Driver Identification Using Vehicular Sensing Data: A Deep Learning Approach

2021 ◽  
Author(s):  
Mozhgan Nasr Azadani ◽  
Azzedine Boukerche
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Sensing Data ◽  
Driver Identification ◽  
Vehicular Sensing

scholarly journals Deep Learning Approach in Automatic Iceberg - Ship Detection with SAR Remote Sensing Data

2018 ◽  
Author(s):  
Cheng Zhan ◽  
Licheng Zhang ◽  
Zhenzhen Zhong ◽  
Sher Didi-Ooi ◽  
Youzuo Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Remote Sensing Data ◽  
Learning Approach ◽  
Sensing Data ◽  
Ship Detection ◽  
Sar Remote Sensing

scholarly journals A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing data

2021 ◽  
Author(s):  
Martijn van den Ende ◽  
Itzhak Lior ◽  
Jean-Paul Ampuero ◽  
Anthony Sladen ◽  
André Ferrari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Acoustic Sensing ◽  
Sensing Data ◽  
Distributed Acoustic Sensing

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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