Zobrazeno 1 - 10
of 1 156
pro vyhledávání: '"Ensemble Machine Learning"'
Autor:
Ibrahim Wichka, Pin-Kuang Lai
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 3669-3679 (2024)
Celiac disease poses a significant health challenge for individuals consuming gluten-containing foods. While the availability of gluten-free products has increased, there is still a need for therapeutic treatments. The advancement of computational dr
Externí odkaz:
https://doaj.org/article/cf856de826aa42019b81ec5742025b4b
Autor:
Aya T. Shalata, Ahmed Alksas, Mohamed Shehata, Sherry Khater, Osama Ezzat, Khadiga M. Ali, Dibson Gondim, Ali Mahmoud, Eman M. El-Gendy, Mohamed A. Mohamed, Norah S. Alghamdi, Mohammed Ghazal, Ayman El-Baz
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial inte
Externí odkaz:
https://doaj.org/article/7e698f5f07a84842b71e4c8290659c0c
Autor:
Tahmina A. Keya, S Sreeramanan, SB Siventhiran, S Maheswaran, Saravana Selvan, Kevin Fernandez, Low J An, A Leela, R Prahankumar, A Lokeshmaran, AV Boratne
Publikováno v:
Medical Journal of Dr. D.Y. Patil Vidyapeeth, Vol 17, Iss 5, Pp 990-1003 (2024)
Background: The world economy is significantly impacted by floods. Identifying flood risk is essential to flood mitigation techniques. AIM: The primary goal of this study is to create a geographic information system (GIS)-based flood susceptibility m
Externí odkaz:
https://doaj.org/article/69b022a1f6aa4c208999f531cf8e17f6
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper iden
Externí odkaz:
https://doaj.org/article/28ca619508b84ec2bf75173ad713223c
Publikováno v:
Proceedings on Engineering Sciences, Vol 6, Iss 3, Pp 1337-1348 (2024)
In the realm of data science, dealing with real-world datasets often presents a formidable challenge, primarily due to the sheer volume of features that significantly lack relevance or may be redundant. Effective feature engineering is vital in const
Externí odkaz:
https://doaj.org/article/2ce1712a13ab4b9583e8856ce3cb0f11
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103048- (2024)
Precise streamflow forecasting in river systems is crucial for water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB) in Spain is a key hydrological hub, providing regulated flow for agricultural, urban, and ene
Externí odkaz:
https://doaj.org/article/2887e077568a4a2295c71857b68f5333
Autor:
Sandeep Gawdiya, Dinesh Kumar, Bulbul Ahmed, Ramandeep Kumar Sharma, Pankaj Das, Manoj Choudhary, Mohamed A. Mattar
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100543- (2024)
Wheat is crucial for global food security and plays significant role in achieving United Nations Sustainable Development Goal 2 (Zero Hunger). In India, wheat accounts for 33.5 % of total cereal production. Accurate and cost effective yield predictio
Externí odkaz:
https://doaj.org/article/4c182aee72f1405da75f21984207cebd
Autor:
Haytham Elmousalami, Ibrahim Sakr
Publikováno v:
Journal of Petroleum Exploration and Production Technology, Vol 14, Iss 10, Pp 2735-2752 (2024)
Abstract Lost circulation and mud losses cause 10 to 20% of the cost of drilling operations under extreme pressure and temperature conditions. Therefore, this research introduces an integrated system for an automated lost circulation severity classif
Externí odkaz:
https://doaj.org/article/2d7ac354b794404dbc573e87205841d2
Publikováno v:
Smart Cities, Vol 7, Iss 4, Pp 1836-1856 (2024)
In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning te
Externí odkaz:
https://doaj.org/article/06c22d45ab0748b7a84fe71f51567efa
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-20 (2024)
Abstract Objective This study was designed to develop and validate a robust predictive model for one-year mortality in elderly coronary heart disease (CHD) patients with anemia using machine learning methods. Methods Demographics, tests, comorbiditie
Externí odkaz:
https://doaj.org/article/c6a759e936c54b41989c82759bf70238