scholarly journals A Deep Learning Approach Towards Railway Safety Risk Assessment

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 102811-102832 ◽  
Author(s):  
Hamad Alawad ◽  
Sakdirat Kaewunruen ◽  
Min An
Keyword(s):  
Risk Assessment ◽  
Deep Learning ◽  
Learning Approach ◽  
Safety Risk ◽  
Railway Safety

scholarly journals An Intelligent Railway Safety Risk Assessment Support System for Railway Operation and Maintenance Analysis

2013 ◽  
Vol 7 (1) ◽  
pp. 27-42 ◽  
Author(s):  
Min An ◽  
Wanchang Lin ◽  
Sheng Huang
Keyword(s):  
Risk Assessment ◽  
Support System ◽  
Failure Modes ◽  
Assessment System ◽  
System Level ◽  
Risk Category ◽  
Component Level ◽  
Safety Risk ◽  
Railway Safety ◽  
Uniform Manner

The paper presents the development of an intelligent railway safety risk assessment based support system. The proposed method can evaluate qualitative and quantitative safety risk data and information in a uniform manner for railway safety risk assessment. It permits the safety risk analysts to assess the risks associated with the failure modes directly using linguistic terms, i.e. qualitative descriptors. The proposed intelligent railway safety risk assessment system is capable of assessing the risks at component level, sub-system level and system level. It can assess not only “hard” risks (e.g. risks of a system), but also “soft” risks (e.g. staff risks). The outcomes of safety risk assessment are represented in two formats, risk score and risk category with a belief of percentage, which provide very useful safety risk information to railway designers, operators, engineers and maintainers for risk response decision making. An illustrative example of staff risk assessment in a railway depot is used to demonstrate the proposed intelligent railway safety risk assessment system. The results indicate that by using the proposed system, risks associated with a railway depot can be assessed effectively and efficiently.

Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach

2020 ◽  
Author(s):  
M. Parimala ◽  
R. M. Swarna Priya ◽  
M. Praveen Kumar Reddy ◽  
Chiranji Lal Chowdhary ◽  
Ravi Kumar Poluru ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Approach

Optimization Driven Deep Learning Approach for Health Monitoring and Risk Assessment in Wireless Body Sensor Networks

2020 ◽  
Vol 16 (1) ◽  
pp. 70-93 ◽  
Author(s):  
Abdalla Alameen ◽  
Ashu Gupta
Keyword(s):  
Risk Assessment ◽  
Deep Learning ◽  
Sensor Networks ◽  
Health Risk ◽  
Body Sensor Networks ◽  
Learning Approach ◽  
Target Node ◽  
Swarm Optimization ◽  
Health Records ◽  
Wireless Body Sensor Networks

Wireless body sensor networks (WBSNs) plays a vital role in monitoring health conditions of patients and is a low-cost solution for dealing with several healthcare applications. Processing large amounts of data and making feasible decisions in emergency cases are the major challenges for WBSNs. Thus, this article addresses these challenges by designing a deep learning approach for health risk assessment by proposing a Fractional Cat-based Salp Swarm Algorithm (FCSSA). At first, the WBSN nodes are utilized for sensing data from patient health records to acquire certain parameters for making the assessment. Based on the obtained parameters, WBSN nodes transmit the data to the target node. Here, the hybrid Harmony Search Algorithm and Particle Swarm Optimization (hybrid HSA-PSO) is used for determining the optimal cluster head. Then, the results produced by the hybrid HSA-PSO are given to the target node, in which the Deep Belief Network (DBN) is used for classifying the health records for the health risk assessment. Here, the DBN is trained using the proposed FCSSA, which is developed by integrating a Fractional Cat Swarm Optimization (FCSO) and Salp Swarm Algorithm (SSA) for initiating the classification. The proposed FCSSA shows better performance using metrics, namely accuracy, energy and throughput with values 94.604, 0.145, and 0.058, respectively.

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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