Comparative analysis of machine learning techniques in optimal site selection

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Detalhes bibliográficos
Principais autores:	Aurnab, Aukik, Choudhury, Shaktiman, Ruhan, Shoubhick Roy, Rifaiya Abrar, Shikh Muhammad, Hossain Rabbi, S.M. Riyadh
Outros Autores:	Khan, Rubayat Ahmed
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2023
Assuntos:	Optimal Site Selection (OSS) Comparative analysis Boosting and stacking SVR Random forest XGBoost Ridge regression Lasso regression Elasticnet Convolutional Neural Network (CNN) Ensemble learning VGG16 VGG19 ResNet DenseNet InceptionV3 Root Mean Squared Error (RMSE) Mean Squared Error (MSE) Mean Absolute Error (MAE) Median Absolute Error (MedAE) Max error (ME) Median Absolute Deviation(MAD) Machine learning. Artificial intelligence.
Acesso em linha:	http://hdl.handle.net/10361/19150

http://hdl.handle.net/10361/19150

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