论文标题
使用多Anfis架构和时空数据的犯罪预测
Crime Prediction Using Multiple-ANFIS Architecture and Spatiotemporal Data
论文作者
论文摘要
仅统计价值就不能带来达卡市犯罪事件的整个情况。我们需要一种更好的方法来利用这些统计价值来预测犯罪事件,并使城市成为更安全的生活场所。对未来的适当决策是降低该地区或城市中刑事犯罪率的关键。如果执法机构可以为未来有效地分配资源,则达卡的犯罪率可以最低。在这项工作中,我们制定了一项倡议,以提供有效的工具,执法人员和侦探可以提前预测犯罪事件,并轻松,快速地做出更好的决定。我们已经使用了几种模糊的推理系统(FIS)和自适应神经模糊的推理系统(ANFI)来预测在某个地方和时间上很可能发生的犯罪类型。
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.