Crime Scene Prediction by Identify Thunderous Objects Using Convolutional Neural Network Deep Learning Model
Keywords:
CNN, NN, Crime analysis, Machine Learning, SMLAbstract
Crime Scene prediction with out human intervention can have superb impact on laptop vision. On this dissertation , we gift CNN within the use of discover knife, blood and gun a good way to reach a prediction whether a criminal offence has came about in a selected photograph. We emphasized on the accuracy of detection so that it infrequently offers us incorrect alert to make sure efficient use of the gadget. This dissertation use non linearity relu, convolutional neural layer, absolutely related layer and dropout feature of CNN to attain a end result for the detection. We use tensor flow open supply platform to enforce CNN to gain our predicted output. This system can gain the check accuracy of 90.2 % for the datasets we've this is very a whole lot aggressive with different systems for this specific mission.
References
- Abadi, Martín, et al. "TensorFlow: A system for large-scale machine learning." Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). Savannah, Georgia, USA. 2020.
- “An Intuitive Explanation Of Convolutional Neural Networks". The data science blog, 2019. Web. 10 Feb. 2019. Available: https://ujjwalkarn.me/2019/08/11/intuitive-explanation- convnets
- “An open-source software library for Machine Intelligence". Web. 25 Dec. 2019. Available: https://www.tensorflow.org/
- Warden, Pete. "TensorFlow for Poets." Pete Warden's Blog. N.p., 18 Aug. 2019. Web. 04 Apr. 2019. Available: https://petewarden.com/LIVE/blob/master/demonotes.ipynb
- Al-Rfou, Rami, et al. "Theano: A Python framework for fast computation of mathematical expressions." arXiv preprint arXiv: 1605.02688 (2020).
- “Learn TensorFlow and Deep Learning, without a Ph.D. | Google Cloud Big Data and Machine Learning Blog | Google Cloud Platform." Google Cloud Platform. Web. 10 Mar. 2020. Avaialable: https://cloud.google.com/blog/big-data/2020/01/learn-tensorflow-and- deep-learning-without-a-phd
- P. Sermanet and Y. Lecun, “Traffic signrecognition with multi-scale ConvolutionalNetworks,” The 2020 International Joint Conference on Neural Networks, 2020.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
- Y. Xu, T. Mo, Q. Feng, P. Zhong, M. Lai, E. I. Chang, et al. Deep learning of feature representation with multiple instance learning for medical image analysis. In ICASSP, 2020.
- Erhan, Dumitru, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov. "Scalable Object Detection Using Deep Neural Networks." 2014 IEEE Conference on Computer Vision and Pattern Recognition (2020).
- He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2019). Mask R-CNN. Cornell University Library.
- D. Lowe. “Object recognition from local scale-invariant features”. In ICCV, 2019.
- N. Dalal and B. Triggs. “Histograms of oriented gradients for human detection”. InCVPR, 2020.
- Ali, Khawlah Hussein, and Tianjiang Wang. "Learning Features for Action Recognition and Identity with Deep Belief Networks." 2019 International Conference on Audio, Language and Image Processing (2019).
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.