Human activity recognition method using joint deep learning and acceleration signal

Named Entity Recognition in Persian Text using Deep Learning

Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...

A Novel Radar Signal Recognition Method based on Deep Learning

Radar signal recognition is of great importance in the field of electronic intelligence reconnaissance. To deal with the problem of parameter complexity and agility of multi-function radars in radar signal recognition, a new model called radar signal recognition based on deep restricted Boltzmann machine (RSRDRBM) is proposed to extract the feature parameters and recognize the radar emitter. Th...

Learning Deep Features for kNN-Based Human Activity Recognition

A CBR approach to Human Activity Recognition (HAR) uses the kNN algorithm to classify sensor data into different activity classes. Different feature representation approaches have been proposed for sensor data for the purpose of HAR. These include shallow features, which can either be hand-crafted from the time and frequency domains, or the coefficients of frequency transformations. Alternative...

Comparing Deep and Classical Machine Learning Methods for Human Activity Recognition using Wrist Accelerometer

Human Activity Recognition using Deep Neural Network with Contextual Information

[1] Li Wei, Shishir. K. Shah, Human Activity Recognition using Deep Neural Network with Contextual Information, ECCV 2016 (In submission) [2] B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning deep features for scene recognition using places database. In Advances in Neural Information Processing Systems [3] W. Choi, K. Shahid, and S. Savarese. What are they doing? : Collective ...