A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς:	Farabe, Abdullah Al, Sharika, Tarin Sultana, Raonak, Nahian, Ashraf, Ghalib
Άλλοι συγγραφείς:	Chakrabarty, Amitabha
Μορφή:	Thesis
Γλώσσα:	English
Έκδοση:	Brac University 2021
Θέματα:	Linear Discriminant Analysis (LDA) Logistic regression Random forest Decision tree KNN and CNN Machine learning. Computer algorithms.
Διαθέσιμο Online:	http://hdl.handle.net/10361/14745

Παρόμοια τεκμήρια

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Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
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Automated fabric color prediction
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Comparative analysis and implementation of credit risk prediction through distinct machine learning models
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A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
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Performance analysis of machine learning algorithms for Malware classification
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A supervised machine learning approach to predict vulnerability to drug addiction
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