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A data-centric deep learning method for Pulmonary nodule detection / Nguyen Chi Cuong, Nguyen Long Giang, Tran Hang Son // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2022. - tr. 19-33. - ISSN: 1813-9663
Ký hiệu phân loại (DDC): 005.74
Lung cancer is one of the most serious cancer-related diseases in Vietnam 9;.“ all over the world. Early detection of lung nodules can help to increase the survival rate 1:3; cancer patients. Computer-aided diagnosis (CAD) systems are proposed in the literature for: 93:36 detection of lung nodules. However, most of the current CAD systems are based on the building ' List—quality machine learning models for a fixed dataset rather than taking into account the dataset properties which are very important for the lung cancer diagnosis. In this paper, we follow the arse-5:2 of data- ccntric approach for lung nodule detection by proposing a data-centric method to in;rares— detection performance of lung nodules on CT scans. Our method takes into account the dataset-span: features (nodule sizes and aspect ratios) to train detection models as well as add more trainizg 141:; from local Vietnamese hospital. We experiment our method on the three widely used object detection networks (Faster R-CNN, YOLOVQ, and RetinaNet). The experimental results show that our prop-used method improves detection sensitivity of these object detection models up to 4.24%.
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A two-channel model for representation learning in vietnamese sentiment classification problem / Nguyen Hoang Quan, Ly Vu, Nguyen Quang Uy // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2020. - tr. 13-31. - ISSN: 1813-9663
Ký hiệu phân loại (DDC): 005.13
Sentiment classification ISC) aims to determine whether a document. conveys a positiveor negative opinion. Due to the rapid development of the digital world. SC hm, rwmme an impor-tant research topic that affects to many aspects of our life. In SC hast-d on maritim- Earning. The representation of the document strongly influences on its accuracy. Embedding 35E; swam techniques, techniques. are proved to be beneficial techniques to the SC problem. Howeve is often not. enough to represent the semantic of Vietnamese (1062235 due tothe complexity of semantics and syntactic structure. In this paper. we propose a new:sematiun learning model called a two-channel vector to learn a higher-level feature of a (locum-er: for SC. Our model uses two neural networks to learn both the semantic feature and the feature. The semantic feature is learnt using W'ord and the syntactic feature is learnt tho-.3; Parts of Speech tag (POS). Two features are then combined and input to a Softmax functic-r. 1.51 med-2:3 the final classification.
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AI and Neurology / Julian Bösel, Rohan Mathur, Lin Cheng, Marianna S. Varelas, Markus A. Hobert & José I. Suarez // Neurological Research and Practice. - . - . - ISSN: 2524-3489
9 pages
Ký hiệu phân loại (DDC): 610.28
Background Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials.
Main body In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise.
Conclusion Artificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.
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Data mining and machine learning : fundamental concepts and algorithms / Mohammed J. Zaki, Wagner Meira
United Kingdom ; New York, NY : Cambridge University Press, Cambridge, 2020
777 pages. : illustrations ; 26 cm.
Ký hiệu phân loại (DDC): 006.3
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts.New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning
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