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If we can devise methods for accurately predicting crime before it occurs or create a \"machine\" that can aid police officers, it will relieve the pressure on law enforcement and help to reduce crime. Machine learning (ML) and computer vision techniques should be used to accomplish this. A few examples are presented in this work, and their outcomes compelled us to conduct further research in this area. As a result of these statistical insights, crime detection and prevention strategies have changed significantly. Using a mix of machine learning and computer vision, law enforcement agencies or authorities can detect, prevent, and solve crimes at a much faster and more accurate rate, according to the goal of this study. 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Augment Hum Res 5(1):14.https://doi.org/10.1007/s41133-020-00033-z" }, { "path": "references.1.doi", "from": null, "to": "10.1007/s13202-020-00889-2" }, { "path": "references.1.key", "from": null, "to": "ref2" }, { "path": "references.1.unstructured", "from": null, "to": "Patel H, Prajapati D, Mahida D, Shah M (2020) Transforming petroleumdownstream sector through big data: a holistic review. J Pet Explor ProdTechnol 10(6):2601–2611.https://doi.org/10.1007/s13202-020-00889-2" }, { "path": "references.10.doi", "from": null, "to": "10.1007/s41133-019-0021-6" }, { "path": "references.10.key", "from": null, "to": "ref11" }, { "path": "references.10.unstructured", "from": null, "to": "Pandya R, Nadiadwala S, Shah R, Shah M (2020) Buildout of methodologyfor meticulous diagnosis of K-complex in EEG for aiding the detection ofAlzheimer's by artificial intelligence. Augment Hum Res 5(1):3.https://doi.org/10.1007/s41133-019-0021-6" }, { "path": "references.11.key", "from": null, "to": "ref12" }, { "path": "references.11.unstructured", "from": null, "to": "Dey A (2016) Machine learning algorithms: a review. Int J Comput Sci InfTechnol 7(3):1174–1179" }, { "path": "references.12.doi", "from": null, "to": "10.1007/s41133-020-00035-x" }, { "path": "references.12.key", "from": null, "to": "ref13" }, { "path": "references.12.unstructured", "from": null, "to": "Sukhadia A, Upadhyay K, Gundeti M, Shah S, Shah M (2020) Optimization ofsmart traffic governance system using artificial intelligence. Augment HumRes 5(1):13.https://doi.org/10.1007/s41133-020-00035-x" }, { "path": "references.13.doi", "from": null, "to": "10.1109/comst.2018.2880039" }, { "path": "references.13.key", "from": null, "to": "ref14" }, { "path": "references.13.unstructured", "from": null, "to": "Musumeci F, Rottondi C, Nag A, Macaluso I, Zibar D, Ruffini M et al (2019)An overview on application of machine learning techniques in opticalnetworks. IEEE Commun Surv Tutorials 21(2):1381–1408.https://doi.org/10.1109/COMST.2018.2880039" }, { "path": "references.14.doi", "from": null, "to": "10.1007/s41133-019-0024-3" }, { "path": "references.14.key", "from": null, "to": "ref15" }, { "path": "references.14.unstructured", "from": null, "to": "Patel D, Shah Y, Thakkar N, Shah K, Shah M (2020) Implementation ofartificial intelligence techniques for cancer detection. Augment Hum Res5(1):6.https://doi.org/10.1007/s41133-019-0024- 3" }, { "path": "references.15.doi", "from": null, "to": "10.1007/s41133-019-0029-y" }, { "path": "references.15.key", "from": null, "to": "ref16" }, { "path": "references.15.unstructured", "from": null, "to": "Kundalia K, Patel Y, Shah M (2020) Multi-label movie genre detection from amovie poster using knowledge transfer learning. Augment Hum Res 5(1):11.https://doi.org/10.1007/s41133-019- 0029-y" }, { "path": "references.16.doi", "from": null, "to": "10.1007/s00362-012-0471-0" }, { "path": "references.16.key", "from": null, "to": "ref17" }, { "path": "references.16.unstructured", "from": null, "to": "Marsland S (2015) Machine learning: an algorithmic perspective. CRC Press,Boca Raton, pp 1–" }, { "path": "references.17.doi", "from": null, "to": "10.1201/b17476-1" }, { "path": "references.17.key", "from": null, "to": "ref18" }, { "path": "references.17.unstructured", "from": null, "to": "https://doi.org/10.1201/b17476-1" }, { "path": "references.18.doi", "from": null, "to": "10.1007/s42488-019-00016-9" }, { "path": "references.18.key", "from": null, "to": "ref19" }, { "path": "references.18.unstructured", "from": null, "to": "Jani K, Chaudhuri M, Patel H, Shah M (2020) Machine learning in films: anapproach towards automation in film censoring. J Data Inf Manag 2(1):55–64.https://doi.org/10.1007/s42488-019- 00016-9" }, { "path": "references.19.doi", "from": null, "to": "10.1007/s41133-019-0023-4" }, { "path": "references.19.key", "from": null, "to": "ref20" }, { "path": "references.19.unstructured", "from": null, "to": "Parekh V, Shah D, Shah M (2020) Fatigue detection using artificialintelligence framework. Augment Hum Res 5(1):5https://doi.org/10.1007/s41133-019-0023-4" }, { "path": "references.2.doi", "from": null, "to": "10.1007/978-1-84882-935-0_1" }, { "path": "references.2.key", "from": null, "to": "ref3" }, { "path": "references.2.unstructured", "from": null, "to": "Szeliski R (2010) Computer vision: algorithms and applications. Springer-Verlag, Berlin, pp 1–979" }, { "path": "references.20.doi", "from": null, "to": "10.1007/s41133-019-0030-5" }, { "path": "references.20.key", "from": null, "to": "ref21" }, { "path": "references.20.unstructured", "from": null, "to": "Gandhi M, Kamdar J, Shah M (2020) Preprocessing of non-symmetricalimages for edge detection. Augment Hum Res 5(1):10https://doi.org/10.1007/s41133-019-0030-5" }, { "path": "references.21.doi", "from": null, "to": "10.1007/s42488-020-00030-2" }, { "path": "references.21.key", "from": null, "to": "ref22" }, { "path": "references.21.unstructured", "from": null, "to": "Panchiwala S, Shah M (2020) A comprehensive study on critical securityissues and challenges of the IoT world. J Data Inf Manag 2(7):257–278.https://doi.org/10.1007/s42488-020-00030-2" }, { "path": "references.22.key", "from": null, "to": "ref23" }, { "path": "references.22.unstructured", "from": null, "to": "Simon A, Deo MS, Venkatesan S, Babu DR (2016) An overview of machinelearning and its applications. Int J Electr Sci Eng 1(1):22–24" }, { "path": "references.23.doi", "from": null, "to": "10.1186/s42492-020-00057-7" }, { "path": "references.23.key", "from": null, "to": "ref24" }, { "path": "references.23.unstructured", "from": null, "to": "Parekh P, Patel S, Patel N, Shah M (2020) Systematic review and meta-analysis of augmented reality in medicine, retail, and games. Vis ComputInd Biomed Art 3(1):21.https://doi.org/10.1186/s42492-020-00057-7" }, { "path": "references.24.doi", "from": null, "to": "10.1007/s41133-020-00032-0" }, { "path": "references.24.key", "from": null, "to": "ref25" }, { "path": "references.24.unstructured", "from": null, "to": "Shah K, Patel H, Sanghvi D, Shah M (2020) A comparative analysis of logisticregression, random forest and KNN models for the text classification.Augment Hum Res 5(1):12.https://doi.org/10.1007/s41133-020-00032-0" }, { "path": "references.25.doi", "from": null, "to": "10.1007/s40745-019-00239-y" }, { "path": "references.25.key", "from": null, "to": "ref26" }, { "path": "references.25.unstructured", "from": null, "to": "Patel D, Shah D, Shah M (2020) The intertwine of brain and body: aquantitative analysis on how big data influences the system of sports. AnnData Sci 7(1):1–16. https://doi.org/10.1007/s40745-019-00239-y" }, { "path": "references.26.doi", "from": null, "to": "10.1016/0885-064x(88)90019-2" }, { "path": "references.26.key", "from": null, "to": "ref27" }, { "path": "references.26.unstructured", "from": null, "to": "Judd S (1988) On the complexity of loading shallow neural networks. JComplex 4(3):177–192. https://doi.org/10.1016/0885-064X(88)90019-2" }, { "path": "references.27.doi", "from": null, "to": "10.1016/s0893-6080(05)80010-3" }, { "path": "references.27.key", "from": null, "to": "ref28" }, { "path": "references.27.unstructured", "from": null, "to": "Blum AL, Rivest RL (1992) Training a 3-node neural network is NP-complete.Neural Netw 5(1):117–127. https://doi.org/10.1016/S0893-6080(05)80010-3" }, { "path": "references.28.doi", "from": null, "to": "10.1186/s40854-020-00205-1" }, { "path": "references.28.key", "from": null, "to": "ref29" }, { "path": "references.28.unstructured", "from": null, "to": "Gupta A, Dengre V, Kheruwala HA, Shah M (2020) Comprehensive review oftext-mining applications in finance. Financ Innov 6(1):1–25. https://doi.org/10.1186/s40854-020-00205-1" }, { "path": "references.29.doi", "from": null, "to": "10.1007/s41133-020-00038-8" }, { "path": "references.29.key", "from": null, "to": "ref30" }, { "path": "references.29.unstructured", "from": null, "to": "Shah N, Engineer S, Bhagat N, Chauhan H, Shah M (2020) Research trendson the usage of machine learning and artificial intelligence in advertising.Augment Hum Res 5(1):19.https://doi.org/10.1007/s41133-020-00038-8" }, { "path": "references.3.doi", "from": null, "to": "10.1145/1873951.1874249" }, { "path": "references.3.key", "from": null, "to": "ref4" }, { "path": "references.3.unstructured", "from": null, "to": "Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library ofcomputer vision algorithms. Paper presented at the 18th ACM internationalconference on multimedia. ACM, Firenze.https://doi.org/10.1145/1873951.1874249" }, { "path": "references.30.doi", "from": null, "to": "10.1186/s42492-020-00062-w" }, { "path": "references.30.key", "from": null, "to": "ref31" }, { "path": "references.30.unstructured", "from": null, "to": "Naik B, Mehta A, Shah M (2020) Denouements of machine learning andmultimodal diagnostic classification of Alzheimer's disease. Vis Comput IndBiomed Art 3(1):26.https://doi.org/10.1186/s42492-020-00062-w" }, { "path": "references.31.doi", "from": null, "to": "10.1109/fskd.2008.222" }, { "path": "references.31.key", "from": null, "to": "ref32" }, { "path": "references.31.unstructured", "from": null, "to": "Chen P, Yuan HY, Shu XM (2008) Forecasting crime using the ARIMA model.In: Paper presented at the 5th international conference on fuzzy systemsand knowledge discovery. IEEE, Ji'nan 18-20 October 2008.https://doi.org/10.1109/FSKD.2008.222" }, { "path": "references.32.key", "from": null, "to": "ref33" }, { "path": "references.32.unstructured", "from": null, "to": "Rani A, Rajasree S (2014) Crime trend analysis and prediction usingmahanolobis distance and dynamic time warping technique. Int J ComputSci Inf Technol 5(3):4131–4135" }, { "path": "references.33.doi", "from": null, "to": "10.1016/s0169-2070(03)00089-x" }, { "path": "references.33.key", "from": null, "to": "ref34" }, { "path": "references.33.unstructured", "from": null, "to": "Gorr W, Harries R (2003) Introduction to crime forecasting. Int J Forecast19(4):551–555. https://doi.org/10.1016/S0169-2070(03)00089-X" }, { "path": "references.34.doi", "from": null, "to": "10.1016/j.apgeog.2017.06.011" }, { "path": "references.34.key", "from": null, "to": "ref35" }, { "path": "references.34.unstructured", "from": null, "to": "Rummens A, Hardyns W, Pauwels L (2017) The use of predictive analysis inspatiotemporal crime forecasting: building and testing a model in an urbancontext. Appl Geogr 86:255–261. https://doi.org/10.1016/j.apgeog.2017.06.011" }, { "path": "references.35.key", "from": null, "to": "ref36" }, { "path": "references.35.unstructured", "from": null, "to": "Bates A (2017) Stingray: a new frontier in police surveillance. Cato InstitutePolicy Analysis, No" }, { "path": "references.36.doi", "from": null, "to": "10.2139/ssrn.2924620" }, { "path": "references.36.key", "from": null, "to": "ref37" }, { "path": "references.36.unstructured", "from": null, "to": "Joh EE (2017) The undue influence of surveillance technology companieson policing. N Y Univ Law Rev 92:101–130. https://doi.org/10.2139/ssrn.2924620" }, { "path": "references.37.doi", "from": null, "to": "10.1177/0093854818771721" }, { "path": "references.37.key", "from": null, "to": "ref38" }, { "path": "references.37.unstructured", "from": null, "to": "Vredeveldt A, Kesteloo L, Van Koppen PJ (2018) Writing alone or together:police officers' collaborative reports of an incident. Crim Justice Behav 45(7):1071–1092.https://doi.org/10.1177/0093854818771721" }, { "path": "references.38.key", "from": null, "to": "ref39" }, { "path": "references.38.unstructured", "from": null, "to": "McNeal GS (2014) Drones and aerial surveillance: considerations forlegislators. In: Brookings Institution: The Robots Are Coming: The Project OnCivilian Robotics, November 2014, Pepperdine University Legal StudiesResearch Paper No. 2015/3" }, { "path": "references.39.key", "from": null, "to": "ref40" }, { "path": "references.39.unstructured", "from": null, "to": "Fatih T, Bekir C (2015) Police use of technology to fight against crime. EurSci J 11(10):286–296" }, { "path": "references.4.doi", "from": null, "to": "10.1109/commantel.2013.6482417" }, { "path": "references.4.key", "from": null, "to": "ref5" }, { "path": "references.4.unstructured", "from": null, "to": "Le TL, Nguyen MQ, Nguyen TTM (2013) Human posture recognition usinghuman skeleton provided by Kinect. In: Paper presented at the 2013international conference on computing, management andtelecommunications. IEEE, Ho Chi Minh City.https://doi.org/10.1109/ComManTel.2013.6482417" }, { "path": "references.40.key", "from": null, "to": "ref41" }, { "path": "references.40.unstructured", "from": null, "to": "Katz CM, Choate DE, Ready JR, Nuňo L (2014) Evaluating the impact ofofficer worn body cameras in the Phoenix Police Department. Center forViolence Prevention & Community Safety, Arizona State University, Phoenix,pp 1–43" }, { "path": "references.41.key", "from": null, "to": "ref42" }, { "path": "references.41.unstructured", "from": null, "to": "Stanley J (2015) Police body-mounted cameras: with right policies in place,a win for all.https://www.aclu.org/police-body-mounted-cameras-right-policies-place-win-all. Accessed 15 Aug 2015" }, { "path": "references.42.doi", "from": null, "to": "10.5121/mlaij.2015.2101" }, { "path": "references.42.key", "from": null, "to": "ref43" }, { "path": "references.42.unstructured", "from": null, "to": "McClendon L, Meghanathan N (2015) Using machine learning algorithms toanalyze crime data. Mach Lear Appl Int J 2(1):1–12. https://doi.org/10.5121/mlaij.2015.2101" }, { "path": "references.43.doi", "from": null, "to": "10.1093/bioinformatics/bth261" }, { "path": "references.43.key", "from": null, "to": "ref44" }, { "path": "references.43.unstructured", "from": null, "to": "Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining inbioinformatics using Weka. Bioinformatics 20(15):2479–2481.https://doi.org/10.1093/bioinformatics/bth261" }, { "path": "references.44.doi", "from": null, "to": "10.1109/iemcon.2018.8614828" }, { "path": "references.44.key", "from": null, "to": "ref45" }, { "path": "references.44.unstructured", "from": null, "to": "Kim S, Joshi P, Kalsi PS, Taheri P (2018) Crime analysis through machinelearning. In: Paper presented at the IEEE 9th annual information technology,Shahet al. Visual Computing for Industry, Biomedicine, and Art (2021) 4:9 Page 13 of 14 electronics and mobile communication conference. IEEE, Vancouver 1-3November 2018.https://doi.org/10.1109/IEMCON.2018.8614828" }, { "path": "references.45.key", "from": null, "to": "ref46" }, { "path": "references.45.unstructured", "from": null, "to": "Tabedzki C, Thirumalaiswamy A, van Vliet P (2018) Yo home to Bel-Air:predicting crime on the streets of Philadelphia. In: University ofPennsylvania, CIS 520: machine learning" }, { "path": "references.46.key", "from": null, "to": "ref47" }, { "path": "references.46.unstructured", "from": null, "to": "Bharati A, Sarvanaguru RAK (2018) Crime prediction and analysis usingmachine learning. Int Res J Eng Technol 5(9):1037–1042" }, { "path": "references.47.key", "from": null, "to": "ref48" }, { "path": "references.47.unstructured", "from": null, "to": "Prithi S, Aravindan S, Anusuya E, Kumar AM (2020) GUI based prediction ofcrime rate using machine learning approach. Int J Comput Sci Mob Comput9(3):221–229" }, { "path": "references.48.doi", "from": null, "to": "10.1371/journal.pone.0176244" }, { "path": "references.48.key", "from": null, "to": "ref49" }, { "path": "references.48.unstructured", "from": null, "to": "Kang HW, Kang HB (2017) Prediction of crime occurrence from multi-modaldata using deep learning. PLoS One 12(4):e0176244.https://doi.org/10.1371/journal.pone.0176244" }, { "path": "references.49.doi", "from": null, "to": "10.1016/j.procs.2020.05.018" }, { "path": "references.49.key", "from": null, "to": "ref50" }, { "path": "references.49.unstructured", "from": null, "to": "Bandekar SR, Vijayalakshmi C (2020) Design and analysis of machinelearning algorithms for the reduction of crime rates in India. ProcediaComput Sci 172:122–127. https://doi.org/10.1016/j.procs.2020.05.018" }, { "path": "references.5.doi", "from": null, "to": "10.1007/s41133-019-0025-2" }, { "path": "references.5.key", "from": null, "to": "ref6" }, { "path": "references.5.unstructured", "from": null, "to": "Ahir K, Govani K, Gajera R, Shah M (2020) Application on virtual reality forenhanced education learning, military training and sports. Augment HumRes 5(1):7. (https://doi.org/10.1007/s41133- 019-0025-2)" }, { "path": "references.50.doi", "from": null, "to": "10.1007/978-981-15-6648-6_22" }, { "path": "references.50.key", "from": null, "to": "ref51" }, { "path": "references.50.unstructured", "from": null, "to": "Hossain S, Abtahee A, Kashem I, Hoque M, Sarker IH (2020) Crime predictionusing spatiotemporal data. arXiv preprint arXiv:2003.09322.https://doi.org/10.1007/978-981-15-6648-6_22" }, { "path": "references.51.doi", "from": null, "to": "10.3390/forecast3040046" }, { "path": "references.51.key", "from": null, "to": "ref52" }, { "path": "references.51.unstructured", "from": null, "to": "Stalidis P, Semertzidis T, Daras P (2018) Examining deep learningarchitectures for crime classification and prediction. arXiv preprint arXiv:1812.00602. p. 1–13" }, { "path": "references.52.key", "from": null, "to": "ref53" }, { "path": "references.52.unstructured", "from": null, "to": "Jha P, Jha R, Sharma A (2019) Behavior analysis and crime prediction usingbig data and machine learning. Int J Recent Technol Eng 8(1):461–468" }, { "path": "references.53.doi", "from": null, "to": "10.29121/ijetmr.v5.i2.2018.615" }, { "path": "references.53.key", "from": null, "to": "ref54" }, { "path": "references.53.unstructured", "from": null, "to": "Tyagi D, Sharma S (2018) An approach to crime data analysis: a systematicreview. Int J Eng Technol Manag Res 5(2):67–74. https://doi.org/10.29121/ijetmr.v5.i2.2018.615" }, { "path": "references.54.doi", "from": null, "to": "10.3390/ijgi7080298" }, { "path": "references.54.key", "from": null, "to": "ref55" }, { "path": "references.54.unstructured", "from": null, "to": "Lin YL, Yen MF, Yu LC (2018) Grid-based crime prediction usinggeographical features. ISPRS Int J Geo-Inf 7(8):298.https://doi.org/10.3390/ijgi7080298" }, { "path": "references.55.key", "from": null, "to": "ref56" }, { "path": "references.55.unstructured", "from": null, "to": "Ahishakiye E, Taremwa D, Omulo EO, Niyonzima I (2017) Crime predictionusing decision tree (J48) classification algorithm. Int J Comput Inf Technol6(3):188–195" }, { "path": "references.56.doi", "from": null, "to": "10.14400/jdc.2014.12.8.321" }, { "path": "references.56.key", "from": null, "to": "ref57" }, { "path": "references.56.unstructured", "from": null, "to": "Sun CC, Yao CL, Li X, Lee K (2014) Detecting crime types using classificationalgorithms. J Digit Inf Manag 12(8):321–327. https://doi.org/10.14400/JDC.2014.12.8.321" }, { "path": "references.57.key", "from": null, "to": "ref58" }, { "path": "references.57.unstructured", "from": null, "to": "Shojaee S, Mustapha A, Sidi F, Jabar MA (2013) A study on classificationlearning algorithms to predict crime status. Int J Digital Content TechnolAppl 7(9):361–369" }, { "path": "references.58.doi", "from": null, "to": "10.14569/ijarai.2015.041207" }, { "path": "references.58.key", "from": null, "to": "ref59" }, { "path": "references.58.unstructured", "from": null, "to": "Obuandike GN, Isah A, Alhasan J (2015) Analytical study of some selectedclassification algorithms in WEKA using real crime data. Int J Adv Res ArtifIntell 4(12):44–48. https://doi.org/10.14569/IJARAI.2015.041207" }, { "path": "references.59.doi", "from": null, "to": "10.17485/ijst/2013/v6i3.6" }, { "path": "references.59.key", "from": null, "to": "ref60" }, { "path": "references.59.unstructured", "from": null, "to": "Iqbal R, Murad MAA, Mustapha A, Panahy PHS, Khanahmadliravi N (2013) Anexperimental study of classification algorithms for crime prediction. Indian JSci Technol 6(3):4219–4225.https://doi.org/10.17485/ijst/2013/v6i3.6" }, { "path": "references.6.doi", "from": null, "to": "10.1016/j.aiia.2020.04.002" }, { "path": "references.6.key", "from": null, "to": "ref7" }, { "path": "references.6.unstructured", "from": null, "to": "Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation ofartificial intelligence in agriculture for optimisation of irrigation andapplication of pesticides and herbicides. Artif Intell Agric 4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002" }, { "path": "references.60.key", "from": null, "to": "ref61" }, { "path": "references.60.unstructured", "from": null, "to": "Jangra M, Kalsi S (2019) Crime analysis for multistate network using naiveBayes classifier. Int J Comput Sci Mob Comput 8(6):134–143" }, { "path": "references.61.doi", "from": null, "to": "10.1088/1742-6596/1450/1/012076" }, { "path": "references.61.key", "from": null, "to": "ref62" }, { "path": "references.61.unstructured", "from": null, "to": "Wibowo AH, Oesman TI (2020) The comparative analysis on the accuracy ofk-NN, naive Bayes, and decision tree algorithms in predicting crimes andcriminal actions in Sleman regency. J Phys Conf Ser 1450:012076.https://doi.org/10.1088/1742-6596/1450/1/012076" }, { "path": "references.62.doi", "from": null, "to": "10.1007/978-3-319-59421-7_24" }, { "path": "references.62.key", "from": null, "to": "ref63" }, { "path": "references.62.unstructured", "from": null, "to": "Vanhoenshoven F, Nápoles G, Bielen S, Vanhoof K (2017) Fuzzy cognitivemaps employing ARIMA components for time series forecasting. In:Czarnowski I, Howlett RJ, Jain LC (eds) Proceedings of the 9th KESinternational conference on intelligent decision technologies 2017, vol 72.Springer, Heidelberg, pp 255–264. https://doi.org/10.1007/978-3-319-59421-7_24" }, { "path": "references.7.doi", "from": null, "to": "10.1016/j.aiia.2019.05.004" }, { "path": "references.7.key", "from": null, "to": "ref8" }, { "path": "references.7.unstructured", "from": null, "to": "Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review onautomation in agriculture using artificial intelligence. Artif Intell Agric 2:1–12.https://doi.org/10.1016/j.aiia.2019.05.004" }, { "path": "references.8.doi", "from": null, "to": "10.1007/s41939-019-00049-y" }, { "path": "references.8.key", "from": null, "to": "ref9" }, { "path": "references.8.unstructured", "from": null, "to": "Kakkad V, Patel M, Shah M (2019) Biometric authentication and imageencryption for image security in cloud framework. Multiscale MultidiscipModel Exp Des 2(4):233–248. https://doi.org/10.1007/s41939-019-00049-y" }, { "path": "references.9.doi", "from": null, "to": "10.1016/j.aiia.2020.06.001" }, { "path": "references.9.key", "from": null, "to": "ref10" }, { "path": "references.9.unstructured", "from": null, "to": "Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition forapplications in smart agriculture: a comprehensive review. Artif Intell Agric4:81–95. https://doi.org/10.1016/j.aiia.2020.06.001" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "Efficient Neural Network model for Cyber Crime Foreca" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2022.v7.i02.pp355-367", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Efficient Neural Network model for Cyber Crime Foreca", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2022.v7.i02.pp355-367" } ], "agents": [ { "role": "author", "name": { "given": "Rajendar.T", "family": "Rajendar.T" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2022-07-03", "date_type": "PublicationDate", "online": "2022-07-03" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "7", "issue": "2", "pages": { "first": "355", "last": "367" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/533", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/533/476", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/533/2171", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/rajendher%20(1).pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "Depending on the gravity of the offence, a person who commits a crime faces legal repercussions from the state or other authorities, including jail time and fines. There has been an alarming rise in the number and variety of criminal acts, necessitating the creation by government agencies of effective techniques for taking preventive actions. Traditional crime-solving methods aren't cutting it in today's environment of rising crime since they're too slow and inefficient. If we can devise methods for accurately predicting crime before it occurs or create a \"machine\" that can aid police officers, it will relieve the pressure on law enforcement and help to reduce crime. Machine learning (ML) and computer vision techniques should be used to accomplish this. A few examples are presented in this work, and their outcomes compelled us to conduct further research in this area. As a result of these statistical insights, crime detection and prevention strategies have changed significantly. Using a mix of machine learning and computer vision, law enforcement agencies or authorities can detect, prevent, and solve crimes at a much faster and more accurate rate, according to the goal of this study. In a nutshell, machine learning and computer vision can revolutionise law enforcement", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2022-07-03", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1007/s41133-020-00033-z", "unstructured": "Shah D, Dixit R, Shah A, Shah P, Shah M (2020) A comprehensive analysisregarding several breakthroughs based on computer intelligence targetingvarious syndromes. Augment Hum Res 5(1):14.https://doi.org/10.1007/s41133-020-00033-z" }, { "key": "ref2", "doi": "10.1007/s13202-020-00889-2", "unstructured": "Patel H, Prajapati D, Mahida D, Shah M (2020) Transforming petroleumdownstream sector through big data: a holistic review. J Pet Explor ProdTechnol 10(6):2601–2611.https://doi.org/10.1007/s13202-020-00889-2" }, { "key": "ref3", "doi": "10.1007/978-1-84882-935-0_1", "unstructured": "Szeliski R (2010) Computer vision: algorithms and applications. Springer-Verlag, Berlin, pp 1–979" }, { "key": "ref4", "doi": "10.1145/1873951.1874249", "unstructured": "Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library ofcomputer vision algorithms. Paper presented at the 18th ACM internationalconference on multimedia. ACM, Firenze.https://doi.org/10.1145/1873951.1874249" }, { "key": "ref5", "doi": "10.1109/commantel.2013.6482417", "unstructured": "Le TL, Nguyen MQ, Nguyen TTM (2013) Human posture recognition usinghuman skeleton provided by Kinect. In: Paper presented at the 2013international conference on computing, management andtelecommunications. IEEE, Ho Chi Minh City.https://doi.org/10.1109/ComManTel.2013.6482417" }, { "key": "ref6", "doi": "10.1007/s41133-019-0025-2", "unstructured": "Ahir K, Govani K, Gajera R, Shah M (2020) Application on virtual reality forenhanced education learning, military training and sports. Augment HumRes 5(1):7. (https://doi.org/10.1007/s41133- 019-0025-2)" }, { "key": "ref7", "doi": "10.1016/j.aiia.2020.04.002", "unstructured": "Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation ofartificial intelligence in agriculture for optimisation of irrigation andapplication of pesticides and herbicides. Artif Intell Agric 4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002" }, { "key": "ref8", "doi": "10.1016/j.aiia.2019.05.004", "unstructured": "Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review onautomation in agriculture using artificial intelligence. Artif Intell Agric 2:1–12.https://doi.org/10.1016/j.aiia.2019.05.004" }, { "key": "ref9", "doi": "10.1007/s41939-019-00049-y", "unstructured": "Kakkad V, Patel M, Shah M (2019) Biometric authentication and imageencryption for image security in cloud framework. Multiscale MultidiscipModel Exp Des 2(4):233–248. https://doi.org/10.1007/s41939-019-00049-y" }, { "key": "ref10", "doi": "10.1016/j.aiia.2020.06.001", "unstructured": "Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition forapplications in smart agriculture: a comprehensive review. Artif Intell Agric4:81–95. https://doi.org/10.1016/j.aiia.2020.06.001" }, { "key": "ref11", "doi": "10.1007/s41133-019-0021-6", "unstructured": "Pandya R, Nadiadwala S, Shah R, Shah M (2020) Buildout of methodologyfor meticulous diagnosis of K-complex in EEG for aiding the detection ofAlzheimer's by artificial intelligence. Augment Hum Res 5(1):3.https://doi.org/10.1007/s41133-019-0021-6" }, { "key": "ref12", "unstructured": "Dey A (2016) Machine learning algorithms: a review. Int J Comput Sci InfTechnol 7(3):1174–1179" }, { "key": "ref13", "doi": "10.1007/s41133-020-00035-x", "unstructured": "Sukhadia A, Upadhyay K, Gundeti M, Shah S, Shah M (2020) Optimization ofsmart traffic governance system using artificial intelligence. Augment HumRes 5(1):13.https://doi.org/10.1007/s41133-020-00035-x" }, { "key": "ref14", "doi": "10.1109/comst.2018.2880039", "unstructured": "Musumeci F, Rottondi C, Nag A, Macaluso I, Zibar D, Ruffini M et al (2019)An overview on application of machine learning techniques in opticalnetworks. IEEE Commun Surv Tutorials 21(2):1381–1408.https://doi.org/10.1109/COMST.2018.2880039" }, { "key": "ref15", "doi": "10.1007/s41133-019-0024-3", "unstructured": "Patel D, Shah Y, Thakkar N, Shah K, Shah M (2020) Implementation ofartificial intelligence techniques for cancer detection. Augment Hum Res5(1):6.https://doi.org/10.1007/s41133-019-0024- 3" }, { "key": "ref16", "doi": "10.1007/s41133-019-0029-y", "unstructured": "Kundalia K, Patel Y, Shah M (2020) Multi-label movie genre detection from amovie poster using knowledge transfer learning. Augment Hum Res 5(1):11.https://doi.org/10.1007/s41133-019- 0029-y" }, { "key": "ref17", "doi": "10.1007/s00362-012-0471-0", "unstructured": "Marsland S (2015) Machine learning: an algorithmic perspective. CRC Press,Boca Raton, pp 1–" }, { "key": "ref18", "doi": "10.1201/b17476-1", "unstructured": "https://doi.org/10.1201/b17476-1" }, { "key": "ref19", "doi": "10.1007/s42488-019-00016-9", "unstructured": "Jani K, Chaudhuri M, Patel H, Shah M (2020) Machine learning in films: anapproach towards automation in film censoring. J Data Inf Manag 2(1):55–64.https://doi.org/10.1007/s42488-019- 00016-9" }, { "key": "ref20", "doi": "10.1007/s41133-019-0023-4", "unstructured": "Parekh V, Shah D, Shah M (2020) Fatigue detection using artificialintelligence framework. Augment Hum Res 5(1):5https://doi.org/10.1007/s41133-019-0023-4" }, { "key": "ref21", "doi": "10.1007/s41133-019-0030-5", "unstructured": "Gandhi M, Kamdar J, Shah M (2020) Preprocessing of non-symmetricalimages for edge detection. Augment Hum Res 5(1):10https://doi.org/10.1007/s41133-019-0030-5" }, { "key": "ref22", "doi": "10.1007/s42488-020-00030-2", "unstructured": "Panchiwala S, Shah M (2020) A comprehensive study on critical securityissues and challenges of the IoT world. J Data Inf Manag 2(7):257–278.https://doi.org/10.1007/s42488-020-00030-2" }, { "key": "ref23", "unstructured": "Simon A, Deo MS, Venkatesan S, Babu DR (2016) An overview of machinelearning and its applications. Int J Electr Sci Eng 1(1):22–24" }, { "key": "ref24", "doi": "10.1186/s42492-020-00057-7", "unstructured": "Parekh P, Patel S, Patel N, Shah M (2020) Systematic review and meta-analysis of augmented reality in medicine, retail, and games. Vis ComputInd Biomed Art 3(1):21.https://doi.org/10.1186/s42492-020-00057-7" }, { "key": "ref25", "doi": "10.1007/s41133-020-00032-0", "unstructured": "Shah K, Patel H, Sanghvi D, Shah M (2020) A comparative analysis of logisticregression, random forest and KNN models for the text classification.Augment Hum Res 5(1):12.https://doi.org/10.1007/s41133-020-00032-0" }, { "key": "ref26", "doi": "10.1007/s40745-019-00239-y", "unstructured": "Patel D, Shah D, Shah M (2020) The intertwine of brain and body: aquantitative analysis on how big data influences the system of sports. AnnData Sci 7(1):1–16. https://doi.org/10.1007/s40745-019-00239-y" }, { "key": "ref27", "doi": "10.1016/0885-064x(88)90019-2", "unstructured": "Judd S (1988) On the complexity of loading shallow neural networks. JComplex 4(3):177–192. https://doi.org/10.1016/0885-064X(88)90019-2" }, { "key": "ref28", "doi": "10.1016/s0893-6080(05)80010-3", "unstructured": "Blum AL, Rivest RL (1992) Training a 3-node neural network is NP-complete.Neural Netw 5(1):117–127. https://doi.org/10.1016/S0893-6080(05)80010-3" }, { "key": "ref29", "doi": "10.1186/s40854-020-00205-1", "unstructured": "Gupta A, Dengre V, Kheruwala HA, Shah M (2020) Comprehensive review oftext-mining applications in finance. Financ Innov 6(1):1–25. https://doi.org/10.1186/s40854-020-00205-1" }, { "key": "ref30", "doi": "10.1007/s41133-020-00038-8", "unstructured": "Shah N, Engineer S, Bhagat N, Chauhan H, Shah M (2020) Research trendson the usage of machine learning and artificial intelligence in advertising.Augment Hum Res 5(1):19.https://doi.org/10.1007/s41133-020-00038-8" }, { "key": "ref31", "doi": "10.1186/s42492-020-00062-w", "unstructured": "Naik B, Mehta A, Shah M (2020) Denouements of machine learning andmultimodal diagnostic classification of Alzheimer's disease. Vis Comput IndBiomed Art 3(1):26.https://doi.org/10.1186/s42492-020-00062-w" }, { "key": "ref32", "doi": "10.1109/fskd.2008.222", "unstructured": "Chen P, Yuan HY, Shu XM (2008) Forecasting crime using the ARIMA model.In: Paper presented at the 5th international conference on fuzzy systemsand knowledge discovery. IEEE, Ji'nan 18-20 October 2008.https://doi.org/10.1109/FSKD.2008.222" }, { "key": "ref33", "unstructured": "Rani A, Rajasree S (2014) Crime trend analysis and prediction usingmahanolobis distance and dynamic time warping technique. Int J ComputSci Inf Technol 5(3):4131–4135" }, { "key": "ref34", "doi": "10.1016/s0169-2070(03)00089-x", "unstructured": "Gorr W, Harries R (2003) Introduction to crime forecasting. Int J Forecast19(4):551–555. https://doi.org/10.1016/S0169-2070(03)00089-X" }, { "key": "ref35", "doi": "10.1016/j.apgeog.2017.06.011", "unstructured": "Rummens A, Hardyns W, Pauwels L (2017) The use of predictive analysis inspatiotemporal crime forecasting: building and testing a model in an urbancontext. Appl Geogr 86:255–261. https://doi.org/10.1016/j.apgeog.2017.06.011" }, { "key": "ref36", "unstructured": "Bates A (2017) Stingray: a new frontier in police surveillance. Cato InstitutePolicy Analysis, No" }, { "key": "ref37", "doi": "10.2139/ssrn.2924620", "unstructured": "Joh EE (2017) The undue influence of surveillance technology companieson policing. N Y Univ Law Rev 92:101–130. https://doi.org/10.2139/ssrn.2924620" }, { "key": "ref38", "doi": "10.1177/0093854818771721", "unstructured": "Vredeveldt A, Kesteloo L, Van Koppen PJ (2018) Writing alone or together:police officers' collaborative reports of an incident. Crim Justice Behav 45(7):1071–1092.https://doi.org/10.1177/0093854818771721" }, { "key": "ref39", "unstructured": "McNeal GS (2014) Drones and aerial surveillance: considerations forlegislators. In: Brookings Institution: The Robots Are Coming: The Project OnCivilian Robotics, November 2014, Pepperdine University Legal StudiesResearch Paper No. 2015/3" }, { "key": "ref40", "unstructured": "Fatih T, Bekir C (2015) Police use of technology to fight against crime. EurSci J 11(10):286–296" }, { "key": "ref41", "unstructured": "Katz CM, Choate DE, Ready JR, Nuňo L (2014) Evaluating the impact ofofficer worn body cameras in the Phoenix Police Department. Center forViolence Prevention & Community Safety, Arizona State University, Phoenix,pp 1–43" }, { "key": "ref42", "unstructured": "Stanley J (2015) Police body-mounted cameras: with right policies in place,a win for all.https://www.aclu.org/police-body-mounted-cameras-right-policies-place-win-all. Accessed 15 Aug 2015" }, { "key": "ref43", "doi": "10.5121/mlaij.2015.2101", "unstructured": "McClendon L, Meghanathan N (2015) Using machine learning algorithms toanalyze crime data. Mach Lear Appl Int J 2(1):1–12. https://doi.org/10.5121/mlaij.2015.2101" }, { "key": "ref44", "doi": "10.1093/bioinformatics/bth261", "unstructured": "Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining inbioinformatics using Weka. Bioinformatics 20(15):2479–2481.https://doi.org/10.1093/bioinformatics/bth261" }, { "key": "ref45", "doi": "10.1109/iemcon.2018.8614828", "unstructured": "Kim S, Joshi P, Kalsi PS, Taheri P (2018) Crime analysis through machinelearning. In: Paper presented at the IEEE 9th annual information technology,Shahet al. Visual Computing for Industry, Biomedicine, and Art (2021) 4:9 Page 13 of 14 electronics and mobile communication conference. IEEE, Vancouver 1-3November 2018.https://doi.org/10.1109/IEMCON.2018.8614828" }, { "key": "ref46", "unstructured": "Tabedzki C, Thirumalaiswamy A, van Vliet P (2018) Yo home to Bel-Air:predicting crime on the streets of Philadelphia. In: University ofPennsylvania, CIS 520: machine learning" }, { "key": "ref47", "unstructured": "Bharati A, Sarvanaguru RAK (2018) Crime prediction and analysis usingmachine learning. Int Res J Eng Technol 5(9):1037–1042" }, { "key": "ref48", "unstructured": "Prithi S, Aravindan S, Anusuya E, Kumar AM (2020) GUI based prediction ofcrime rate using machine learning approach. Int J Comput Sci Mob Comput9(3):221–229" }, { "key": "ref49", "doi": "10.1371/journal.pone.0176244", "unstructured": "Kang HW, Kang HB (2017) Prediction of crime occurrence from multi-modaldata using deep learning. PLoS One 12(4):e0176244.https://doi.org/10.1371/journal.pone.0176244" }, { "key": "ref50", "doi": "10.1016/j.procs.2020.05.018", "unstructured": "Bandekar SR, Vijayalakshmi C (2020) Design and analysis of machinelearning algorithms for the reduction of crime rates in India. ProcediaComput Sci 172:122–127. https://doi.org/10.1016/j.procs.2020.05.018" }, { "key": "ref51", "doi": "10.1007/978-981-15-6648-6_22", "unstructured": "Hossain S, Abtahee A, Kashem I, Hoque M, Sarker IH (2020) Crime predictionusing spatiotemporal data. arXiv preprint arXiv:2003.09322.https://doi.org/10.1007/978-981-15-6648-6_22" }, { "key": "ref52", "doi": "10.3390/forecast3040046", "unstructured": "Stalidis P, Semertzidis T, Daras P (2018) Examining deep learningarchitectures for crime classification and prediction. arXiv preprint arXiv:1812.00602. p. 1–13" }, { "key": "ref53", "unstructured": "Jha P, Jha R, Sharma A (2019) Behavior analysis and crime prediction usingbig data and machine learning. Int J Recent Technol Eng 8(1):461–468" }, { "key": "ref54", "doi": "10.29121/ijetmr.v5.i2.2018.615", "unstructured": "Tyagi D, Sharma S (2018) An approach to crime data analysis: a systematicreview. Int J Eng Technol Manag Res 5(2):67–74. https://doi.org/10.29121/ijetmr.v5.i2.2018.615" }, { "key": "ref55", "doi": "10.3390/ijgi7080298", "unstructured": "Lin YL, Yen MF, Yu LC (2018) Grid-based crime prediction usinggeographical features. ISPRS Int J Geo-Inf 7(8):298.https://doi.org/10.3390/ijgi7080298" }, { "key": "ref56", "unstructured": "Ahishakiye E, Taremwa D, Omulo EO, Niyonzima I (2017) Crime predictionusing decision tree (J48) classification algorithm. Int J Comput Inf Technol6(3):188–195" }, { "key": "ref57", "doi": "10.14400/jdc.2014.12.8.321", "unstructured": "Sun CC, Yao CL, Li X, Lee K (2014) Detecting crime types using classificationalgorithms. J Digit Inf Manag 12(8):321–327. https://doi.org/10.14400/JDC.2014.12.8.321" }, { "key": "ref58", "unstructured": "Shojaee S, Mustapha A, Sidi F, Jabar MA (2013) A study on classificationlearning algorithms to predict crime status. Int J Digital Content TechnolAppl 7(9):361–369" }, { "key": "ref59", "doi": "10.14569/ijarai.2015.041207", "unstructured": "Obuandike GN, Isah A, Alhasan J (2015) Analytical study of some selectedclassification algorithms in WEKA using real crime data. Int J Adv Res ArtifIntell 4(12):44–48. https://doi.org/10.14569/IJARAI.2015.041207" }, { "key": "ref60", "doi": "10.17485/ijst/2013/v6i3.6", "unstructured": "Iqbal R, Murad MAA, Mustapha A, Panahy PHS, Khanahmadliravi N (2013) Anexperimental study of classification algorithms for crime prediction. Indian JSci Technol 6(3):4219–4225.https://doi.org/10.17485/ijst/2013/v6i3.6" }, { "key": "ref61", "unstructured": "Jangra M, Kalsi S (2019) Crime analysis for multistate network using naiveBayes classifier. Int J Comput Sci Mob Comput 8(6):134–143" }, { "key": "ref62", "doi": "10.1088/1742-6596/1450/1/012076", "unstructured": "Wibowo AH, Oesman TI (2020) The comparative analysis on the accuracy ofk-NN, naive Bayes, and decision tree algorithms in predicting crimes andcriminal actions in Sleman regency. J Phys Conf Ser 1450:012076.https://doi.org/10.1088/1742-6596/1450/1/012076" }, { "key": "ref63", "doi": "10.1007/978-3-319-59421-7_24", "unstructured": "Vanhoenshoven F, Nápoles G, Bielen S, Vanhoof K (2017) Fuzzy cognitivemaps employing ARIMA components for time series forecasting. In:Czarnowski I, Howlett RJ, Jain LC (eds) Proceedings of the 9th KESinternational conference on intelligent decision technologies 2017, vol 72.Springer, Heidelberg, pp 255–264. https://doi.org/10.1007/978-3-319-59421-7_24" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/533", "sha256_before": null, "sha256_after": "debf88aa7e752726cc3ce6a4b3c8d1e5f94950499dc2d4ebf686eeab218fcd3b" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:46", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2022.v7.i02.pp355-367", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Efficient Neural Network model for Cyber Crime Foreca", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2022.v7.i02.pp355-367" } ], "agents": [ { "role": "author", "name": { "given": "Rajendar.T", "family": "Rajendar.T" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2022-07-03", "date_type": "PublicationDate", "online": "2022-07-03" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "7", "issue": "2", "pages": { "first": "355", "last": "367" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/533", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/533/476", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/533/2171", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/rajendher%20(1).pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "Depending on the gravity of the offence, a person who commits a crime faces legal repercussions from the state or other authorities, including jail time and fines. There has been an alarming rise in the number and variety of criminal acts, necessitating the creation by government agencies of effective techniques for taking preventive actions. Traditional crime-solving methods aren't cutting it in today's environment of rising crime since they're too slow and inefficient. If we can devise methods for accurately predicting crime before it occurs or create a \"machine\" that can aid police officers, it will relieve the pressure on law enforcement and help to reduce crime. Machine learning (ML) and computer vision techniques should be used to accomplish this. A few examples are presented in this work, and their outcomes compelled us to conduct further research in this area. As a result of these statistical insights, crime detection and prevention strategies have changed significantly. Using a mix of machine learning and computer vision, law enforcement agencies or authorities can detect, prevent, and solve crimes at a much faster and more accurate rate, according to the goal of this study. In a nutshell, machine learning and computer vision can revolutionise law enforcement", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2022-07-03", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1007/s41133-020-00033-z", "unstructured": "Shah D, Dixit R, Shah A, Shah P, Shah M (2020) A comprehensive analysisregarding several breakthroughs based on computer intelligence targetingvarious syndromes. Augment Hum Res 5(1):14.https://doi.org/10.1007/s41133-020-00033-z" }, { "key": "ref2", "doi": "10.1007/s13202-020-00889-2", "unstructured": "Patel H, Prajapati D, Mahida D, Shah M (2020) Transforming petroleumdownstream sector through big data: a holistic review. J Pet Explor ProdTechnol 10(6):2601–2611.https://doi.org/10.1007/s13202-020-00889-2" }, { "key": "ref3", "doi": "10.1007/978-1-84882-935-0_1", "unstructured": "Szeliski R (2010) Computer vision: algorithms and applications. Springer-Verlag, Berlin, pp 1–979" }, { "key": "ref4", "doi": "10.1145/1873951.1874249", "unstructured": "Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library ofcomputer vision algorithms. Paper presented at the 18th ACM internationalconference on multimedia. ACM, Firenze.https://doi.org/10.1145/1873951.1874249" }, { "key": "ref5", "doi": "10.1109/commantel.2013.6482417", "unstructured": "Le TL, Nguyen MQ, Nguyen TTM (2013) Human posture recognition usinghuman skeleton provided by Kinect. In: Paper presented at the 2013international conference on computing, management andtelecommunications. IEEE, Ho Chi Minh City.https://doi.org/10.1109/ComManTel.2013.6482417" }, { "key": "ref6", "doi": "10.1007/s41133-019-0025-2", "unstructured": "Ahir K, Govani K, Gajera R, Shah M (2020) Application on virtual reality forenhanced education learning, military training and sports. Augment HumRes 5(1):7. (https://doi.org/10.1007/s41133- 019-0025-2)" }, { "key": "ref7", "doi": "10.1016/j.aiia.2020.04.002", "unstructured": "Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation ofartificial intelligence in agriculture for optimisation of irrigation andapplication of pesticides and herbicides. Artif Intell Agric 4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002" }, { "key": "ref8", "doi": "10.1016/j.aiia.2019.05.004", "unstructured": "Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review onautomation in agriculture using artificial intelligence. Artif Intell Agric 2:1–12.https://doi.org/10.1016/j.aiia.2019.05.004" }, { "key": "ref9", "doi": "10.1007/s41939-019-00049-y", "unstructured": "Kakkad V, Patel M, Shah M (2019) Biometric authentication and imageencryption for image security in cloud framework. Multiscale MultidiscipModel Exp Des 2(4):233–248. https://doi.org/10.1007/s41939-019-00049-y" }, { "key": "ref10", "doi": "10.1016/j.aiia.2020.06.001", "unstructured": "Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition forapplications in smart agriculture: a comprehensive review. Artif Intell Agric4:81–95. https://doi.org/10.1016/j.aiia.2020.06.001" }, { "key": "ref11", "doi": "10.1007/s41133-019-0021-6", "unstructured": "Pandya R, Nadiadwala S, Shah R, Shah M (2020) Buildout of methodologyfor meticulous diagnosis of K-complex in EEG for aiding the detection ofAlzheimer's by artificial intelligence. Augment Hum Res 5(1):3.https://doi.org/10.1007/s41133-019-0021-6" }, { "key": "ref12", "unstructured": "Dey A (2016) Machine learning algorithms: a review. Int J Comput Sci InfTechnol 7(3):1174–1179" }, { "key": "ref13", "doi": "10.1007/s41133-020-00035-x", "unstructured": "Sukhadia A, Upadhyay K, Gundeti M, Shah S, Shah M (2020) Optimization ofsmart traffic governance system using artificial intelligence. Augment HumRes 5(1):13.https://doi.org/10.1007/s41133-020-00035-x" }, { "key": "ref14", "doi": "10.1109/comst.2018.2880039", "unstructured": "Musumeci F, Rottondi C, Nag A, Macaluso I, Zibar D, Ruffini M et al (2019)An overview on application of machine learning techniques in opticalnetworks. IEEE Commun Surv Tutorials 21(2):1381–1408.https://doi.org/10.1109/COMST.2018.2880039" }, { "key": "ref15", "doi": "10.1007/s41133-019-0024-3", "unstructured": "Patel D, Shah Y, Thakkar N, Shah K, Shah M (2020) Implementation ofartificial intelligence techniques for cancer detection. Augment Hum Res5(1):6.https://doi.org/10.1007/s41133-019-0024- 3" }, { "key": "ref16", "doi": "10.1007/s41133-019-0029-y", "unstructured": "Kundalia K, Patel Y, Shah M (2020) Multi-label movie genre detection from amovie poster using knowledge transfer learning. Augment Hum Res 5(1):11.https://doi.org/10.1007/s41133-019- 0029-y" }, { "key": "ref17", "doi": "10.1007/s00362-012-0471-0", "unstructured": "Marsland S (2015) Machine learning: an algorithmic perspective. CRC Press,Boca Raton, pp 1–" }, { "key": "ref18", "doi": "10.1201/b17476-1", "unstructured": "https://doi.org/10.1201/b17476-1" }, { "key": "ref19", "doi": "10.1007/s42488-019-00016-9", "unstructured": "Jani K, Chaudhuri M, Patel H, Shah M (2020) Machine learning in films: anapproach towards automation in film censoring. J Data Inf Manag 2(1):55–64.https://doi.org/10.1007/s42488-019- 00016-9" }, { "key": "ref20", "doi": "10.1007/s41133-019-0023-4", "unstructured": "Parekh V, Shah D, Shah M (2020) Fatigue detection using artificialintelligence framework. Augment Hum Res 5(1):5https://doi.org/10.1007/s41133-019-0023-4" }, { "key": "ref21", "doi": "10.1007/s41133-019-0030-5", "unstructured": "Gandhi M, Kamdar J, Shah M (2020) Preprocessing of non-symmetricalimages for edge detection. Augment Hum Res 5(1):10https://doi.org/10.1007/s41133-019-0030-5" }, { "key": "ref22", "doi": "10.1007/s42488-020-00030-2", "unstructured": "Panchiwala S, Shah M (2020) A comprehensive study on critical securityissues and challenges of the IoT world. J Data Inf Manag 2(7):257–278.https://doi.org/10.1007/s42488-020-00030-2" }, { "key": "ref23", "unstructured": "Simon A, Deo MS, Venkatesan S, Babu DR (2016) An overview of machinelearning and its applications. Int J Electr Sci Eng 1(1):22–24" }, { "key": "ref24", "doi": "10.1186/s42492-020-00057-7", "unstructured": "Parekh P, Patel S, Patel N, Shah M (2020) Systematic review and meta-analysis of augmented reality in medicine, retail, and games. Vis ComputInd Biomed Art 3(1):21.https://doi.org/10.1186/s42492-020-00057-7" }, { "key": "ref25", "doi": "10.1007/s41133-020-00032-0", "unstructured": "Shah K, Patel H, Sanghvi D, Shah M (2020) A comparative analysis of logisticregression, random forest and KNN models for the text classification.Augment Hum Res 5(1):12.https://doi.org/10.1007/s41133-020-00032-0" }, { "key": "ref26", "doi": "10.1007/s40745-019-00239-y", "unstructured": "Patel D, Shah D, Shah M (2020) The intertwine of brain and body: aquantitative analysis on how big data influences the system of sports. AnnData Sci 7(1):1–16. https://doi.org/10.1007/s40745-019-00239-y" }, { "key": "ref27", "doi": "10.1016/0885-064x(88)90019-2", "unstructured": "Judd S (1988) On the complexity of loading shallow neural networks. JComplex 4(3):177–192. https://doi.org/10.1016/0885-064X(88)90019-2" }, { "key": "ref28", "doi": "10.1016/s0893-6080(05)80010-3", "unstructured": "Blum AL, Rivest RL (1992) Training a 3-node neural network is NP-complete.Neural Netw 5(1):117–127. https://doi.org/10.1016/S0893-6080(05)80010-3" }, { "key": "ref29", "doi": "10.1186/s40854-020-00205-1", "unstructured": "Gupta A, Dengre V, Kheruwala HA, Shah M (2020) Comprehensive review oftext-mining applications in finance. Financ Innov 6(1):1–25. https://doi.org/10.1186/s40854-020-00205-1" }, { "key": "ref30", "doi": "10.1007/s41133-020-00038-8", "unstructured": "Shah N, Engineer S, Bhagat N, Chauhan H, Shah M (2020) Research trendson the usage of machine learning and artificial intelligence in advertising.Augment Hum Res 5(1):19.https://doi.org/10.1007/s41133-020-00038-8" }, { "key": "ref31", "doi": "10.1186/s42492-020-00062-w", "unstructured": "Naik B, Mehta A, Shah M (2020) Denouements of machine learning andmultimodal diagnostic classification of Alzheimer's disease. Vis Comput IndBiomed Art 3(1):26.https://doi.org/10.1186/s42492-020-00062-w" }, { "key": "ref32", "doi": "10.1109/fskd.2008.222", "unstructured": "Chen P, Yuan HY, Shu XM (2008) Forecasting crime using the ARIMA model.In: Paper presented at the 5th international conference on fuzzy systemsand knowledge discovery. IEEE, Ji'nan 18-20 October 2008.https://doi.org/10.1109/FSKD.2008.222" }, { "key": "ref33", "unstructured": "Rani A, Rajasree S (2014) Crime trend analysis and prediction usingmahanolobis distance and dynamic time warping technique. Int J ComputSci Inf Technol 5(3):4131–4135" }, { "key": "ref34", "doi": "10.1016/s0169-2070(03)00089-x", "unstructured": "Gorr W, Harries R (2003) Introduction to crime forecasting. Int J Forecast19(4):551–555. https://doi.org/10.1016/S0169-2070(03)00089-X" }, { "key": "ref35", "doi": "10.1016/j.apgeog.2017.06.011", "unstructured": "Rummens A, Hardyns W, Pauwels L (2017) The use of predictive analysis inspatiotemporal crime forecasting: building and testing a model in an urbancontext. Appl Geogr 86:255–261. https://doi.org/10.1016/j.apgeog.2017.06.011" }, { "key": "ref36", "unstructured": "Bates A (2017) Stingray: a new frontier in police surveillance. Cato InstitutePolicy Analysis, No" }, { "key": "ref37", "doi": "10.2139/ssrn.2924620", "unstructured": "Joh EE (2017) The undue influence of surveillance technology companieson policing. N Y Univ Law Rev 92:101–130. https://doi.org/10.2139/ssrn.2924620" }, { "key": "ref38", "doi": "10.1177/0093854818771721", "unstructured": "Vredeveldt A, Kesteloo L, Van Koppen PJ (2018) Writing alone or together:police officers' collaborative reports of an incident. Crim Justice Behav 45(7):1071–1092.https://doi.org/10.1177/0093854818771721" }, { "key": "ref39", "unstructured": "McNeal GS (2014) Drones and aerial surveillance: considerations forlegislators. In: Brookings Institution: The Robots Are Coming: The Project OnCivilian Robotics, November 2014, Pepperdine University Legal StudiesResearch Paper No. 2015/3" }, { "key": "ref40", "unstructured": "Fatih T, Bekir C (2015) Police use of technology to fight against crime. EurSci J 11(10):286–296" }, { "key": "ref41", "unstructured": "Katz CM, Choate DE, Ready JR, Nuňo L (2014) Evaluating the impact ofofficer worn body cameras in the Phoenix Police Department. Center forViolence Prevention & Community Safety, Arizona State University, Phoenix,pp 1–43" }, { "key": "ref42", "unstructured": "Stanley J (2015) Police body-mounted cameras: with right policies in place,a win for all.https://www.aclu.org/police-body-mounted-cameras-right-policies-place-win-all. Accessed 15 Aug 2015" }, { "key": "ref43", "doi": "10.5121/mlaij.2015.2101", "unstructured": "McClendon L, Meghanathan N (2015) Using machine learning algorithms toanalyze crime data. Mach Lear Appl Int J 2(1):1–12. https://doi.org/10.5121/mlaij.2015.2101" }, { "key": "ref44", "doi": "10.1093/bioinformatics/bth261", "unstructured": "Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining inbioinformatics using Weka. Bioinformatics 20(15):2479–2481.https://doi.org/10.1093/bioinformatics/bth261" }, { "key": "ref45", "doi": "10.1109/iemcon.2018.8614828", "unstructured": "Kim S, Joshi P, Kalsi PS, Taheri P (2018) Crime analysis through machinelearning. In: Paper presented at the IEEE 9th annual information technology,Shahet al. Visual Computing for Industry, Biomedicine, and Art (2021) 4:9 Page 13 of 14 electronics and mobile communication conference. IEEE, Vancouver 1-3November 2018.https://doi.org/10.1109/IEMCON.2018.8614828" }, { "key": "ref46", "unstructured": "Tabedzki C, Thirumalaiswamy A, van Vliet P (2018) Yo home to Bel-Air:predicting crime on the streets of Philadelphia. In: University ofPennsylvania, CIS 520: machine learning" }, { "key": "ref47", "unstructured": "Bharati A, Sarvanaguru RAK (2018) Crime prediction and analysis usingmachine learning. Int Res J Eng Technol 5(9):1037–1042" }, { "key": "ref48", "unstructured": "Prithi S, Aravindan S, Anusuya E, Kumar AM (2020) GUI based prediction ofcrime rate using machine learning approach. Int J Comput Sci Mob Comput9(3):221–229" }, { "key": "ref49", "doi": "10.1371/journal.pone.0176244", "unstructured": "Kang HW, Kang HB (2017) Prediction of crime occurrence from multi-modaldata using deep learning. PLoS One 12(4):e0176244.https://doi.org/10.1371/journal.pone.0176244" }, { "key": "ref50", "doi": "10.1016/j.procs.2020.05.018", "unstructured": "Bandekar SR, Vijayalakshmi C (2020) Design and analysis of machinelearning algorithms for the reduction of crime rates in India. ProcediaComput Sci 172:122–127. https://doi.org/10.1016/j.procs.2020.05.018" }, { "key": "ref51", "doi": "10.1007/978-981-15-6648-6_22", "unstructured": "Hossain S, Abtahee A, Kashem I, Hoque M, Sarker IH (2020) Crime predictionusing spatiotemporal data. arXiv preprint arXiv:2003.09322.https://doi.org/10.1007/978-981-15-6648-6_22" }, { "key": "ref52", "doi": "10.3390/forecast3040046", "unstructured": "Stalidis P, Semertzidis T, Daras P (2018) Examining deep learningarchitectures for crime classification and prediction. arXiv preprint arXiv:1812.00602. p. 1–13" }, { "key": "ref53", "unstructured": "Jha P, Jha R, Sharma A (2019) Behavior analysis and crime prediction usingbig data and machine learning. Int J Recent Technol Eng 8(1):461–468" }, { "key": "ref54", "doi": "10.29121/ijetmr.v5.i2.2018.615", "unstructured": "Tyagi D, Sharma S (2018) An approach to crime data analysis: a systematicreview. Int J Eng Technol Manag Res 5(2):67–74. https://doi.org/10.29121/ijetmr.v5.i2.2018.615" }, { "key": "ref55", "doi": "10.3390/ijgi7080298", "unstructured": "Lin YL, Yen MF, Yu LC (2018) Grid-based crime prediction usinggeographical features. ISPRS Int J Geo-Inf 7(8):298.https://doi.org/10.3390/ijgi7080298" }, { "key": "ref56", "unstructured": "Ahishakiye E, Taremwa D, Omulo EO, Niyonzima I (2017) Crime predictionusing decision tree (J48) classification algorithm. Int J Comput Inf Technol6(3):188–195" }, { "key": "ref57", "doi": "10.14400/jdc.2014.12.8.321", "unstructured": "Sun CC, Yao CL, Li X, Lee K (2014) Detecting crime types using classificationalgorithms. J Digit Inf Manag 12(8):321–327. https://doi.org/10.14400/JDC.2014.12.8.321" }, { "key": "ref58", "unstructured": "Shojaee S, Mustapha A, Sidi F, Jabar MA (2013) A study on classificationlearning algorithms to predict crime status. Int J Digital Content TechnolAppl 7(9):361–369" }, { "key": "ref59", "doi": "10.14569/ijarai.2015.041207", "unstructured": "Obuandike GN, Isah A, Alhasan J (2015) Analytical study of some selectedclassification algorithms in WEKA using real crime data. Int J Adv Res ArtifIntell 4(12):44–48. https://doi.org/10.14569/IJARAI.2015.041207" }, { "key": "ref60", "doi": "10.17485/ijst/2013/v6i3.6", "unstructured": "Iqbal R, Murad MAA, Mustapha A, Panahy PHS, Khanahmadliravi N (2013) Anexperimental study of classification algorithms for crime prediction. Indian JSci Technol 6(3):4219–4225.https://doi.org/10.17485/ijst/2013/v6i3.6" }, { "key": "ref61", "unstructured": "Jangra M, Kalsi S (2019) Crime analysis for multistate network using naiveBayes classifier. Int J Comput Sci Mob Comput 8(6):134–143" }, { "key": "ref62", "doi": "10.1088/1742-6596/1450/1/012076", "unstructured": "Wibowo AH, Oesman TI (2020) The comparative analysis on the accuracy ofk-NN, naive Bayes, and decision tree algorithms in predicting crimes andcriminal actions in Sleman regency. J Phys Conf Ser 1450:012076.https://doi.org/10.1088/1742-6596/1450/1/012076" }, { "key": "ref63", "doi": "10.1007/978-3-319-59421-7_24", "unstructured": "Vanhoenshoven F, Nápoles G, Bielen S, Vanhoof K (2017) Fuzzy cognitivemaps employing ARIMA components for time series forecasting. In:Czarnowski I, Howlett RJ, Jain LC (eds) Proceedings of the 9th KESinternational conference on intelligent decision technologies 2017, vol 72.Springer, Heidelberg, pp 255–264. https://doi.org/10.1007/978-3-319-59421-7_24" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/533", "sha256_before": "debf88aa7e752726cc3ce6a4b3c8d1e5f94950499dc2d4ebf686eeab218fcd3b", "sha256_after": "e1bbcc007a08f45426be8d311d41dbde6b6aae89d85ec2d68e8114c2e6a1cfa6" } ] }