A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews

2020 ◽  
Vol 11 (3) ◽  
pp. 1-22
Author(s):  
Jie Tao ◽  
Lina Zhou
Keyword(s):  
Deep Learning ◽  
Online Reviews ◽  
Learning Approach ◽  
Weakly Supervised

scholarly journals A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics

2021 ◽  
Vol 1 (6) ◽  
pp. 100094
Author(s):  
Corin F. Otesteanu ◽  
Martina Ugrinic ◽  
Gregor Holzner ◽  
Yun-Tsan Chang ◽  
Christina Fassnacht ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Learning Approach ◽  
Label Free ◽  
Imaging Flow Cytometry ◽  
Weakly Supervised

scholarly journals Deep Learning Approach of Product Evaluation Using Comment Analysis

2021 ◽  
Vol 9 (12) ◽  
pp. 1043-1047
Author(s):  
Syed Mudasar
Keyword(s):  
Deep Learning ◽  
Critical Role ◽  
Product Evaluation ◽  
Online Reviews ◽  
Learning Approach ◽  
Subjective Data ◽  
User Comments ◽  
Negative Sentiment ◽  
Purchasing Patterns

Abstract: Digital reviews now play a critical role in strengthening global consumer communications and influencing consumer purchasing patterns. Consumers can use e-commerce giants like Amazon, Flipchart, Snap deal, Jio and others to share their experiences and provide real insights about the performance of a product to future buyers. The classification of reviews into positive and negative sentiment is required in order to derive relevant insights from a big set of reviews. Comment Analysis is a computer programme that extracts subjective data from text. Out of Various Classification models Deep Learning Approach of Product Evaluation Using Comment Analysis is to develop a model that uses AI technologies like Deep Learning to process thousands and millions of online reviews on a product in a split second of time and rate the products on a scale of 1-5 based on the user comments We have worked on two deep learning models based on Recurrent Neural Networks (RNN) and Graph Convolution Network (GCN). Keywords: LSTN, GCN, NLTK

Weakly Supervised Deep Learning Approach to Breast MRI Assessment

2021 ◽  
Author(s):  
Michael Z Liu ◽  
Cara Swintelski ◽  
Shawn Sun ◽  
Maham Siddique ◽  
Elise Desperito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Mri ◽  
Learning Approach ◽  
Weakly Supervised

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Author response for "Deep learning and reinforcement learning approach on microgrid"

2020 ◽  
Author(s):  
Kumar Chandrasekaran ◽  
Prabaakaran Kandasamy ◽  
Srividhya Ramanathan
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Author Response ◽  
Learning Approach
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