|W01||Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020)||July 11-12||Series of PKAW (Principle and Practice of Data and Knowledge Acquisition Workshop) workshop has long been an integral part of PRICAI over nearly two decades.The purpose of this workshop is to provide a place for intensive discussion on all aspects of knowledge acquisition. Multidisciplinary approach for knowledge acquisition is the topic of interests that includes data driven approach based on machine learning etc. as well as those to make human experts’ implicit knowledge machine executable. In this respect, PKAW2020 calls for a wide range of research papers concerning knowledge acquisition. We also invite submissions concerning data acquisition and representation. All the accepted papers are published as the post proceedings of Lecture Notes in Computer Science by Springer. PKAW2020 is sponsored by SIG-KBS, a special interest group of Knowledge Based System of The Japanese Society of Artificial Intelligence.|
|W02||8th Artificial Intelligence for Knowledge Management (AI4KM)||July 13||The objective of this multidisciplinary session is to gather both researchers and practitioners to discuss experiences in various AI approaches and techniques applied to knowledge management and innovation. It includes methodological, technical and organizational aspects of AI used for knowledge management and feedback from KM applications using AI. Among the topics to address are: knowledge management methods, models, decision support systems, management advisors, virtual training, serious games, applications of machine learning to support innovation and eco-innovation, knowledge visualization for improving the creativity and human-machine interfaces, image mining making links between data and images ex bio-detection are good examples of multidisciplinary applications as well as knowledge management applied to global security and sustainability.|
|W03||Disease Computational Modeling||July 12||
Many common chronic diseases remain mysterious as to how they manifest themselves and how they progress. The Disease Computational Modeling (DCM) workshop aims at providing a research forum where AI based computational methods are presented and discussed to further our understanding of how diseases develop over time. This calls for methods that can discover biological association for disease genotyping; clinical representation for disease phenotyping; probabilistic graph models for disease progression; disease-symptom models for screening or early diagnosis; disease-drug models for optimal care patterns; embedding of medical concepts w./w.o. patient visits; or other novel disease computational models with the potential if injecting meaningful insights in healthcare.
|W04||5th International Workshop on Biomedical Informatics with Optimization and Machine learning (BOOM 2020)||July 11||The BOOM workshop aims at catalyzing synergies among biomedical informatics, artificial intelligence, machine learning, and optimization. This workshop is targeting an audience of applied mathematicians, computer scientists, industrial engineers, bioinformaticians, computational biologists, clinicians, and healthcare researchers who are interested in exploring the emerging and fascinating interdisciplinary topics.|