Enhanced auto associative neural network using feed forward neural network: an approach to improve performance of fault detection and analysis

Meti, Subhas A.; Sangam, V. G.

doi:10.1504/ijdats.2019.103754

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S. A. Meti et al, 2019 proposed a neural network to identify and classify the faults with auto associative integrated with cascade feed forward propagation method. There is an increase in correlation coefficient with a reduction in mean square error of the proposed architecture compared with the conventional auto associative neural network.…”

Section: Conventional Machine Learning and Deep Learning Modelsmentioning

confidence: 99%

Attention-Based Convolution Bidirectional Recurrent Neural Network for Sentiment Analysis

Sivakumar

Haritha

et al. 2022

International Journal of Decision Support System Technology

View full text Add to dashboard Cite

Customer conveys their opinion in natural language about an entity. Applying sentiment analysis to those reviews is a very complex task. The significance terms that are influencing the polarity of a review are not examined. The terms that are having contextual meaning are not recognized which are present across multiple sentences in a review. To address the above two issues, we have proposed an Attention-based Convolution Bi-directional Recurrent Neural Network (ACBRNN). In this model, two convolution layer captures phrase-level feature, while Self-Attention in the middle assigns high weight to the significant terms and Bi-directional GRU performs a conceptual scanning of review through forward and backward direction. We have conducted four different experiments viz., Unidirectional, Bidirectional, Hybrid and Proposed model on IMDB dataset to show the significance of the proposed model. The proposed model has obtained an F1 score of 87.94% on IMDB dataset which is 5.41% higher than CNN. Thus the proposed architecture performs well while comparing with all other baseline models.

show abstract

Section: Conventional Machine Learning and Deep Learning Modelsmentioning

confidence: 99%