17 |
| NS, IN (Joint) |
2022-03-10 11:00 |
Online | Online | Experimental Evaluation of Influence of Distributing Deep Learning-Based IDSs on Their Classification Accuracy and Explainability Ayaka Oki, Yukio Ogawa, Kaoru Ota, Mianxiong Dong (Muroran-IT) IN2021-33 |
Increased data traffic associated with the wide spread usage of IoT devices accentuates the risk of large-scale cyber at... [more] | IN2021-33 pp.13-18 |
| MBE, NC (Joint) |
2022-03-02 11:00 |
Online | Online | Learning of a stacked autoencoder with regularizers added to the cost function, evaluation of their effectiveness, and clarification of its information compression mechanism Masumi Ishikawa (Kyutech) NC2021-49 |
Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sp... [more] | NC2021-49 pp.17-22 |
| LOIS, ICM | 2022-01-27 15:25 |
Online | Online | [Invited Talk]
Cyber Security with Human-in-the-Loop Machine Learning Masato Uchida (Waseda Univ.) ICM2021-38 LOIS2021-36 |
There have been many studies on methods to detect various malicious activities in cyberspace using machine learning mode... [more] | ICM2021-38 LOIS2021-36 pp.31-33 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 12:10 |
Online | Online | Deep learning of mixture of continuous and categorical data with regularizers added to the cost function and evaluation of the effectiveness of sparse modeling Masumi Ishikawa (Kyutech) NC2021-45 |
Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sp... [more] | NC2021-45 pp.65-70 |
| IBISML | 2022-01-18 11:15 |
Online | Online | [Invited Talk]
TBA Jun Sakuma (Tsukuba Univ./RIKEN) |
Explainability is one of the key elements required in medical image diagnosis using deep image recognition models. In th... [more] | |
| ET | 2021-12-11 13:00 |
Online | Online | Development of trait-based neural automated essay scoring incorporating multidimensional item response theory Takumi Shibata, Masaki Uto (UEC) ET2021-33 |
In recent years, deep neural network (DNN)-based automated essay scoring (AES) models that can simultaneously predict th... [more] | ET2021-33 pp.23-28 |
| SWIM | 2021-11-27 14:10 |
Online | Online | Studies of maximum electricity forecasting model including electricity market price
-- Time series analysis with extra regressors added -- Hiroyuki Ogura (Nihon Univ.), Shunsuke Managi (Kyushu Univ.) SWIM2021-27 |
As one of the solutions to the difficult problem of achieving both stable electricity supply and decarbonization, improv... [more] | SWIM2021-27 pp.7-14 |
| BioX | 2021-10-14 13:35 |
Online | Online | [Invited Talk]
"Explanation" in Machine Learning Satoshi Hara (Osaka Univ.) BioX2021-43 |
(To be available after the conference date) [more] | BioX2021-43 pp.3-6 |
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