Human Activity Recognition using wearable body sensor by machine learning approach

Human Activity Recognition using wearable body sensor by machine learning approach

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

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Promi, Sadia Tangim, Rahman, Md. Zahidur, Mostafa, Moumita, Harun, Sarah Bintay
مؤلفون آخرون:	Alam, Md. Golam Rabiul
التنسيق:	أطروحة
اللغة:	en_US
منشور في:	Brac University 2020
الموضوعات:	Human Activity Recognition HAR Machine Learning Convolutional Neural Network
الوصول للمادة أونلاين:	http://hdl.handle.net/10361/14054

مواد مشابهة

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Analysis on face recognition based on five different viewpoint of face images using MTCNN and FaceNet
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Bangla sign language recognition and sentence building using deep learning
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Classification of Bangla regional languages and recognition of artificial Bangla speech using deep learning
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A novel lightweight CNN approach for Bangladeshi sign language gesture recognition
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AVCL: Audio Video clustering for learning Conversation labeling using Neural Network and NLP
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A new pattern recognition method for abnormal event detection in crowded scenarios
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RansomListener: Ransom call sound investigation using LSTM and CNN Architectures
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Comparative study of toxic comments classification using machine learning algorithms
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Detection of violent activity in surveillance system using different deep learning techniques
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Affordable voice assistive intelligent glass for visually impaired people in perspective of Bangladesh
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Single and multi-view 2D image processing for enhanced 3D object reconstruction
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An efficient deep learning approach for brain tumor detection using 3D convolutional neural network
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Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease
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Machine learning approach for face recognition from 3D models generated by multiple 2D angular images
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Voice recognition using machine learning and central database to enhance security system
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