Speech emotion detection using supervised, unsupervised and feature selection algorithms

Speech emotion detection using supervised, unsupervised and feature selection algorithms

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

Manylion Llyfryddiaeth
Prif Awduron:	Rifat, Abu Nuraiya Mahfuza Yesmin, Biswas, Aditi, Chowdhury, Nadia Farhin
Awduron Eraill:	Uddin, Jia
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	BRAC University 2019
Pynciau:	SER MFCC Random forest Gradient boosting SVM CNN RFE P-Value PCA Supervised learning (Machine learning)
Mynediad Ar-lein:	http://hdl.handle.net/10361/12287

Eitemau Tebyg

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Comparative analysis and implementation of credit risk prediction through distinct machine learning models
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Analysis on dengue severity using machine learning approach
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Demystifying machine learning models for IOT attack detection with explainable AI
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Exploring machine learning techniques for symptom-based detection of livestock diseases
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Sentiment analysis for Bengali newspaper headlines
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Early threat warning via speech and emotion recognition from voice calls
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Predicting stock market movement using sentiment analysis of twitter feed
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Rating detection by reviews using ML and NLP towards mobile phone recommendation
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A Study of gainful employment of learners receiving skills training in the informal sector using Machine Learning
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Classification of respiratory diseases and COVID-19 from respiratory and cough sound using deep learning techniques
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Gene expression analysis using machine learning
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Supervision in thought and action /
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Predicting obesity: a comparative analysis of machine learning models incorporating different features
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Subjectivity analysis using machine learning algorithm
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Excellence in supervision : essential skills for the new supervisor /
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N.P.K. based crop suggesting model
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Best feature selection and data visualization for breast cancer prediction
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Predicting crime using deep learning
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Detecting navigation challenges for the visually impaired with mobile monitoring of biosignal
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