Nakib, MohammadKhan, Rozin TanvirHasan, Md. SakibulUddin, Jia2026-08-272026-08-272018-09-13M. Nakib, R. T. Khan, M. S. Hasan and J. Uddin, "Crime Scene Prediction by Detecting Threatening Objects Using Convolutional Neural Network," 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 2018, pp. 1-4, doi: 10.1109/IC4ME2.2018.8465583.97815386477522-s2.0-85055878966https://hdl.handle.net/10361/29546Crime scene prediction without human intervention can have outstanding impact on computer vision. In this paper, we present CNN (Convolutional Neural Network) in the use of detect knife, blood and gun from an image. Detecting these threatening objects from image can give us a prediction whether a crime occurred or not and from where the image is taken. We emphasized on the accuracy of detection so that it hardly gives us wrong alert to ensure efficient use of the system. This model use Rectified Linear Unit (ReLU), Convolutional Layer, Fully connected layer and dropout function of CNN to reach a result for the detection. We use Tensorflow, a open source platform to implement CNN to achieve our expected output. The proposed model achieves 90.2% accuracy for the tested dataset.4 Pagesen-USFeature extractionLoad modelingComputer scienceCamerasConvolutional neural networkCrime sceneTensorFlowObject detectionCrime prevention.Computer vision.Conference Proceeding10.1109/IC4ME2.2018.8465583