[PDF] Top 20 Disfluency Detection using Auto Correlational Neural Networks
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Disfluency Detection using Auto Correlational Neural Networks
... Our proposed approach, based on an auto- correlational neural network (ACNN), belongs to the class of sequence tagging approaches. These approaches use classification techniques such as conditional ... See full document
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Captioning for Motion Detection for video surveillance Applications using Deep Learning
... model neural network which automatically learns to features from the images and generate the ...trained using COCO dataset where the dataset is split into test, train and validation ...convolutional ... See full document
6
Dimension Reduction and Clustering of High Dimensional Data using Auto Associative Neural Networks
... also Auto-Associative Neural Networks (AANN) [1] ...Bottleneck Neural Networks (BNN) has been previously used for data compression or dimension reduction particularly in the field of ... See full document
7
Brain Tumor Classification Using Convolutional Neural Networks
... The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, ... See full document
5
Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection
... on disfluency detection falls into three main categories: sequence tagging, noisy-channel and parsing-based ...recurrent neural networks (Hough and Schlangen, 2015; Za- yats et ...sentences ... See full document
10
Steganography Detection using Functional Link Artificial Neural Networks
... Artificial Neural Network to detect the coded ...forward neural network and to overcome the linear mapping, functionally expands the input ...of neural networks using JPEG ...the ... See full document
5
Fungus Detection using Convolutional Neural Networks
... In [1], The fungus is the big hazard and farmers lost nearly a million dollars per year due to different varieties of species in fungus. An automated system for the detection of fungus in the air spores. Air ... See full document
5
Detection and Recognition of Vehicle Using Neural Networks
... of using Raspberry pi and webcam in the toll mechanism would aid in motion capture and recognition of number plates, while the vehicle approaches the ...instant detection of the vehicle and its weightage ... See full document
5
Pneumonia Detection Using Convolutional Neural Networks
... Convolutional Neural Network is optimized to perform the complicated task of detecting diseases like pneumonia to assist medical experts in diagnosis and possible treatment of the ...VGGNet using 1024 by ... See full document
6
Medicare fraud detection using neural networks
... fraud detection, but challenges associated with class-imbalanced big data hinder ...fraud detection task to compare six deep learning methods designed to address the class imbalance ...model. Neural ... See full document
35
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
... One of the tasks that we address in this work is multilingual representation learning where the aim is to learn aligned representations for words across languages. Some notable neural network based ap- proaches ... See full document
11
Political Ideology Detection Using Recursive Neural Networks
... An individual’s words often reveal their po- litical ideology. Existing automated tech- niques to identify ideology from text focus on bags of words or wordlists, ignoring syn- tax. Taking inspiration from recent work in ... See full document
10
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
... disfluent using a variety of different techniques, including conditional random fields (Ostendorf and Hahn, 2013; Zayats et ...recurrent neural networks (Hough and Schlangen, 2015; Zayats et ...for ... See full document
7
Face Recognition and Feature Detection Using Artificial Neural Networks and ANFIS
... The experiment has been conducted on a 50 individuals. Each individual has three views and each view has 3 samples. Their images have been taken under constant light so as to ensure that their all three views look alike. ... See full document
5
A Neural Attention Model for Disfluency Detection
... of disfluency detection using the encoder-decoder ...treat disfluency detection as a sequence-to-sequence problem and propose a neural attention- based model which can ... See full document
10
Training and classification of Epilepsy Detection using EEG
... inputs. Using our "sigma" function, this reduces to an output of 1, the highest possible output value, and a sure indication that the image is of an ... See full document
13
Virus Detection using Artificial Neural Networks
... In Phase 1, the Training Data, which comprises of two types of executable files-legitimate and virus infected, is given as input to the Feature Extractor. The Feature Extractor takes a one feature (PE Structure field) at ... See full document
7
Token Level Metaphor Detection using Neural Networks
... is using a lookup function to ex- tract existing, pre-trained word embeddings for all content words in the data ...metaphor detection on a se- mantic ... See full document
6
Singleton Detection using Word Embeddings and Neural Networks
... singleton detection system based on word embed- dings and neural networks is presented, which achieves state-of-the-art perfor- mance ...of using neural networks and word ... See full document
7
NETWORK INTRUSION DETECTION USING DEEP NEURAL NETWORKS
... According to [17], RNNs are considered reduced-size neural networks. In that paper, the author proposes a three layer RNN architecture with 41 features as inputs and four intrusion categories as outputs, ... See full document
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