A Distributed Ensemble of Deep Convolutional Neural Networks with Random Forest for Big Data Sentiment Analysis

2019 ◽  
pp. 153-162
Author(s):  
Badr Ait Hammou ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks

Big data analysis for brain tumor detection: Deep convolutional neural networks

2018 ◽  
Vol 87 ◽  
pp. 290-297 ◽  
Author(s):  
Javeria Amin ◽  
Muhammad Sharif ◽  
Mussarat Yasmin ◽  
Steven Lawrence Fernandes
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Brain Tumor ◽  
Data Analysis ◽  
Convolutional Neural Networks ◽  
Tumor Detection ◽  
Big Data Analysis ◽  
Deep Convolutional Neural Networks

A novel MapReduce-based deep convolutional neural network algorithm

2021 ◽  
pp. 1-13
Author(s):  
Xiang-Min Liu ◽  
Jian Hu ◽  
Deborah Simon Mwakapesa ◽  
Y.A. Nanehkaran ◽  
Yi-Min Mao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Feature Learning ◽  
Deep Convolutional Neural Networks ◽  
Network Training ◽  
Load Rate ◽  
Data Environment ◽  
Neural Networks Optimization

Deep convolutional neural networks (DCNNs), with their complex network structure and powerful feature learning and feature expression capabilities, have been remarkable successes in many large-scale recognition tasks. However, with the expectation of memory overhead and response time, along with the increasing scale of data, DCNN faces three non-rival challenges in a big data environment: excessive network parameters, slow convergence, and inefficient parallelism. To tackle these three problems, this paper develops a deep convolutional neural networks optimization algorithm (PDCNNO) in the MapReduce framework. The proposed method first pruned the network to obtain a compressed network in order to effectively reduce redundant parameters. Next, a conjugate gradient method based on modified secant equation (CGMSE) is developed in the Map phase to further accelerate the convergence of the network. Finally, a load balancing strategy based on regulate load rate (LBRLA) is proposed in the Reduce phase to quickly achieve equal grouping of data and thus improving the parallel performance of the system. We compared the PDCNNO algorithm with other algorithms on three datasets, including SVHN, EMNIST Digits, and ISLVRC2012. The experimental results show that our algorithm not only reduces the space and time overhead of network training but also obtains a well-performing speed-up ratio in a big data environment.

scholarly journals Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks

Algorithms ◽  
2016 ◽  
Vol 9 (2) ◽  
pp. 41 ◽  
Author(s):  
Yuhai Yu ◽  
Hongfei Lin ◽  
Jiana Meng ◽  
Zhehuan Zhao
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks

scholarly journals Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks

2019 ◽  
Vol 28 (3) ◽  
pp. 377-386 ◽  
Author(s):  
Kamal Sarkar
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Early Stage ◽  
Contextual Information ◽  
Indian Languages ◽  
Deep Convolutional Neural Networks ◽  
Detection Model ◽  
Detection Systems ◽  
Hidden Layer

Abstract Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks which consist of one convolutional layer, one hidden layer and one output layer, which is a soft-max layer. Our proposed approach has been tested on the Bengali tweet dataset released for Sentiment Analysis in Indian Languages contest 2015. We have compared the performance of our proposed convolutional neural networks (CNN)-based model with a sentiment polarity detection model that uses deep belief networks (DBN). Our experiments reveal that the performance of our proposed CNN-based system is better than our implemented DBN-based system and some existing Bengali sentiment polarity detection systems.

scholarly journals Neonatal Seizure Detection Using Deep Convolutional Neural Networks

2019 ◽  
Vol 29 (04) ◽  
pp. 1850011 ◽  
Author(s):  
Amir H. Ansari ◽  
Perumpillichira J. Cherian ◽  
Alexander Caicedo ◽  
Gunnar Naulaers ◽  
Maarten De Vos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Convolutional Neural Networks ◽  
Heuristic Method ◽  
Seizure Detection ◽  
Neonatal Seizure ◽  
Deep Convolutional Neural Networks ◽  
Feature Based ◽  
Eeg Recordings ◽  
Nonlinear Classifier

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

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