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What is Convolution Neural Network

Neural Networks for Natural Language Processing
A type of deep neural networks that uses convolution and pooling layers to typically classify imagery.
Published in Chapter:
Neural Network Applications in Hate Speech Detection
Brian Tuan Khieu (San Jose State University, USA) and Melody Moh (San Jose State University, USA)
Copyright: © 2020 |Pages: 17
DOI: 10.4018/978-1-7998-1159-6.ch012
Abstract
This chapter presents a literature survey of the current state of hate speech detection models with a focus on neural network applications in the area. The growth and freedom of social media has facilitated the dissemination of positive and negative ideas. Proponents of hate speech are one of the key abusers of the privileges allotted by social media, and the companies behind these networks have a vested interest in identifying such speech. Manual moderation is too cumbersome and slow to deal with the torrent of content generation on these social media sites, which is why many have turned to machine learning. Neural network applications in this area have been very promising and yielded positive results. However, there are newly discovered and unaddressed problems with the current state of hate speech detection. Authors' survey identifies the key techniques and methods used in identifying hate speech, and they discuss promising new directions for the field as well as newly identified issues.
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Medical Image Classification
This is a variant of ANN and consists of different types of hidden layers named as convolution layer, pooling layer. This network is more suitable to handle images and video. Original image is convoluted as it goes deeper in the network and at the last layer activation function can be used to produce a single class as output.
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