scholarly journals A deep learning approach for human behavior prediction with explanations in health social networks: social restricted Boltzmann machine (SRBM+)

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Nhathai Phan ◽  
Dejing Dou ◽  
Brigitte Piniewski ◽  
David Kil
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Human Behavior ◽  
Restricted Boltzmann Machine ◽  
Learning Approach ◽  
Behavior Prediction ◽  
Boltzmann Machine

scholarly journals Ontology-based deep learning for human behavior prediction with explanations in health social networks

2017 ◽  
Vol 384 ◽  
pp. 298-313 ◽  
Author(s):  
Nhathai Phan ◽  
Dejing Dou ◽  
Hao Wang ◽  
David Kil ◽  
Brigitte Piniewski
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Human Behavior ◽  
Behavior Prediction

The optimization of parallel convolutional RBM based on Spark

2019 ◽  
Vol 17 (02) ◽  
pp. 1940011 ◽  
Author(s):  
Yun Jiang ◽  
Junyu Zhuo ◽  
Juan Zhang ◽  
Xiao Xiao
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Image Classification ◽  
Image Recognition ◽  
Restricted Boltzmann Machine ◽  
Sequential Algorithm ◽  
Boltzmann Machine ◽  
X Ray ◽  
Model Based ◽  
Traditional Algorithm

With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditional sequential algorithm. We train the CRBM with the method and apply it to breast X-ray image classification. The experiments show that it can improve the precision and the speed of training compared with traditional algorithm.

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

scholarly journals A Human Behavior-Driven Deep-Learning Approach for Automatic Sigmoid Segmentation

2019 ◽  
Vol 105 (1) ◽  
pp. S93-S94
Author(s):  
Y. Gonzalez ◽  
C. Shen ◽  
H. Jung ◽  
X. Jia
Keyword(s):  
Deep Learning ◽  
Human Behavior ◽  
Learning Approach

Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

2015 ◽  
Vol 23 (6) ◽  
pp. 2163-2173 ◽  
Author(s):  
C. L. Philip Chen ◽  
Chun-Yang Zhang ◽  
Long Chen ◽  
Min Gan
Keyword(s):  
Deep Learning ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine
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