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Autor:
Wenzhong Li, Chengshuai Liu, Caihong Hu, Chaojie Niu, Runxi Li, Ming Li, Yingying Xu, Lu Tian
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteri
Externí odkaz:
https://doaj.org/article/5882d904fc5944a39f7d09019027fd30
Autor:
Wenzhong Li, Chengshuai Liu, Yingying Xu, Chaojie Niu, Runxi Li, Ming Li, Caihong Hu, Lu Tian
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 54, Iss , Pp 101873- (2024)
Study region: Flood formation involves complex nonlinear processes and numerous variables, with data-driven models becoming a key non-engineering approach to flood prevention and mitigation. Yet, single machine learning models are insufficient to ful
Externí odkaz:
https://doaj.org/article/a538810516f34399b9ed9cd157b62b56
Publikováno v:
IEEE Access, Vol 12, Pp 53664-53676 (2024)
(1) Background: Code smell is the most popular and reliable method for detecting potential errors in code. In real-world circumstances, a single source code may have multiple code smells. Multi-label code smell detection is a popular research study.
Externí odkaz:
https://doaj.org/article/f39d7c7bdd004a35abb888a8c6d53730
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 4, Pp 1660-1679 (2023)
In an era characterised by rapid technological advancement, the application of algorithmic approaches to address complex problems has become crucial across various disciplines. Within the realm of education, there is growing recognition of the pivota
Externí odkaz:
https://doaj.org/article/acbdc889d905489a867015affdeb4ce8
Autor:
Muhamad Fajri, Aji Primajaya
Publikováno v:
Journal of Applied Informatics and Computing, Vol 7, Iss 1, Pp 10-15 (2023)
Classification is one of the important tasks in the field of Machine Learning. Classification can be viewed as an Optimization Problem (Optimization Problem) with the aim of finding the best model that can represent the relationship/pattern between d
Externí odkaz:
https://doaj.org/article/4fec4663dade4912bcc2cd45a790a09c
Akademický článek
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Autor:
Bangwen Xi, Gang Xiong, Kirill A. Kozin, Chang He, Tariku Sinshaw Tamir, Yonggang Song, Xiong Liu, Zhen Shen
Publikováno v:
Results in Control and Optimization, Vol 13, Iss , Pp 100320- (2023)
Polysilicon is mainly obtained by the Siemens process chemical vapor deposition (CVD) reaction, in which hydrogen gas and trichlorosilane (TCS) are fed into the CVD reactor to produce polysilicon. However, adjusting the feeding parameters in a step-b
Externí odkaz:
https://doaj.org/article/fb0010a1772448ba80c444f2d7446d4d
Autor:
Md. Simul Hasan Talukder, Rejwan Bin Sulaiman, Mohammad Raziuddin Chowdhury, Musarrat Saberin Nipun, Taminul Islam
Publikováno v:
Smart Agricultural Technology, Vol 5, Iss , Pp 100297- (2023)
Potatoes are the third-largest food crop globally, but their production frequently encounters difficulties because of aggressive pest infestations. Early classification those potato pests plays an important role in the detection and prevention of the
Externí odkaz:
https://doaj.org/article/96734bc72a62456b9184f1849388cc42
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 8, Iss 1, Pp 274-284 (2023)
Abstract As a complex hot problem in the financial field, stock trend forecasting uses a large amount of data and many related indicators; hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis. Resear
Externí odkaz:
https://doaj.org/article/a12c4eb9ef264adb9c5c9fb2adc079cc
Publikováno v:
IEEE Access, Vol 11, Pp 88025-88039 (2023)
This paper focuses on enhancing machine learning (ML)-based diagnosis and clinical decision-making by leveraging radiomics data, which provides a quantitative description of grayscale medical images such as MRI, CT, PET, or X-Ray. Extracted using adv
Externí odkaz:
https://doaj.org/article/ab8e49f1626140c798bd2674aec4138c