Comparative analysis of machine learning techniques in optimal site selection

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.

Xehetasun bibliografikoak
Egile Nagusiak:	Aurnab, Aukik, Choudhury, Shaktiman, Ruhan, Shoubhick Roy, Rifaiya Abrar, Shikh Muhammad, Hossain Rabbi, S.M. Riyadh
Beste egile batzuk:	Khan, Rubayat Ahmed
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2023
Gaiak:	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.
Sarrera elektronikoa:	http://hdl.handle.net/10361/19150

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LRFMV: an efficient customer segmentation model for superstores
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Benchmarking erasure coding schemes in openStack swift
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Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19
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RansomListener: Ransom call sound investigation using LSTM and CNN Architectures
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An exploratory study of teacher’s oral corrective feedback in the government secondary schools of Bangladesh
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