Zobrazeno 1 - 10
of 367
pro vyhledávání: '"TOMOYA SAKAI"'
Autor:
Takeru SATO, Tsunehisa SUZUKI, Tatsuya FUJII, Mitsuyoshi NOMURA, Hiroshi SAITO, Takashi YAMAKAWA, Yuki SHISHIKURA, Tomoya SAKAI
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 89, Iss 928, Pp 23-00161-23-00161 (2023)
The purpose of this paper is to examine which coating material on tool edges is best for cutting spheroidal graphite cast iron containing 35% Nickel (Ni) and to understand the relation between friction properties of the coatings and cutting ability.
Externí odkaz:
https://doaj.org/article/5fdbfe353c544f5898a8b58f89569a63
Autor:
Eiji Mitate, Kirin Inoue, Retsushi Sato, Youichi Shimomoto, Seigo Ohba, Kinuko Ogata, Tomoya Sakai, Jun Ohno, Ikuo Yamamoto, Izumi Asahina
Publikováno v:
Diagnostic Pathology, Vol 17, Iss 1, Pp 1-8 (2022)
Abstract Background We aimed to develop an artificial intelligence (AI)-assisted oral cytology method, similar to cervical cytology. We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing ce
Externí odkaz:
https://doaj.org/article/39c8bbcac1ab44aa907c2a55ea7544b3
Autor:
Toshikazu Fukumitsu, Yasushi Obase, Yuji Ishimatsu, Shota Nakashima, Hiroshi Ishimoto, Noriho Sakamoto, Kosei Nishitsuji, Shunpei Shiwa, Tomoya Sakai, Sueharu Miyahara, Kazuto Ashizawa, Hiroshi Mukae, Ryo Kozu
Publikováno v:
BMC Pulmonary Medicine, Vol 19, Iss 1, Pp 1-8 (2019)
Abstract Background Honeycombing on high-resolution computed tomography (HRCT) is a distinguishing feature of usual interstitial pneumonia and predictive of poor outcome in interstitial lung diseases (ILDs). Although fine crackles are common in ILD p
Externí odkaz:
https://doaj.org/article/78d38b15cf594c8cb6d81ffb2db7ec58
Publikováno v:
Medicine; 7/7/2023, Vol. 102 Issue 27, p1-4, 4p
Publikováno v:
2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6cc236dfcfe9e981019d2c6f845c7387
https://doi.org/10.1109/dicta56598.2022.10034581
https://doi.org/10.1109/dicta56598.2022.10034581
Autor:
Tomoya Sakai
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6458c96164cfde01e134083002ab56e1
https://doi.org/10.1145/3534678.3539465
https://doi.org/10.1145/3534678.3539465
Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machi
Autor:
Tomoya Sakai, Nobuyuki Shimizu
Publikováno v:
AAAI
The goal of binary classification is to identify whether an input sample belongs to positive or negative classes. Usually, supervised learning is applied to obtain a classification rule, but in real-world applications, it is conceivable that only pos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::149bbbc61b6bd40aff106ff78e806ff2
https://doi.org/10.1609/aaai.v33i01.33014838
https://doi.org/10.1609/aaai.v33i01.33014838
Autor:
Tomoya Sakai, Naoto Ohsaka
Publikováno v:
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) ISBN: 9781611976700
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e91ea8609ce3920e6864672d50316cc5
https://doi.org/10.1137/1.9781611976700.42
https://doi.org/10.1137/1.9781611976700.42
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783030757649
PAKDD (2)
PAKDD (2)
With set-wise (exact-k, slate, combinatorial) recommendation, we aim to optimize the whole set of items to recommend while taking the dependency among items into consideration. This enables us to model, for example, the substitution relationship of i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51c57fa362f817df0cf836600c47f389
https://doi.org/10.1007/978-3-030-75765-6_40
https://doi.org/10.1007/978-3-030-75765-6_40