Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach

2020 ◽  
Vol 253 ◽  
pp. 119841 ◽  
Author(s):  
Hong-Wei Wang ◽  
Xiao-Bing Li ◽  
Dongsheng Wang ◽  
Juanhao Zhao ◽  
Hong-di He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ground Level ◽  
Learning Approach ◽  
Ground Level Ozone ◽  
Hybrid Sequence ◽  
Regional Prediction

scholarly journals An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

2020 ◽  
Vol 54 (18) ◽  
pp. 11037-11047 ◽  
Author(s):  
Weeberb J. Requia ◽  
Qian Di ◽  
Rachel Silvern ◽  
James T. Kelly ◽  
Petros Koutrakis ◽  
...  
Keyword(s):  
United States ◽  
Ensemble Learning ◽  
Ground Level ◽  
Learning Approach ◽  
Spatiotemporal Resolution ◽  
Ground Level Ozone ◽  
High Spatiotemporal Resolution

scholarly journals Estimating Ground-Level PM2.5by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach

2017 ◽  
Vol 44 (23) ◽  
pp. 11,985-11,993 ◽  
Author(s):  
Tongwen Li ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Xuechen Zhang ◽  
Liangpei Zhang
Keyword(s):  
Deep Learning ◽  
Ground Level ◽  
Learning Approach

The study of ground-level ozone over Kyiv and its impact on public health

2011 ◽  
Vol 17 (1) ◽  
pp. 52-59
Author(s):  
A.V. Shavrina ◽  
◽  
I.A. Mikulskaya ◽  
S.I. Kiforenko ◽  
V.A. Sheminova ◽  
...  
Keyword(s):  
Public Health ◽  
Ground Level ◽  
Ground Level Ozone

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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