scholarly journals AUTOMATIC QUESTION GENERATION MODEL BASED ON DEEP LEARNING APPROACH

2021 ◽  
Vol 21 (2) ◽  
pp. 110-123
Author(s):  
Mai Mokhtar ◽  
Salma Doma ◽  
Hala Abdel-Galil
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Generation Model ◽  
Question Generation ◽  
Model Based ◽  
Automatic Question Generation

Clustering single-cell RNA-seq data with a model-based deep learning approach

2019 ◽  
Vol 1 (4) ◽  
pp. 191-198 ◽  
Author(s):  
Tian Tian ◽  
Ji Wan ◽  
Qi Song ◽  
Zhi Wei
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Learning Approach ◽  
Rna Seq ◽  
Model Based

scholarly journals Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

2020 ◽  
Vol 11 (3) ◽  
pp. 2235-2244 ◽  
Author(s):  
Yan Yang ◽  
Zhifang Yang ◽  
Juan Yu ◽  
Baosen Zhang ◽  
Youqiang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Flow ◽  
Learning Approach ◽  
Fast Calculation ◽  
Model Based ◽  
Probabilistic Power Flow

Action Generation Model Based on Changes in State Patterns

2009 ◽  
Vol 129 (9) ◽  
pp. 1690-1698
Author(s):  
Manabu Gouko ◽  
Naoki Tomi ◽  
Tomoaki Nagano ◽  
Koji Ito
Keyword(s):  
Generation Model ◽  
Model Based

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.

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