Abstract
The exacerbation of high crime rate has become a critical impediment to the country’s economy, therefore necessitating the involvement of data analysts and scientists in extracting invaluable insights from crime data to proffer solutions towards crime prevention and regulation. To this end, this study presents a novel Particle Swarm-Cuckoo Search (PS-CS) optimization algorithm which leverages the potency of both particle swarm optimization and cuckoo search algorithms to enhance the optimization of network parameters and enable the training of deep neural networks for crime prediction. The proposed PS-CS model supersedes the traditional backpropagation algorithm which is fraught with limitations such as slow convergence and a proclivity for local optima. The implementation of the proposed model has the potential to be an efficacious tool for predicting crime rates in India, thus facilitating the efforts of law enforcement agencies in controlling and curbing criminal activities. We conducted a comprehensive and intricate evaluation of the efficacy of our proposed methodologies vis-à-vis the most pervasive and prevalent classification models currently in use. These models encompassed not only conventional machine learning techniques, such as Multiple Linear Regression, Support Vector Regression (SVR), Random Forest Regression, and Decision Trees, but also advanced and state-of-the-art deep learning models such as Residual Neural Network-152 (ResNet), Visual Geometry Group (VGG), and EfficienteNet-B7. Our findings evince that the suggested approach, PS-CS, in conjunction with CNN model, outperformed all other models, yielding an unparalleled accuracy score of 99.87%. By comparison, other models, including Multiple Linear Regression, SVR, Random Forest Regression, and Decision Trees, exhibited markedly inferior accuracy scores ranging from 80% to 95%. It is therefore evident that our proposed methodology, when integrated with a deep learning model, proves to be an exceedingly efficacious and robust solution for classification tasks.
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References
Ab Hamid, TMT, et al. (2021) Ensemble based filter feature selection with harmonize particle swarm optimization and support vector machine for optimal cancer classification. 5: p. 100054
Acion L, Kelmansky D, van der Laan M, Sahker E, Jones D, Arndt SJPO (2017) Use of a machine learning framework to predict substance use disorder treatment success. PLoS One 12(4):e0175383
Awal MA, Rabbi J, Hossain SI, Hashem M (2016) "Using linear regression to forecast future trends in crime of Bangladesh," In 2016 5th international conference on informatics, electronics and vision (ICIEV), pp. 333–338: IEEE
Aziz RM (2022) Cuckoo search-based optimization for cancer classification: a new hybrid approach. J Comput Biol 29(6):565–584
Aziz R, Verma CK, Srivastava N (2017) A novel approach for dimension reduction of microarray. Comput Biol Chem 1(71):161–169
Aziz RM, Baluch MF, Patel S, Ganie AH (2022) LGBM: a machine learning approach for Ethereum fraud detection. Int J Inf Technol 14(7):3321–3331
Aziz RM, Baluch MF, Patel S, Kumar P (2022) A machine learning based approach to detect the Ethereum fraud transactions with limited attributes. Karbala Int J Mod Sci 8:139–151
Aziz RM, Sharma P, Hussain A (2022) Machine learning algorithms for crime prediction under Indian penal code. Annals Data Sci 6:1–32
Aziz RM, Hussain A, Sharma P, Kumar P (2022) Machine learning-based soft computing regression analysis approach for crime data prediction. Karbala Int J Mod Sci 8(1):1–9
Aziz RM, Joshi AA, Kumar K, Gaani AH (2023) Hybrid feature selection techniques utilizing soft computing methods for cancer data. In: Computational and analytic methods in biological sciences. River Publishers, pp 23–39
Aziz RM, Desai NP, Baluch MF (2023) Computer vision model with novel cuckoo search based deep learning approach for classification of fish image. Multimed Tools Appl 82(3):3677–3696
Aziz RM, Mahto R, Das A, Ahmed SU, Roy P, Mallik S, Li A (2023) CO-WOA: novel optimization approach for deep learning classification of fish image. Chem Biodivers 2:e202201123
Aziz RM, Mahto R, Goel K, Das A, Kumar P, Saxena A (2023) Modified genetic algorithm with deep learning for fraud transactions of Ethereum smart contract. Appl Sci 13(2):697
Buonanno P, Montolio D (2008) Identifying the socio-economic and demographic determinants of crime across Spanish provinces. Int Rev Law Econ 28(2):89–97
Das P, Das AK (2019) "Application of classification techniques for prediction and analysis of crime in India," in Computational intelligence in data mining: Springer, pp. 191–201
Desai NP, Baluch MF, Makrariya A, MusheerAziz R (2022) Image processing model with deep learning approach for fish species classification. Turk J Comput Math Educ (TURCOMAT) 13(1):85–99
Dutt A (2018) Locating patriarchy in violence against women in India: social, legal and alternative responses. PEOPLE: Int J Soc Sci 4(2):212–228
Gacharich N (2021) "Deep learning for crime prediction," Florida Atlantic University, Doctoral dissertation, Florida Atlantic University
Gupta M, Chandra B, Gupta MP (2014) A framework of intelligent decision support system for Indian police. J Enterp Inf Manag
Gupta M, Chandra B, Gupta MP (2014) A framework of intelligent decision support system for Indian police. J Enterp Inf Manag. 27(5):512-40
Hann C (2009) The theft of anthropology. Theory Cult Soc 26(7–8):126–147
Harb A, Kassem H, Ghorayeb K (2020) Black hole particle swarm optimization for well placement optimization. Comput Geosci 24:1979–2000
Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Futur Gener Comput Syst 1(81):307–313
Hassan SU, Shabbir M, Iqbal S, Said A, Kamiran F, Nawaz R, Saif U (2021) Leveraging deep learning and SNA approaches for smart city policing in the developing world. Int J Inf Manag 1(56):102045
Hatcher WG, Yu WJIA (2018) A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6:24411–24432
Heeramun R, Magnusson C, Gumpert CH, Granath S, Lundberg M, Dalman C, Rai D (2017) Autism and convictions for violent crimes: population-based cohort study in Sweden. J Am Acad Child Adolesc Psychiatry 56(6):491–497
Hossain S, Abtahee A, Kashem I, Hoque MM, Sarker IH (2020) "Crime Prediction Using Spatio-Temporal Data," in International Conference on Computing Science, Communication and Security, pp. 277–289: Springer
Jawad K, Mahto R, Das A, Ahmed SU, Aziz RM, Kumar P (2023) Novel cuckoo search-based metaheuristic approach for deep learning prediction of depression. Appl Sci 13(9):5322
Jenga K, Catal C, Kar G (2023) Machine learning in crime prediction. J Ambient Intell Humaniz Comput 14(3):2887–2913
Keyvanpour MR, Javideh M, Ebrahimi MR (2011) Detecting and investigating crime by means of data mining: a general crime matching framework. Procedia Comput Sci 1(3):872–880
Khiralla F, Mabrouk A (2020) Statistics of cybercrime from 2016 to the first half of 2020. Int J Comput Sci Netw 9(5):252–261
Kulsudjarit K (2004) Drug problem in southeast and Southwest Asia. Ann N Y Acad Sci 1025(1):446–457
Kumar A, Purohit K, Kumar K (2021) Stock price prediction using recurrent neural network and long short-term memory. In conference proceedings of ICDLAIR2019 2021 (pp. 153-160). Springer International Publishing
Kumar JR, Dhabliya D, Dari SS (2023) A comparative study of machine learning algorithms for image recognition in privacy protection and crime detection. Int J Intell Syst Appl Eng 11(9s):482–490
Kumar K, Kurhekar M (2017) Sentimentalizer: Docker container utility over cloud. In2017 ninth international conference on advances in pattern recognition (ICAPR) 2017 Dec 27 (pp. 1-6). IEEE
Kumar S, Kumar K (2018) Irsc: integrated automated review mining system using virtual machines in cloud environment. In 2018 conference on information and communication technology (CICT) 2018 Oct 26 (pp. 1-6). IEEE
Kumari S, Singh M, Kumar K (2021) Prediction of liver disease using grouping of machine learning classifiers. InConference proceedings of ICDLAIR2019 2021 (pp. 339-349). Springer International Publishing
McDermott RC, Kilmartin C, McKelvey DK, Kridel MM (2015) College male sexual assault of women and the psychology of men: past, present, and future directions for research. Psychol Men Masculinity 16(4):355
Misra, S (2021) "The police system in India." Global perspectives in policing and law enforcement : 171, Books
Mittal M, Goyal LM, Sethi JK, Jude Hemanth D (2019) Monitoring the impact of economic crisis on crime in India using machine learning. Comput Econ 53(4):1467–1485
Morewitz S (2019) Kidnapping and violence: new research and clinical perspectives. Springer
Rodrigues A, González JA, Mateu J (2023) A conditional machine learning classification approach for spatio-temporal risk assessment of crime data. Stoch Env Res Risk A 15:1–4
Safat W, Asghar S, Gillani SA (2021) Empirical analysis for crime prediction and forecasting using machine learning and deep learning techniques. IEEE Access 6(9):70080–70094
Saravanan P, Selvaprabu J, Arun Raj L, Abdul Azeez Khan A, Javubar Sathick K (2021) Survey on crime analysis and prediction using data mining and machine learning techniques. In: Advances in smart grid technology: select proceedings of PECCON 2019— vol II, Springer, Singapore, pp 435–448
Shermila AM, Bellarmine AB, Santiago N (2018) "Crime data analysis and prediction of perpetrator identity using machine learning approach," In 2018 2nd international conference on trends in electronics and informatics (ICOEI), pp. 107–114: IEEE
Tayal DK, Jain A, Arora S, Agarwal S, Gupta T, Tyagi N (2015) Crime detection and criminal identification in India using data mining techniques. AI & Soc 30(1):117–127
ToppiReddy HK, Saini B, Mahajan G. Crime prediction & monitoring framework based on spatial analysis. Procedia computer science. 2018 Jan 1;132:696-705.
ToppiReddy HK, Saini B, Mahajan G (2018) Crime prediction & monitoring framework based on spatial analysis. Procedia Comput Sci 1(132):696–705
van Dijk A, Wolswijk H (2017) Criminal liability for serious traffic offences: essays on causing death, injury and danger in traffic. Eleven International Publishing
Venter G, Sobieszczanski-Sobieski J (2003) Particle swarm optimization. AIAA J 41(8):1583–1589
Vijayvergia A, Kumar K (2018) STAR: rating of reviewS by exploiting variation in emoTions using trAnsfer leaRning framework. In2018 conference on information and communication technology (CICT) 2018 Oct 26 (pp. 1-6). IEEE
Vijayvergia A, Kumar K (2021) Selective shallow models strength integration for emotion detection using GloVe and LSTM. Multimed Tools Appl 80(18):28349–28363
Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22:387–408
Wang S, Cao J, Philip SY (2020) Deep learning for spatio-temporal data mining: a survey. IEEE Trans Knowl Data Eng 34(8):3681–3700
Wheeler AP, Steenbeek W (2021) Mapping the risk terrain for crime using machine learning. J Quant Criminol 37:445–480
Yadav S, Timbadia M, Yadav A, Vishwakarma R, Yadav N (2017) Crime pattern detection, analysis & prediction. In: In 2017 international conference of electronics, communication and aerospace technology (ICECA), vol 1. IEEE, pp 225–230
Yaqoob A, Aziz RM, Verma NK, Lalwani P, Makrariya A, Kumar P (2023) A review on nature-inspired algorithms for Cancer disease prediction and classification. Math 11(5):1081
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Aziz, R.M., Hussain, A. & Sharma, P. Cognizable crime rate prediction and analysis under Indian penal code using deep learning with novel optimization approach. Multimed Tools Appl 83, 22663–22700 (2024). https://doi.org/10.1007/s11042-023-16371-0
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DOI: https://doi.org/10.1007/s11042-023-16371-0