Zobrazeno 1 - 10
of 1 335
pro vyhledávání: '"multilayer perceptron (mlp)"'
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 12, Pp 5179-5192 (2024)
The interpretation of the cone penetration test (CPT) still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers. This paper presents a CPT virtual calibration c
Externí odkaz:
https://doaj.org/article/a34218855bf24b48a995093eacc026bd
Publikováno v:
Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol 26, Iss Özel Sayı, Pp 17-34 (2024)
Yapay Sinir Ağları (YSA), makine öğrenmesi alanında yaygın olarak kullanılan etkili bir yöntemdir ve tahmin yapmada başarılı sonuçlar sağlayabilir. YSA, biyolojik sinir sisteminden ilham alınarak matematiksel bir model oluşturur. Bu ç
Externí odkaz:
https://doaj.org/article/37c5b5abbec24d0b93a12f1a5198bdf9
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1422-1434 (2025)
Accurate prediction of oceanic eddy trajectory is crucial for monitoring ocean climate change, but the complex dynamics mechanism and changeable environmental effects make it difficult. In recent years, many deep-learning methods have been proposed t
Externí odkaz:
https://doaj.org/article/b74646f70a0f4058a4925ec8f79acd24
Autor:
Júlio César Silva de Souza, Oswaldo Honorato Júnior, Geraldo Lúcio Tiago Filho, Otávio Augusto Salgado Carpinteiro, Hailton Silveira Domingues Biancardine Júnior, Ivan Felipe Silva dos Santos
Publikováno v:
Revista Brasileira de Recursos Hídricos, Vol 29 (2024)
ABSTRACT Cavitation is a phenomenon that reduces the useful life of hydraulic machines, taking place in function of the variation of the pressure gradient at a constant temperature. In hydraulic turbines, cavitation occurs when the turbine operates b
Externí odkaz:
https://doaj.org/article/a514a6fa4a8f42cf88983d80a5504b93
Publikováno v:
PeerJ Computer Science, Vol 10, p e2425 (2024)
Brain tumors are widely recognized as the primary cause of cancer-related mortality globally, necessitating precise detection to enhance patient survival rates. The early identification of brain tumor is presented with significant challenges in the h
Externí odkaz:
https://doaj.org/article/78d69dd145964868b7589eca2c3fc086
Publikováno v:
Case Studies in Thermal Engineering, Vol 63, Iss , Pp 105297- (2024)
This paper introduces a comprehensive approach for predicting chemical concentrations (C) of sulfur compound in a two-dimensional space (x, y) by numerical solution of mass transfer equation and integration of machine learning. Data of training/valid
Externí odkaz:
https://doaj.org/article/ffeadb5f7c994641a67ecc636696aa77
Autor:
Mehdi Fuladipanah, Alireza Shahhosseini, Namal Rathnayake, Hazi Md. Azamathulla, Upaka Rathnayake, D. P. P. Meddage, Kiran Tota-Maharaj
Publikováno v:
Water Practice and Technology, Vol 19, Iss 6, Pp 2442-2459 (2024)
Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditio
Externí odkaz:
https://doaj.org/article/22380a96883a4454aa7afe15f7d3d075
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102585- (2024)
This paper presents a comprehensive investigation into the structural response of curved composite beams enhanced with carbon nanotube (CNT). Employing a multiscale framework, our analysis leverages the finite element method (FEM) to account for both
Externí odkaz:
https://doaj.org/article/07803855c137472c9f55e649a999e201
Autor:
Yamei Liu
Publikováno v:
Frontiers in Psychology, Vol 15 (2024)
ObjectiveThis study aims to precisely model the nonlinear relationship between university students’ literature reading preferences (LRP) and their levels of anxiety and depression using a multilayer perceptron (MLP) to identify reading-related risk
Externí odkaz:
https://doaj.org/article/92d5a54398f646c6b7c7d263fdadaefe