scholarly journals Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective

2020 ◽  
Vol Volume 13 ◽  
pp. 355-371
Author(s):  
Jasleen Kaur ◽  
Asif Irshad Khan ◽  
Yoosef B Abushark ◽  
Md Mottahir Alam ◽  
Suhel Ahmad Khan ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference System ◽  
Web Application ◽  
Fuzzy Inference ◽  
Security Risk ◽  
Inference System ◽  
System A ◽  
Neuro Fuzzy ◽  
Design Perspective ◽  
Security Risk Assessment

scholarly journals ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

2015 ◽  
Vol 123 (13) ◽  
pp. 32-38 ◽  
Author(s):  
Navneet Walia ◽  
Harsukhpreet Singh ◽  
Anurag Sharma
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
System A ◽  
Neuro Fuzzy

Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro-fuzzy inference system and K-means clustering

2016 ◽  
Vol 33 (3) ◽  
pp. 230-238 ◽  
Author(s):  
Mahyar Taghizadeh Nouei ◽  
Ali Vahidian Kamyad ◽  
MahmoodReza Sarzaeem ◽  
Somayeh Ghazalbash
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Coronary Surgery ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Risk Assessment

scholarly journals Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

2020 ◽  
Vol 10 (15) ◽  
pp. 5156
Author(s):  
Hamad Alawad ◽  
Min An ◽  
Sakdirat Kaewunruen
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Railway Stations ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Managing Risk

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.

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