Matches in SemOpenAlex for { <https://semopenalex.org/work/W3031844275> ?p ?o ?g. }
- W3031844275 endingPage "102832" @default.
- W3031844275 startingPage "102811" @default.
- W3031844275 abstract "Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks." @default.
- W3031844275 created "2020-06-05" @default.
- W3031844275 creator A5011024488 @default.
- W3031844275 creator A5033737553 @default.
- W3031844275 creator A5051303118 @default.
- W3031844275 date "2020-01-01" @default.
- W3031844275 modified "2023-10-14" @default.
- W3031844275 title "A Deep Learning Approach Towards Railway Safety Risk Assessment" @default.
- W3031844275 cites W130013534 @default.
- W3031844275 cites W1576730730 @default.
- W3031844275 cites W1615893186 @default.
- W3031844275 cites W1641498739 @default.
- W3031844275 cites W1871050032 @default.
- W3031844275 cites W1888172398 @default.
- W3031844275 cites W1925745898 @default.
- W3031844275 cites W1966580635 @default.
- W3031844275 cites W1971085546 @default.
- W3031844275 cites W1973143425 @default.
- W3031844275 cites W1978784418 @default.
- W3031844275 cites W1979617887 @default.
- W3031844275 cites W1983364832 @default.
- W3031844275 cites W2005029343 @default.
- W3031844275 cites W2008748781 @default.
- W3031844275 cites W2014850865 @default.
- W3031844275 cites W2016053056 @default.
- W3031844275 cites W2017803470 @default.
- W3031844275 cites W2022367724 @default.
- W3031844275 cites W2022844898 @default.
- W3031844275 cites W2024688311 @default.
- W3031844275 cites W2030036829 @default.
- W3031844275 cites W2042568090 @default.
- W3031844275 cites W2046286451 @default.
- W3031844275 cites W2047389622 @default.
- W3031844275 cites W2055591905 @default.
- W3031844275 cites W2056818943 @default.
- W3031844275 cites W2064132797 @default.
- W3031844275 cites W2076871221 @default.
- W3031844275 cites W2086902370 @default.
- W3031844275 cites W2089253196 @default.
- W3031844275 cites W2091149439 @default.
- W3031844275 cites W2093866254 @default.
- W3031844275 cites W2095124372 @default.
- W3031844275 cites W2097470741 @default.
- W3031844275 cites W2101286829 @default.
- W3031844275 cites W2114588272 @default.
- W3031844275 cites W2120999040 @default.
- W3031844275 cites W2136877695 @default.
- W3031844275 cites W2139730689 @default.
- W3031844275 cites W2145287260 @default.
- W3031844275 cites W2155200364 @default.
- W3031844275 cites W2155273149 @default.
- W3031844275 cites W2191848209 @default.
- W3031844275 cites W2239589426 @default.
- W3031844275 cites W2242707865 @default.
- W3031844275 cites W2253429366 @default.
- W3031844275 cites W2278035293 @default.
- W3031844275 cites W2290320094 @default.
- W3031844275 cites W2293063104 @default.
- W3031844275 cites W2295002716 @default.
- W3031844275 cites W2295706091 @default.
- W3031844275 cites W2312738360 @default.
- W3031844275 cites W2341973567 @default.
- W3031844275 cites W239591682 @default.
- W3031844275 cites W2396510984 @default.
- W3031844275 cites W2401239412 @default.
- W3031844275 cites W2406523001 @default.
- W3031844275 cites W2407692387 @default.
- W3031844275 cites W2411835804 @default.
- W3031844275 cites W2414900880 @default.
- W3031844275 cites W2471989303 @default.
- W3031844275 cites W2479115394 @default.
- W3031844275 cites W2499698264 @default.
- W3031844275 cites W2511065100 @default.
- W3031844275 cites W2531409750 @default.
- W3031844275 cites W2535374659 @default.
- W3031844275 cites W2541166653 @default.
- W3031844275 cites W2560722161 @default.
- W3031844275 cites W2588142644 @default.
- W3031844275 cites W2589057013 @default.
- W3031844275 cites W2618146632 @default.
- W3031844275 cites W2620175222 @default.
- W3031844275 cites W2698753202 @default.
- W3031844275 cites W2753777934 @default.
- W3031844275 cites W2761891891 @default.
- W3031844275 cites W2768148056 @default.
- W3031844275 cites W2772016598 @default.
- W3031844275 cites W2773972523 @default.
- W3031844275 cites W2774869152 @default.
- W3031844275 cites W2790722345 @default.
- W3031844275 cites W2790977031 @default.
- W3031844275 cites W2791012243 @default.
- W3031844275 cites W2791697444 @default.
- W3031844275 cites W2793899888 @default.
- W3031844275 cites W2794204271 @default.
- W3031844275 cites W2796755741 @default.
- W3031844275 cites W2801035401 @default.
- W3031844275 cites W2801492038 @default.
- W3031844275 cites W2802084776 @default.