Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Takafumi Nakanish"'
Autor:
Takafumi Nakanishi
Publikováno v:
IEEE Access, Vol 12, Pp 121093-121113 (2024)
Complex “black-box” artificial intelligence (AI) models are interpreted using interpretive machine learning and explainable AI (XAI); therefore, assessing the importance of global and local features is crucial. The previously proposed approximate
Externí odkaz:
https://doaj.org/article/147e025a219049ca9317f3ccc80040b3
Publikováno v:
IEEE Access, Vol 12, Pp 88696-88714 (2024)
Explainable artificial intelligence (XAI) techniques are used to understand the rationale behind the decision-making of machine learning models. In addition to the need for model explainability, the demand for an ever-growing number of multimodal fea
Externí odkaz:
https://doaj.org/article/c4cef0cb205d4b87829aedff95a09668
Autor:
Takafumi Nakanishi
Publikováno v:
IEEE Access, Vol 12, Pp 52623-52640 (2024)
In the evolving landscape of interpretable machine learning (ML) and explainable artificial intelligence, transparent and comprehensible ML models are crucial for data-driven decision-making. Traditional approaches have limitations in distinguishing
Externí odkaz:
https://doaj.org/article/69df6957d61f40ff942f1a6160426d5c
Autor:
Rikito Ohnishi, Yuki Murakami, Takafumi Nakanish, Ryotaro Okada, Teru Ozawa, Kosuke Fukushima, Taichi Miyamae, Yutaka Ogasawara, Kei Akiyama, Kazuhiro Ohashi
Publikováno v:
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2022-Winter ISBN: 9783031261343
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a1f0b22d0b36b1f7ab6177a1e837d83
https://doi.org/10.1007/978-3-031-26135-0_6
https://doi.org/10.1007/978-3-031-26135-0_6
Autor:
Takafumi Nakanishi
Publikováno v:
IEEE Access, Vol 11, Pp 101020-101044 (2023)
Data-driven decision-making has become pervasive in the fields of interpretive machine learning and Explainable AI (XAI). While both fields aim to improve human comprehension of machine learning models, they differ in focus. Interpretive machine lear
Externí odkaz:
https://doaj.org/article/d384bac191034dc8b56df9569fa2a0fa
Inconsistencies of connection for heterogeneity and a new relation discovery method that solved them
Publikováno v:
ICIS
We represent the inconsistencies of the past research on the connections among such heterogeneous fields as Linked Data, Semantic Web, Bridge Ontology, and Schema Mapping as well as our own past researches. Graph structures are commonly represented a
Autor:
Jirayu Boonyakida, Jian Xu, Jun Satoh, Takafumi Nakanishi, Tohru Mekata, Tatsuya Kato, Enoch Y. Park
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract White spot syndrome virus (WSSV) is one of the most devastating pathogens in penaeid shrimp and can cause massive damage in shrimp aquaculture industries. Previously, the WSSV structural protein VP15 was identified as an antigenic reagent ag
Externí odkaz:
https://doaj.org/article/55318a81648e4186979ba8cfc7dd9698
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 10, Iss 1, Pp 1-16 (2021)
Abstract Herein, we present a novel topic variation detection method that combines a topic extraction method and a change-point detection method. It extracts topics from time-series text data as the feature of each time and detects change points from
Externí odkaz:
https://doaj.org/article/9125fcd766e1465d92b2816e5ded07b7
Autor:
Kyohei Matsumoto, Takafumi Nakanishi, Toshitada Sakawa, Kengo Onodera, Shinichiro Orimo, Hiroyuki Kobayashi
Publikováno v:
Emitter: International Journal of Engineering Technology, Vol 7, Iss 1 (2019)
In this paper, we present a thinking support system, AI-Josyu. This system also operates as a class support system which helps to teachers for lightening their work. AI-Josyu is implemented based on media-driven real-time content management framework
Externí odkaz:
https://doaj.org/article/d5b216e6cdf345d089368f259dced13f