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Crime Prediction Using Multiple-ANFIS Architecture and Spatiotemporal Data
arXiv - CS - Logic in Computer Science Pub Date : 2020-11-07 , DOI: arxiv-2011.05805 Mashnoon Islam, Redwanul Karim, Kalyan Roy, Saif Mahmood, Sadat Hossain, M. Rashedur Rahman
arXiv - CS - Logic in Computer Science Pub Date : 2020-11-07 , DOI: arxiv-2011.05805 Mashnoon Islam, Redwanul Karim, Kalyan Roy, Saif Mahmood, Sadat Hossain, M. Rashedur Rahman
Statistical values alone cannot bring the whole scenario of crime occurrences
in the city of Dhaka. We need a better way to use these statistical values to
predict crime occurrences and make the city a safer place to live. Proper
decision-making for the future is key in reducing the rate of criminal offenses
in an area or a city. If the law enforcement bodies can allocate their
resources efficiently for the future, the rate of crime in Dhaka can be brought
down to a minimum. In this work, we have made an initiative to provide an
effective tool with which law enforcement officials and detectives can predict
crime occurrences ahead of time and take better decisions easily and quickly.
We have used several Fuzzy Inference Systems (FIS) and Adaptive Neuro-Fuzzy
Inference Systems (ANFIS) to predict the type of crime that is highly likely to
occur at a certain place and time.
中文翻译:
使用多 ANFIS 架构和时空数据的犯罪预测
仅靠统计值并不能反映达卡市犯罪事件的全部情况。我们需要一种更好的方法来使用这些统计值来预测犯罪事件并使城市成为更安全的居住地。为未来做出正确的决策是降低一个地区或一个城市的刑事犯罪率的关键。如果执法机构能够为未来有效分配资源,达卡的犯罪率就可以降到最低。在这项工作中,我们主动提供了一种有效的工具,执法人员和侦探可以利用它提前预测犯罪事件并轻松快速地做出更好的决定。
更新日期:2020-11-12
中文翻译:
使用多 ANFIS 架构和时空数据的犯罪预测
仅靠统计值并不能反映达卡市犯罪事件的全部情况。我们需要一种更好的方法来使用这些统计值来预测犯罪事件并使城市成为更安全的居住地。为未来做出正确的决策是降低一个地区或一个城市的刑事犯罪率的关键。如果执法机构能够为未来有效分配资源,达卡的犯罪率就可以降到最低。在这项工作中,我们主动提供了一种有效的工具,执法人员和侦探可以利用它提前预测犯罪事件并轻松快速地做出更好的决定。