scholarly journals Neuroscience-Inspired Artificial Vision Feature Parallelism and Deep Learning Models, A Comparative Study II Depth

2019 ◽  
Vol 9 (9) ◽  
pp. 127-139
Author(s):  
Marwa Yousif Hassan ◽  
Othman O. Khalifa ◽  
Aisha Hassan Abdalla
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Artificial Vision ◽  
Learning Models

scholarly journals Comparative study of deep learning models for sentiment analysis

2018 ◽  
Vol 7 (2.14) ◽  
pp. 5726
Author(s):  
Oumaima Hourrane ◽  
El Habib Benlahmar ◽  
Ahmed Zellou
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Word Embeddings ◽  
Human Language ◽  
Learning Models ◽  
Automatic Learning ◽  
Learning Capability ◽  
The Web

Sentiment analysis is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, sentiment analysis and deep learning have been merged as deep learning models are effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.

Employment of Pre-trained Deep Learning Models for Date Classification: A Comparative Study

2021 ◽  
pp. 181-189
Author(s):  
Aiman Al-Sabaawi ◽  
Reem Ibrahim Hasan ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Laith Alzubaidi
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Learning Models
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