H. Ohwada

Hayato Ohwada, Katsutoshi Kanamori, Sho Ushikubo, 2016, 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

Using Constraint Programming to Design an Option-based Decision Support System

Fumio Mizoguchi, Hayato Ohwada, F. Mizoguchi, 1995 .

Cooperative robot programming based on the parallel logic language KL1

Fumio Mizoguchi, Hayato Ohwada, Hiroyuki Nishiyama, 1996, Other Conferences.

Advances in Hybrid Genetic Algorithms with Learning and GPU for Scheduling Problems: Brief Survey and Case Study

Mitsuo Gen, Hayato Ohwada, John Runwei Cheng, 2020 .

Deep 3D Convolutional Neural Network Architectures for Alzheimer's Disease Diagnosis

Hayato Ohwada, Chien-Liang Liu, Hiroki Karasawa, 2018, ACIIDS.

Various Classifiers to Investigate the Relationship Between CSR Activities and Corporate Value

Hayato Ohwada, Ling Feng, Katsutoshi Kanamori, 2016, IEA/AIE.

Advances in Hybrid Evolutionary Algorithms for Fuzzy Flexible Job-shop Scheduling: State-of-the-Art Survey

Mitsuo Gen, Hayato Ohwada, Lin Lin, 2021, ICAART.

Early Detection Method for Subclinical Mastitis in Auto Milking Systems Using Machine Learning

H. Ohwada, C. Kubota, Haruka Motohashi, 2020, IEEE International Conference on Cognitive Informatics and Cognitive Computing.

Clustering Finger Motion Data from Virtual Reality-Based Training to Analyze Patients with Mild Cognitive Impairment

Hayato Ohwada, Takehiko Yamaguchi, Tania Giovannetti, 2016, Int. J. Softw. Sci. Comput. Intell..

Predicting Cow's Delivery Using Movement and Position Data Based on Machine Learning

Hayato Ohwada, Yusuke Ono, Hiroyuki Nishiyama, 2019, CATA.

Analysis of CSR activities Affecting Corporate Value Using Machine Learning

Hayato Ohwada, Feng Ling, Kanau Mitsuzuka, 2017, ICMLC.

Special issue on inductive logic programming

Akihiro Yamamoto, Katsumi Inoue, Hayato Ohwada, 2017, Machine Learning.

Classifying Cognitive Load and Driving Situation with Machine Learning

Fumio Mizoguchi, Hayato Ohwada, Hirotoshi Iwasaki, 2014 .

Comparison of Feature Selection Methods to Classify Inhibitors in DUD-E Database

Hayato Ohwada, Heri Kuswanto, Renny Yunia Nurhidayah, 2018, INNS Conference on Big Data.

Discovering Rules of Subtle Deficits Indicating Mild Cognitive Impairment Using Inductive Logic Programming

Hayato Ohwada, Takehiko Yamaguchi, Tania Giovannetti, 2017, HCI.

Extracting Rules for Decreasing Body Fat Mass Using Various Classifiers from Daily Lifestyle Habits Data

Hayato Ohwada, Katsutoshi Kanamori, Sho Ushikubo, 2016, Int. J. Softw. Sci. Comput. Intell..

Inductive Logic Programming: Challenges

Akihiro Yamamoto, Katsumi Inoue, Hayato Ohwada, 2016, AAAI.

Machine learning for the prediction of successful extubation among patients with mechanical ventilation in the intensive care unit: A retrospective observational study.

H. Ohwada, T. Tagami, T. Masuno, 2021, Journal of Nippon Medical School = Nippon Ika Daigaku zasshi.

Applicability of machine-learning techniques in predicting customer defection

Hayato Ohwada, Niken Prasasti, H. Ohwada, 2014, 2014 International Symposium on Technology Management and Emerging Technologies.

Smart Office Robot Collaboration Based on Multi-Agent Programming

Hironori Hiraishi, Fumio Mizoguchi, Hayato Ohwada, 1999, Artif. Intell..

Towards Agent-Oriented Smart Office Based on Concurrent Logic Languages

Fumio Mizoguchi, Hayato Ohwada, Hiroyuki Nishiyama, 1998, PRICAI.

Logic specifications for multiple robots based on a current programming language

Fumio Mizoguchi, Hayato Ohwada, Hiroyuki Nishiyama, 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

A multiagent robot language for communication and concurrency control

Fumio Mizoguchi, Hayato Ohwada, Hiroyuki Nishiyama, 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

Customer Lifetime Value and Defection Possibility Prediction Model Using Machine Learning: An Application to a Cloud-Based Software Company

Masato Okada, Hayato Ohwada, Katsutoshi Kanamori, 2014, ACIIDS.

New Survival Prediction System for Terminal Patients based on Machine Learning

Hayato Ohwada, Takeshi Yamada, Tatsuki Hirozawa, 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

V. Constraint Logic Programming Approach to Optimization

Fumio Mizoguchi, Hayato Ohwada, F. Mizoguchi, 1993 .

A Qualitative Quantitative Simulator Based on Constraint Logic Programming

Fumio Mizoguchi, Hayato Ohwada, F. Mizoguchi, 1991 .

Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study

H. Ohwada, T. Tagami, T. Masuno, 2021 .

Binary Classification of Compounds by Learning from Docking Software Results and Chemical Structures

H. Ohwada, S. Aoki, Kanamori Katsutoshi, 2012 .

Using Machine-Generated Soft Constraints for Roster Problems

Hayato Ohwada, Yoshihisa Shiina, H. Ohwada, 2011, ILP.

Inductive generalization from relational database

Fumio Mizoguchi, Hayato Ohwada, Masako Ishii, 1994 .

Predicting radiation protection and toxicity of p53 targeting radioprotectors using machine learning

Hayato Ohwada, Masataka Kimura, Shin Aoki, 2017, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

Utilizing finger movement data to cluster patients with everyday action impairment

Hayato Ohwada, Takehiko Yamaguchi, Niken Prasasti, 2016, 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

An Analysis of Motion Transition in Subtle Errors using Inductive Logic Programming: A Case Study in Approaches to Mild Cognitive Impairment

Hayato Ohwada, Takehiko Yamaguchi, Keisuke Abe, 2018, Int. J. Softw. Sci. Comput. Intell..

Generating Rules of Action Transition in Errors in Daily Activities from a Virtual Reality-Based Training Data

Hayato Ohwada, Takehiko Yamaguchi, Tania Giovannetti, 2017, HCI.