An Investigation of Crime Detection Using Artificial Intelligence and Face Sketch Synthesis

References

• Anwarul, S., & Dahiya, S. (2022). Rectified DenseNet169-based automated criminal recognition system for the prediction of crime prone areas using face recognition. Journal of Electronic Imaging, 31(04), 043055. https://doi.org/10.1117/1.JEI.31.4.043055
   Google Scholar
• Aravindan, S., Anusuya, E., & Ashok Kumar, M. (2020). Gui based prediction of crime rate using machine learning approach. International Journal of Computer Science and Mobile Computing, 9(3), 221–229.
   Google Scholar
• Arora, M., & Kumar, M. (2021). AutoFER: PCA and PSO based automatic facial emotion recognition. Multimedia Tools and Applications, 80(2), 3039–3049. https://doi.org/10.1007/s11042-020-09726-4
   Web of Science ®Google Scholar
• Arul Kumar, P. V. (2022). Future robotics system design challenges including software engineering automation—A report. Journal of Electrical Engineering and Automation, 4(3), 175–186. https://doi.org/10.36548/jeea.2022.3.005
   Google Scholar
• Ateş, E. C. (2021). Big data, data mining, machine learning, and deep learning concepts in crime data. Journal of Penal Law & Criminology, 293–319. https://doi.org/10.26650/JPLC2020-813328
   Google Scholar
• Bandekar, S. R., & Vijayalakshmi, C. (2020). Design and analysis of machine learning algorithms for the reduction of crime rates in India. Procedia Computer Science, 172, 122–127. https://doi.org/10.1016/j.procs.2020.05.018
   Google Scholar
• Bansal, M., Kumar, M., & Kumar, M. (2021). 2D object recognition techniques: State-of-the-art work. Archives of Computational Methods in Engineering, 28(3), 1147–1161. https://doi.org/10.1007/s11831-020-09409-1
   Web of Science ®Google Scholar
• Bhuyan, H. K., & Pani, S. K. (2021). Crime predictive model using big data analytics. In: Pani SK, Singh SK, Garg L, et al. (eds). Intelligent data analytics for terror threat prediction (pp. 57–78). Wiley. https://doi.org/10.1002/9781119711629.ch3
   Google Scholar
• Boulos, M. (2021). Facial recognition and face mask detection using machine learning techniques [Theses]. Dissertations and Culminating Projects.
   Google Scholar
• Fontes, C., Hohma, E., Corrigan, C. C., & Lütge, C. (2022). AI-powered public surveillance systems: Why we (might) need them and how we want them. Technology in Society, 71, 102137. https://doi.org/10.1016/j.techsoc.2022.102137
   Web of Science ®Google Scholar
• Gupta, S., & Kumar, M. (2020). Forensic document examination system using boosting and bagging methodologies. Soft Computing, 24(7), 5409–5426. https://doi.org/10.1007/s00500-019-04297-5
   Web of Science ®Google Scholar
• Gupta, S., Thakur, K., & Kumar, M. (2021). 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions. The Visual Computer, 37(3), 447–456. https://doi.org/10.1007/s00371-020-01814-8
   Web of Science ®Google Scholar
• Gupta, S. K., Alemran, A., Singh, P., Khang, A., Dixit, C. K., & Haralayya, B. (2023). Image segmentation on Gabor filtered images using projective transformation [Paper presentation]. 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), 1–6. https://doi.org/10.1109/ICRTEC56977.2023.10111885
   Google Scholar
• Gupta, S. K., Pattnaik, B., Agrawal, V., Boddu, R. S. K., Srivastava, A., & Hazela, B. (2022). Malware detection using genetic cascaded support vector machine classifier in Internet of things [Paper presentation]. 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), 1–6. https://doi.org/10.1109/ICCSEA54677.2022.9936404
   Google Scholar
• Hossain, S., Abtahee, A., Kashem, I., Hoque, M. M., & Sarker, I. H. (2020). Crime prediction using spatio-temporal data. In: Chaubey, N., Parikh, S., Amin, K. (eds) Computing science, communication and security. COMS2 2020. Communications in computer and information science (pp. 277–289). Springer. https://doi.org/10.1007/978-981-15-6648-6_22
   Google Scholar
• Jayaswal, R., & Dixit, M. (2020). Comparative analysis of human face recognition by traditional methods and deep learning in real-time environment [Paper presentation]. 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), 66–71. https://doi.org/10.1109/CSNT48778.2020.9115779
   Google Scholar
• Jha, P., Jha, R., & Sharma, A. (2019). Behavior analysis and crime prediction using big data and machine learning. International Journal of Recent Technology and Engineering, 8(1), 461–468.
   Google Scholar
• Koul, S., Kumar, M., Khurana, S. S., Mushtaq, F., & Kumar, K. (2022). An efficient approach for copy-move image forgery detection using convolution neural network. Multimedia Tools and Applications, 81(8), 11259–11277. https://doi.org/10.1007/s11042-022-11974-5
   Web of Science ®Google Scholar
• Kumar, A., Kaur, A., & Kumar, M. (2019). Face detection techniques: A review. Artificial Intelligence Review, 52(2), 927–948. https://doi.org/10.1007/s10462-018-9650-2
   Web of Science ®Google Scholar
• Kumar, K. K., & Venkateswara Reddy, H. (2022). Crime activities prediction system in video surveillance by an optimized deep learning framework. Concurrency and Computation: Practice and Experience, 34(11), e6852. https://doi.org/10.1002/cpe.6852
   Web of Science ®Google Scholar
• Kumar, S., Pathak, S., & Singh, J. (2022). An enhanced digital forensic investigation framework for XSS attack. Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1009–1018. https://doi.org/10.1080/09720529.2022.2072424
   Web of Science ®Google Scholar
• Manna, S., Ghildiyal, S., & Bhimani, K. (2020). Face recognition from video using deep learning [Paper presentation]. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 1101–1106. https://doi.org/10.1109/ICCES48766.2020.9137927
   Google Scholar
• Mehta, S., & Shukla, P. (2023). A review on machine learning-based approaches for image forgery detection. In: Uddin, M.S., Bansal, J.C. (eds). Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2022. Algorithms for Intelligent Systems. (pp. 75–90). Springer. https://doi.org/10.1007/978-981-99-1435-7_8
   Google Scholar
• Najm, H., Ansaf, H., & Hassen, O. A. (2019). An effective implementation of face recognition using deep convolutional. Journal of Southwest Jiaotong University, 54(5). https://doi.org/10.35741/issn.0258-2724.54.5.29
   Google Scholar
• Oatley, G. C. (2022). Themes in data mining, big data, and crime analytics. WIREs Data Mining and Knowledge Discovery, 12(2), e1432. https://doi.org/10.1002/widm.1432
   Web of Science ®Google Scholar
• Onyema, E. M., Shukla, P. K., Dalal, S., Mathur, M. N., Zakariah, M., & Tiwari, B. (2021). Enhancement of patient facial recognition through deep learning algorithm: ConvNet. Journal of Healthcare Engineering, 2021, 5196000–5196008. https://doi.org/10.1155/2021/5196000
   PubMed Web of Science ®Google Scholar
• Peng, C., Wang, N., Li, J., & Gao, X. (2020). Face sketch synthesis in the wild via deep patch representation-based probabilistic graphical model. IEEE Transactions on Information Forensics and Security, 15, 172–183. https://doi.org/10.1109/TIFS.2019.2916633
   Web of Science ®Google Scholar
• Rajagopal, N. K., Qureshi, N. I., Durga, S., Ramirez Asis, E. H., Huerta Soto, R. M., Gupta, S. K., & Deepak, S. (2022). Future of business culture: An artificial intelligence-driven digital framework for organization decision-making process. Complexity, 2022, 1–14. https://doi.org/10.1155/2022/7796507
   Web of Science ®Google Scholar
• Rajapakshe, C., Balasooriya, S., Dayarathna, H., Ranaweera, N., Walgampaya, N., & Pemadasa, N. (2019). Using CNNs RNNs and machine learning algorithms for real-time crime prediction [Paper presentation]. 2019 International Conference on Advancements in Computing (ICAC), 310–316. https://doi.org/10.1109/ICAC49085.2019.9103425
   Google Scholar
• Shah, N., Bhagat, N., & Shah, M. (2021). Crime forecasting: A machine learning and computer vision approach to crime prediction and prevention. Visual Computing for Industry, Biomedicine, and Art, 4(1), 9. https://doi.org/10.1186/s42492-021-00075-z
   PubMedGoogle Scholar
• Singh, S., Ahuja, U., Kumar, M., Kumar, K., & Sachdeva, M. (2021). Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimedia Tools and Applications, 80(13), 19753–19768. https://doi.org/10.1007/s11042-021-10711-8
   PubMed Web of Science ®Google Scholar
• Sivakumar, P., Jayabalaguru, V., Ramsugumar, R., & Kalaisriram, S. (2021). Real time crime detection using deep learning algorithm [Paper presentation]. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), 1–5. https://doi.org/10.1109/ICSCAN53069.2021.9526393
   Google Scholar
• Wan, W., Yang, Y., & Lee, H. (2021). Generative adversarial learning for detail-preserving face sketch synthesis. Neurocomputing, 438, 107–121. https://doi.org/10.1016/j.neucom.2021.01.050
   Google Scholar
• Yan, L., Zheng, W., Gou, C., & Wang, F.-Y. (2021). IsGAN: Identity-sensitive generative adversarial network for face photo-sketch synthesis. Pattern Recognition, 119, 108077. https://doi.org/10.1016/j.patcog.2021.108077
   Web of Science ®Google Scholar