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Safest Route Detection via Danger Index Calculation and K-Means Clustering
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作者 isha puthige Kartikay Bansal +8 位作者 Chahat Bindra Mahekk Kapur Dilbag Singh Vipul Kumar Mishra Apeksha Aggarwal Jinhee Lee Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa 《Computers, Materials & Continua》 SCIE EI 2021年第11期2761-2777,共17页
The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset,which provides us with location tagged crime statistics;we are im... The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset,which provides us with location tagged crime statistics;we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one.The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results.Moreover,a comparative analysis has been performed among various clustering techniques to obtain best results.we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users.The successful implementation would hopefully aid us to curb the ever-increasing crime rates;as it aims to provide the user with a beforehand knowledge of the route they are about to take.A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer.Thus,addressing a social problem which needs to be eradicated from our modern era. 展开更多
关键词 Agglomerative CLUSTERING crime rate danger index DBSCAN
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