Abstract

The willingness enables an electronic device or technology to accomplish projects that a human can do but faster and with fewer errors, like speech recognition, visual perception, cognitive thinking, decision-making, and experiential education, it is known as machine learning or Artificial Intelligence (AI). The many recent area of development that is utilized to improve the application of artificial intelligence (AI) in the domains of scientific investigation and the judicial system. Experts in Science of Crime and criminal investigation face many challenges today, including the deluge of data, the minute elements of testimony in the complex and chaotic conditions, the usual scientific setups, and occasionally the ignorance that could lead to a miscarriage of justice or an unsuccessful investigation. AI is the weapon of choice for overcoming various deep learning and machine learning-related problems. And to achieve such without error, impartial, and repeatable leads to in numerous forensics fields, neural networks have produced and case-based reasoning are utilized. These days, artificial intelligence (AI) is helping practically every well-known field in forensic science and criminal investigation. It does this through a variety of methods that include data extraction, statistical evaluation and probable techniques, machine learning, detection of patterns, image processing, machine learning, analysis of information, analytical and numerical methods, and pictorial modelling. Artificial intelligence is assisting forensic specialists and investigators by creating rational proof, creating three-dimensional reconstructions of scene of crime, efficiently managing evidence, and assessing it to draw reasonable inferences at different stages of an examination. In addition to being utilized for crime prevention, detection, and even prediction of future crimes or criminal conduct, AI-based algorithms are able to identify large volumes of data that indicate risk.

• 1.   Baraniuk, C. (2019, March 04). The new weapon in the fight against crime. BBC
• 2.   Moses, K. (2011). Chapter 6: Automatic fingerprint identification systems (AFIS).The fingerprint source book. National Institute of Justice, Washington DC, 6-1.
• 3.   Mitchell, F. (2010). The use of Artificial Intelligence in Digital Forensics: An introduction. Digital Evidence & Elec. Signature L. Rev., 7, 35.
• 4.   Green, M. (n.d.). Human Factors In Forensic Evidence*. Retrieved July 15, 2020, from https://www.visualexpert.com/Resources/ forensics.html
• 5.   Turvey, B.E. (2018). Criminal Profiling: Evidence, Experts, and Miscarriages of Justice. The Psychology and Sociology of Wrongful Convictions: Forensic Science Reform, 11.
• 6.   Kok, J.N., Boers, E.J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: definition, trends, techniques, and cases. Artificial intelligence, 1.
• 7.   Anirudh, V.K. (2019, October 17). What Is Deep Learning: Definition, Framework, and Neural et works Weblogpost.etrieveduly 2, 2020, from https://www.toolbox.com/tech/artificialintelligence/tech-101/what-is-deep-learningdefinition-framework-and-neural-networks/
• 8.   Krogh,A. (2008). Whatare artificial neural networks?.Nature biotechnology, 26(2),195-197.
• 9.   Franke, K., & Srihari, S. N. (2007, August). Computational forensics: Towards hybridintelligent crime investigation. In Third International Symposium on Information Assurance and Security (pp. 383-386). IEEE.
• 10.   Rigano, C. (2019). Using artificial intelligence to address criminal justice needs. National Institute of Justice. Issue,(280).
• 11.   Saba, T., & Rehman, A. (2013). Effects of artificially intelligent tools on pattern recognition. International Journal of Machine Learning and Cybernetics, 4(2), 155-162.
• 12.   Kasar, M. M., Bhattacharyya, D., & Kim, T. H. (2016). Face recognition using neural network: a review. International Journal of Security and Its Applications, 10(3), 81-100.
• 13.   Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2020). Supervised Deep Learning in Fingerprint Recognition. In Advances in Deep Learning (pp. 111-132). Springer, Singapore.
• 14.   Kasar, M.M., Bhattacharyya, D., & Kim, T.H. (2016). Face recognition using neural network: a review. International Journal of Security and Its Applications, 10(3), 81-100.
• 15.   Kulik, S.D. (2015). Neural Network Model Of Artificial Intelligence For Handwriting Recognition. Journal of Theoretical & Applied Information Technology, 73(2).
• 16.   Rigano, C. (2019). Using artificial intelligence to address criminal justice needs. National Institute of Justice. Issue, (280).
• 17.   Tortora, L., Meynen, G., Bijlsma, J., Tronci, E., & Ferracuti, S. (2020). Neuroprediction and AI in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective. Frontiers in Psychology, 11.
• 18.   Srivastava, N. (2014). Lie detection system using artificial neural network. Journal of Global Research in Computer Science, 5(8), 9-13.
• 19.   Takano, H., Momota, Y., Ozaki, T., Shiozawa, S., & Terada, K. (2019). Per-sonal Identification from Dental Findings Using AI and Image Analysis against Great Disaster in Japan. ForensicLeg Investig Sci, 5,041.
• 20.   Hoelz, B.W., Ralha, C.G., & Geeverghese, R. (2009, March). Artificial intelligence applied to computer forensics. In Proceedings of the 2009 ACM symposium on Applied Computing (pp. 883-888).
• 21.   Chinnikatti, S.K. (2018). Artificial Intelligence in Forensic Science. Computer, 46, 707- 727.
• 22.   Mitchell, F. (2010). The use of Artificial Intelligence in Digital Forensics: An introduction. Digital Evidence & Elec. Signature L. Rev., 7, 35.
• 23.   Hoelz, B.W., Ralha, C.G., & Geeverghese, R. (2009, March). Artificial intelligence applied to computer forensics. In Proceedings of the 2009 ACM symposium on Applied Computing (pp. 883-888).
• 24.   Mukkamala, S., & Sung, A.H. (2003). Identifying significant features for network forensic analysis using artificial intelligent techniques. International Journal of digital evidence, 1(4), 1-17.
• 25.   James, S., Kish, P.E., & Sutton, T.P. (2005). Principles of bloodstain pattern analysis: theory and practice. CRC Press.
• 26.   Acampora, G., Vitiello, A., Di Nunzio, C., Saliva, M., & Garofano, L. (2014, October). Bloodstain Pattern Analysis. In Proceedings of the International Joint Conference on Computational Intelligence-Volume 2 (pp. 211- 216). SCITEPRESS-Science and Technology Publications, Lda.
• 27.   Rigano, C. (2019). Using artificial intelligence to address criminal justice needs. National Institute of Justice. Issue, (280).
• 28.   Shapiro, A. (2017). Reform predictive policing.Nature News, 541 (7638), 458.
• 29.   Joshi, N. (2019, November 30). The rise of AI in crime prevention and detection Weblog post. Retrieved July 30, 2020, from https://www.allerin.com/blog/the-rise-of-ai-in-crimeprevention-and-detection.

Data Sharing Statement

Funding