|
|
|
Juan Murillo-Morera, Carlos Castro-Herrera, Javier Arroyo, Ruben Fuentes-Fernandez
Pág. 114 - 137
Today, it is common for software projects to collect measurement data through development processes. With these data, defect prediction software can try to estimate the defect proneness of a software module, with the objective of assisting and guiding so...
ver más
|
|
|
|
|
|
Nitesh Awasthi, Jayant Nath Tripathi, George P. Petropoulos, Pradeep Kumar, Abhay Kumar Singh, Kailas Kamaji Dakhore, Kripan Ghosh, Dileep Kumar Gupta, Prashant K. Srivastava, Kleomenis Kalogeropoulos, Sartajvir Singh and Dhiraj Kumar Singh
This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfal...
ver más
|
|
|
|
|
|
Ismail Fathy, Gamal M. Abdel-Aal, Maha Rashad Fahmy, Amira Fathy, Martina Zelenakova, Hany F. Abd-ElHamid, Jakub Racek and Ahmed Moustafa A. Moussa
Urban flooding is a problem faced by most countries because of climate change. Without storm drainage systems, negative impacts may occur, such as traffic problems and increasing groundwater levels, especially in lowlands. The implementation of storm dra...
ver más
|
|
|
|
|
|
Feifei He, Qinjuan Wan, Yongqiang Wang, Jiang Wu, Xiaoqi Zhang and Yu Feng
Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal...
ver más
|
|
|
|
|
|
Leon Kopitar, Iztok Fister, Jr. and Gregor Stiglic
Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to im...
ver más
|
|
|