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Yıl 2020, , 293 - 319, 18.11.2020
https://doi.org/10.26650/JPLC2020-813328

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Kaynakça

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Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Yıl 2020, , 293 - 319, 18.11.2020
https://doi.org/10.26650/JPLC2020-813328

Öz

Along with the rapid change of information technologies and the widespread use of the internet over time, data stacks with ample diversity and quite large volumes has emerged. The use of data mining is increasing day by day due to the huge part it plays in the acquisition of information by making necessary analyses especially within a large amount of data. Obtaining accurate information is a key factor affecting decision-making processes. Crime data is included among the application areas of data mining, being one of the data stacks which is rapidly growing with each passing day. Crime events constitute unwanted behaviour in every society. For this reason, it is important to extract meaningful information from crime data. This article aims to provide an overview of the use of data mining and machine learning in crime data and to give a new perspective on the decision-making processes by presenting examples of the use of data mining for a crime. For this purpose, some examples of data mining and machine learning in crime and security areas are presented by giving a conceptual framework in the subject of big data, data mining, machine learning, and deep learning along with task types, processes, and methods.

Proje Numarası

Proje kapsamında değildir.

Kaynakça

  • Abdullah, N., Ismail, S. A., Sophiayati, S., & Sam, S. M. (2015). Data quality in big data: a review. International Journal of Advances in Soft Computing & Its Applications, 7(3). google scholar
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  • Adewumi, A. O., & Akinyelu, A. A. (2017). A survey of machine-learning and nature-inspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8(2), 937-953. google scholar
  • Aggarwal, C. C. (2018). Machine learning for text. Cham: Springer International Publishing. google scholar
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  • Agu, S. C., Ajah, I., & Ibe, W. E. (2019). Impact of Human Character and Information System on Corruption Risk in Nigeria. International Journal of Scientific Research and Engineering Development, 2(4), 481-485. google scholar
  • Ahmed, A. (2020). “From Data to Wisdom” Using Machine Learning Capabilities in Accounting and Finance Professionals. Talent Development & Excellence, 12. google scholar
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  • Akdemir, N., & Lawless, C. J. (2020). Exploring the human factor in cyber-enabled and cyber-dependent crime victimisation: a lifestyle routine activities approach. Internet Research, 30(6), 1665-1687. google scholar
  • Aledhari, M., Di Pierro, M., Hefeida, M., & Saeed, F. (2018). A deep learning-based data minimization algorithm for fast and secure transfer of big genomic datasets. IEEE Transactions on Big Data. google scholar
  • Arora, S., Bhatia, M. P. S., & Kukreja, H. (2020, February). A Multimodal Biometric System for Secure User Identification Based on Deep Learning. In International Congress on Information and Communication Technology (pp. 95-103). Springer, Singapore. google scholar
  • Arshad, H., Jantan, A., & Omolara, E. (2019). Evidence collection and forensics on social networks: Research challenges and directions. Digital Investigation, 28, 126-138. google scholar
  • Ateş, E.C., Bostanci, E., & Guzel, M. S. (2020). Security Evaluation of Industry 4.0: Understanding Industry 4.0 on the Basis of Crime, Big Data, Internet Of Thing (IoT) and Cyber Physical Systems. Güvenlik Bilimleri Dergisi, (International Security Congress Special Issue), 29-50. google scholar
  • Ayre, L. B., & Craner, J. (2017). Open data: What it is and why you should care. Public Library Quarterly, 36(2), 173-184. google scholar
  • Beniwal, S., & Arora, J. (2012). Classification and feature selection techniques in data mining. International journal of engineering research & technology (IJERT), 1(6), 1-6. google scholar
  • Berk, R. (2017). An impact assessment of machine learning risk forecasts on parole board decisions and recidivism. Journal of Experimental Criminology, 13(2), 193-216. google scholar
  • Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping multidimensional data (pp. 25- 71). Springer, Berlin, Heidelberg. google scholar
  • Bhuyan, M. H., Saharia, S., & Bhattacharyya, D. K. (2012). An effective method for fingerprint classification. arXiv preprint arXiv:1211.4658. google scholar
  • Blei, D. M., & Smyth, P. (2017). Science and data science. Proceedings of the National Academy of Sciences, 114(33), 8689-8692. google scholar
  • Bock, F. E., Aydin, R. C., Cyron, C. J., Huber, N., Kalidindi, S. R., & Klusemann, B. (2019). A review of the application of machine learning and data mining approaches in continuum materials mechanics. Frontiers in Materials, 6, 110. google scholar
  • Bode, J. (2019, June). Every Contact Leaves a Trace: A Literary Reality of Locard’s Exchange Principle. In Outside the Box: A Multi-Lingual Forum (p. 18). google scholar
  • Bostanci, E. (2015). 3D reconstruction of crime scenes and design considerations for an interactive investigation tool. arXiv preprint arXiv:1512.03156. google scholar
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679. google scholar
  • Bulgakova, E., Bulgakov, V., Trushchenkov, I., Vasilev, D., & Kravets, E. (2019). Big data in investigating and preventing crimes. In Big Data-driven World: Legislation Issues and Control Technologies (pp. 61-69). Springer, Cham. google scholar
  • Campbell, C., & Ying, Y. (2011). Learning with support vector machines. Synthesis lectures on artificial intelligence and machine learning, 5(1), 1-95. google scholar
  • Ch, R., Gadekallu, T. R., Abidi, M. H., & Al-Ahmari, A. (2020). Computational System to Classify Cyber Crime Offenses Using Machine Learning. Sustainability, 12(10), 4087. google scholar
  • Chan, J., & Bennett Moses, L. (2017). Making sense of big data for security. The British journal of criminology, 57(2), 299-319. google scholar
  • Chau, D. H., Pandit, S., & Faloutsos, C. (2006, September). Detecting fraudulent personalities in networks of online auctioneers. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 103-114). Springer, Berlin, Heidelberg. google scholar
  • Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209. google scholar
  • Clarke, C. (2006). Proactive policing: Standing on the shoulders of community‐based policing. Police Practice and Research, 7(1), 3-17. google scholar
  • Commission, (2017). Kriminalistik. Gendarmerie and Coast Guard Academy, Ankara. google scholar
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Toplam 102 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hukuk
Bölüm Araştırma Makalesi
Yazarlar

Emre Cihan Ateş 0000-0001-9550-4532

Gazi Erkan Bostancı 0000-0001-8547-7569

Serdar Msg 0000-0002-3408-0083

Proje Numarası Proje kapsamında değildir.
Yayımlanma Tarihi 18 Kasım 2020
Gönderilme Tarihi 20 Ekim 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Ateş, E. C., Bostancı, G. E., & Msg, S. (2020). Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data. Ceza Hukuku Ve Kriminoloji Dergisi, 8(2), 293-319. https://doi.org/10.26650/JPLC2020-813328
AMA Ateş EC, Bostancı GE, Msg S. Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data. Ceza Hukuku ve Kriminoloji Dergisi. Kasım 2020;8(2):293-319. doi:10.26650/JPLC2020-813328
Chicago Ateş, Emre Cihan, Gazi Erkan Bostancı, ve Serdar Msg. “Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data”. Ceza Hukuku Ve Kriminoloji Dergisi 8, sy. 2 (Kasım 2020): 293-319. https://doi.org/10.26650/JPLC2020-813328.
EndNote Ateş EC, Bostancı GE, Msg S (01 Kasım 2020) Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data. Ceza Hukuku ve Kriminoloji Dergisi 8 2 293–319.
IEEE E. C. Ateş, G. E. Bostancı, ve S. Msg, “Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data”, Ceza Hukuku ve Kriminoloji Dergisi, c. 8, sy. 2, ss. 293–319, 2020, doi: 10.26650/JPLC2020-813328.
ISNAD Ateş, Emre Cihan vd. “Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data”. Ceza Hukuku ve Kriminoloji Dergisi 8/2 (Kasım 2020), 293-319. https://doi.org/10.26650/JPLC2020-813328.
JAMA Ateş EC, Bostancı GE, Msg S. Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data. Ceza Hukuku ve Kriminoloji Dergisi. 2020;8:293–319.
MLA Ateş, Emre Cihan vd. “Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data”. Ceza Hukuku Ve Kriminoloji Dergisi, c. 8, sy. 2, 2020, ss. 293-19, doi:10.26650/JPLC2020-813328.
Vancouver Ateş EC, Bostancı GE, Msg S. Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data. Ceza Hukuku ve Kriminoloji Dergisi. 2020;8(2):293-319.