Abstract
Criminal Investigation (CI) plays an important role in policing, where police use various traditional techniques to investigate criminal activities such as robbery and assault. However, the techniques should hybrid with the use of artificial intelligence to analyze and determine different crime types for taking actions in real-time. In contrast with the manual process of investigating a large amount of data collected related to a criminal investigation. In this paper, we present a novel Cognitive Computing enabled Convolution Neural Networks (CC-CNN) approach for identifying crime types, such as robbery and assault, collected from unstructured textual data. We develop learning algorithms and provide a cognitive assistant to assist a police investigator in easily understanding crime types. We train and validate the CC-CNN technique on two datasets including handcrafted text-crime dataset and sentiment polarity dataset of negative and positive reviews. The experimental results show that our approach performs at a high level in terms of accuracy, error rate and time processing using both datasets.
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References
Higginson, A., Eggins, E., Mazerolle, L.: Police techniques for investigating serious violent crime: a systematic review. Trends Issues Crime Crim. Justice 539, 1–13 (2017)
Loughnan, A.: The legislation we had to have?: the crimes (criminal organisations control) act 2009 (NSW). Curr. Issues Crim. Justice 20(3), 457–465 (2009)
Police Investigation process, October 2019. https://www.victimsofcrime.vic.gov.au/police-investigation/the-investigation
Connor, M.A.: Professionalism in forensic archaeology: transitioning from “Cowboy of Science” to “Officer of the Court”. In: Forensic Archaeology, pp. 33–42. Springer, Cham (2019)
Cronin, J.M., Murphy, G.R., Spahr, L.L., Toliver, J.I., Weger, R.E.: Promoting Effective Homicide Investigation (2007) (2019)
Stelfox, P.: Criminal investigation: an introduction to principles and practice. Willan (2013)
Scalia, V.: Martin O’Neill-Key (2018). Challenges in Criminal Investigation. Policing: J. Policy Pract. (2018)
Liu, Y., et al.: Crime scene investigation image retrieval with fusion CNN features based on transfer learning. In: Proceedings of the 3rd International Conference on Multimedia and Image Processing. ACM (2018)
Hu, Y., et al.: Short text classification with a convolutional neural networks based method. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE (2018)
Tzelepi, M., Tefas, A.: Deep convolutional learning for content based image retrieval. Neurocomputing 275, 2467–2478 (2018)
Conneau, A., Schwenk, H., Barraul, L., et al.: Very deep convolutional networks for text classification. Assoc. Comput. Linguist. 1, 107–1116 (2017)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.A.: Convolutional neural network for modelling sentences. Assoc. Comput. Linguist. 1, 655–665 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746–1751 (2014)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2267–2273 (2015)
Lee, J.Y., Dernoncourt, F.: Sequential short-text classification with recurrent and convolutional neural networks. In: The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 515–520 (2016)
Sentiment Polarity datasets, October 2019. http://www.cs.cornell.edu/people/pabo/movie-review-data/
Rong, X.: word2vec parameter learning explained. arXiv preprint arXiv:1411.2738 (2014)
CNN text classification TensorFlow, October 2019. http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
Fleming, J.: The pursuit of professionalism: lessons from Australasia. The future of policing, pp. 385–398. Routledge (2013)
Koper, C.S., Lum, C., Willis, J.J.: Optimizing the use of technology in policing: results and implications from a multi-site study of the social, organizational, and behavioural aspects of implementing police technologies. Policing: J. Policy Pract. 8(2), 212–221 (2014)
Gehl, R., Plecas, D.: Introduction to criminal investigation: processes, practices and thinking. Justice Institute of British Columbia (2017)
Baber, C., Smith, P., Cross, J., Hunter, J.E., McMaster, R.: Crime scene investigation as distributed cognition. Pragmat. Cogn. 14(2), 357–385 (2006)
Braga, A.A.: Moving the work of criminal investigators towards crime control. Harvard Kennedy School Program in Criminal Justice Policy and Management (2011)
Ariel, B., et al.: Report: increases in police use of force in the presence of body-worn cameras are driven by officer discretion: a protocol-based subgroup analysis of ten randomized experiments. J. Exp. Criminol. 12(3), 453–463 (2016). https://doi.org/10.1007/s11292-016-9261-3
Parris, H.: The home office and the provincial police in England and Wales—1856–1870. In: The New Police in the Nineteenth Century, pp. 117–142. Routledge (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Saikia, M., Baruah, B.: Chaotic map based image encryption in Spatial domain: a brief survey. In: Proceedings of the First International Conference on Intelligent Computing and Communication. Springer, Singapore (2017)
Schiliro, F., Beheshti, A., Ghodratnama, S., Amouzgar, F., Benatallah, B., Yang, J., Sheng, Q.Z., Casati, F., Motahari-Nezhad, H.R.: iCOP: IoT-enabled policing processes. In: International Conference on Service-Oriented Computing, pp. 447–452. Springer, Cham, November 2018
Moustafa, N., Creech, G., Sitnikova, E., Keshk, M.: Collaborative anomaly detection framework for handling big data of cloud computing. In: 2017 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE, November 2017
Moustafa, N., Creech, G., Slay, J.: Anomaly detection system using beta mixture models and outlier detection. In: Progress in Computing, Analytics and Networking, pp. 125–135. Springer, Singapore (2018)
Keshk, M., Moustafa, N., Sitnikova, E., Creech, G.: Privacy preservation intrusion detection technique for SCADA systems. In: 2017 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE, November 2017
Beheshti, A., Schiliro, F., Ghodratnama, S., Amouzgar, F., Benatallah, B., Yang, J., Motahari-Nezhad, H.R.: iProcess: enabling IoT platforms in data-driven knowledge-intensive processes. In: International Conference on Business Process Management, pp. 108–126. Springer, Cham, September 2018
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We Acknowledge the AI-enabled Processes (AIPFootnote 1) Research Centre and Spitfire Memorial Defence Grant (PS39150) for funding this research.
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Schiliro, F., Beheshti, A., Moustafa, N. (2021). A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_39
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