433 240

Title

Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models

Authors Names :   Hamzah A. Alsayadi   1 *     Nima Khodadadi   2     Sunil Kumar   3  

1  Affiliation :  Computer Science Department, Faculty of Sciences, Ibb University, Yemen

    Email :  hamzah.sayadi@cis.asu.edu.eg


2  Affiliation :  Department of Civil and Environmental Engineering, Florida International University, Miami, FL, USA

    Email :   Nkhod002@fiu.edu


3  Affiliation :  School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248001, India

    Email :  drskumar.cs@gmail.com



Doi   :   https://doi.org/10.54216/JAIM.010103

Received: January 09, 2022 Accepted: May 18, 2022

Abstract :

 The term” crime prevention” refers to a group of initiatives that work with people, communities, businesses, non-governmental organizations, and all levels of government to address the numerous social and environmental risk factors for crime, disorder, and victimization in communities. In this paper, the authors proposed various regression model for the prediction of communities and crime including decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The communities and crime dataset are used for training and evaluation the proposed model. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.

Keywords :

Communities and crime; Ensemble model , Machine learning; Regression model.

References :

[1] Miguel Saraiva, Irina Matijosaitien ˇ e, Saloni Mishra, and Ana Amante. Crime prediction and monitoring in porto, portugal, using machine learning, spatial and text analytics. ISPRS International Journal of Geo-Information, 11(7):400, 2022.

[2] European Commission. My region, my europe, our future. Seventh report on economic, social and territorial cohesion, 2017.

[3] Patricia L Brantingham and Paul J Brantingham. Situational crime prevention in practice. Canadian journal of criminology, 32(1):17–40, 1990.

[4] Martin A Andresen. Environmental criminology: Evolution, theory, and practice. Routledge, 2014.

[5] Richard Wortley and Michael Townsley. Environmental criminology and crime analysis: Situating the theory, analytic approach and application. In Environmental criminology and crime analysis, pages 20–45. Routledge, 2016.

[6] RVG Clarke. “situational” crime prevention: Theory and practice. In Crime Opportunity Theories, pages 471–482. Routledge, 2017.

[7] Rizwan Iqbal, Masrah Azrifah Azmi Murad, Aida Mustapha, Payam Hassany Shariat Panahy, and Nasim Khanahmadliravi. An experimental study of classifcation algorithms for crime prediction. Indian Journal of Science and Technology, 6(3):4219–4225, 2013.

[8] Fatihah Mohd, Noor Maizura Mohamad Noor, et al. A comparative study to evaluate fltering methods for crime data feature selection. Procedia computer science, 116:113–120, 2017.

[9] Sujatha Radhakrishnan and Ezhilmaran Devarasan. Computing the probability on socio economic factors to predict the crime locations by means of joint probability based amabc-fcil. International Journal of Intelligent Engineering and System, 9(3):80–89, 2016.

[10] Ying-Lung Lin, Tenge-Yang Chen, and Liang-Chih Yu. Using machine learning to assist crime prevention. In 2017 6th IIAI international congress on advanced applied informatics (IIAI-AAI), pages 1029–1030. IEEE, 2017.

[11] Xu Zhang, Lin Liu, Luzi Xiao, and Jiakai Ji. Comparison of machine learning algorithms for predicting crime hotspots. IEEE Access, 8:181302–181310, 2020.

[12] Irina Matijosaitiene, Anthony McDowald, and Vishal Juneja. Predicting safe parking spaces: A machine learning approach to geospatial urban and crime data. Sustainability, 11(10):2848, 2019.

[13] Marcus Pinto, Hsinrong Wei, Kiyatou Konate, and Ida Touray. Delving into factors influencing new York crime data with the tools of machine learning. Journal of Computing Sciences in Colleges, 36(2):61–70, 2020.

[14] Mohammad Al Boni and Matthew S Gerber. Area-specifc crime prediction models. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 671–676. IEEE, 2016.

[15] Qiang Zhang, Pingmei Yuan, Qiyun Zhou, and Zhiming Yang. Mixed spatial-temporal characteristics based crime hot spots prediction. In 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 97–101. IEEE, 2016.

[16] Nafz Mahmud, Khalid Ibn Zinnah, Yeasin Ar Rahman, and Nasim Ahmed. Crimecast: A crime prediction and strategy direction service. In 2016 19th International Conference on Computer and Information Technology (ICCIT), pages 414–418. IEEE, 2016.

[17] Fateha Khanam Bappee, Am´ılcar Soares Junior, and Stan Matwin. Predicting crime using spatial features. In Canadian Conference on Artifcial Intelligence, pages 367–373. Springer, 2018.

[18] Hyeon-Woo Kang and Hang-Bong Kang. Prediction of crime occurrence from multi-modal data using deep learning. PloS one, 12(4):e0176244, 2017.

[19] Xiangyu Zhao and Jiliang Tang. Exploring transfer learning for crime prediction. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pages 1158–1159. IEEE, 2017.

[20] Roman Marchant, Sebastian Haan, Garner Clancey, and Sally Cripps. Applying machine learning to criminology: semi-parametric spatial-demographic bayesian regression. Security Informatics, 7(1):1–19, 2018.

[21] S Prabakaran and Shilpa Mitra. Survey of analysis of crime detection techniques using data mining and machine learning. In Journal of Physics: Conference Series, volume 1000, page 012046. IOP Publishing, 2018.

[22] Eugenio Cesario, Charlie Catlett, and Domenico Talia. Forecasting crimes using autoregressive models. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pages 795–802. IEEE, 2016.

[23] Luiz GA Alves, Haroldo V Ribeiro, and Francisco A Rodrigues. Crime prediction through urban metrics and statistical learning. Physica A: Statistical Mechanics and its Applications, 505:435–443, 2018.

[24] Nelson Baloian, Col Enrique Bassaletti, Mario Fernandez, Oscar Figueroa, Pablo Fuentes, Ra ´ ul Manase-vich, Marcos Orchard, Sergio Penafel, Jos ˜ e A Pino, and Mario Vergara. Crime prediction using patterns and context. In 2017 IEEE 21st international conference on computer supported cooperative work in design (CSCWD), pages 2–9. IEEE, 2017.

[25] Miquel Vaquero Barnadas. Machine learning applied to crime prediction. B.S. thesis, Universitat Politecnica de Catalunya, 2016.

[26] Wenjuan Wei, Olivier Ramalho, Laeticia Malingre, Sutharsini Sivanantham, John C Little, and Corinne Mandin. Machine learning and statistical models for predicting indoor air quality. Indoor Air, 29(5):704– 726, 2019.

[27] El-Sayed M. El-Kenawy, Seyedali Mirjalili, Fawaz Alassery, Yu-Dong Zhang, Marwa Metwally Eid, Shady Y. El-Mashad, Bandar Abdullah Aloyaydi, Abdelhameed Ibrahim, and Abdelaziz A. Abdelhamid. Novel Meta-Heuristic Algorithm for Feature Selection, Unconstrained Functions and Engineering Problems. IEEE Access, 10:40536–40555, 2022.

[28] Abdelaziz A. Abdelhamid, El-Sayed M. El-Kenawy, Bandar Alotaibi, Ghada M. Amer, Mahmoud Y. Abdelkader, Abdelhameed Ibrahim, and Marwa Metwally Eid. Robust Speech Emotion Recognition Using CNN+LSTM Based on Stochastic Fractal Search Optimization Algorithm. IEEE Access, 10:49265– 49284, 2022.

[29] S Abdullah, M Ismail, and AN Ahmed. Multi-layer perceptron model for air quality prediction. Malaysian Journal of Mathematical Sciences, 13:85–95, 2019.

[30] Doaa Sami Khafaga, Amel Ali Alhussan, El-Sayed M. El-kenawy, Ali E. Takieldeen, Tarek M. Hassan, Ehab A. Hegazy, Elsayed Abdel Fattah Eid, Abdelhameed Ibrahim, and Abdelaziz A. Abdelhamid. Metaheuristics for Feature Selection and Classifcation in Diagnostic Breast-Cancer. Computers, Materials & Continua, 73(1):749–765, 2022.

[31] Doaa Sami Khafaga, Amel Ali Alhussan, El-Sayed M. El-kenawy, Abdelhameed Ibrahim, Said H. Abd Elkhalik, Shady Y. El-Mashad, and Abdelaziz A. Abdelhamid. Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM. Computers, Materials & Continua, 73(1):865–881, 2022.

[32] Ruizhi Zhong, Raymond L Johnson Jr, and Zhongwei Chen. Using machine learning methods to identify coals from drilling and logging-while-drilling lwd data. In Asia Pacifc Unconventional Resources Technology Conference, Brisbane, Australia, 18-19 November 2019, pages 970–994. Unconventional Resources Technology Conference, 2020.

[33] Nagwan Abdel Samee, El-Sayed M. El-Kenawy, Ghada Atteia, Mona M. Jamjoom, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Noha E. El-Attar, Tarek Gaber, Adam Slowik, and Mahmoud Y. Shams. Metaheuristic Optimization Through Deep Learning Classifcation of COVID-19 in Chest X-Ray Images. Computers, Materials & Continua, 73(2):4193–4210, 2022.

[34] Hussah Nasser AlEisa, El-Sayed M. El-kenawy, Amel Ali Alhussan, Mohamed Saber, Abdelaziz A. Abdelhamid, and Doaa Sami Khafaga. Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm. Computers, Materials & Continua, 73(2):2371–2387, 2022.

[35] Zhi-Hua Zhou. Machine learning. Springer Nature, 2021.

[36] Doaa Sami Khafaga, Amel Ali Alhussan, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Marwa Metwally Eid, and Abdelaziz A. Abdelhamid. Solving optimization problems of metamaterial and double t-shape antennas using advanced meta-heuristics algorithms. IEEE Access, 10:74449–74471, 2022.

[37] Burhan BARAN. Air quality index prediction in besiktas district by artifcial neural networks and k nearest neighbors. Muhendislik Bilimleri ve Tasarım Dergisi ¨ , 9(1):52–63, 2021.


Cite this Article as :
Style #
MLA Hamzah A. Alsayadi, Nima Khodadadi,Sunil Kumar. "Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models." Journal of Artificial Intelligence and Metaheuristics, Vol. 1, No. 1, ,PP. 27-34.
APA Hamzah A. Alsayadi, Nima Khodadadi,Sunil Kumar. (). Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models. Journal of Journal of Artificial Intelligence and Metaheuristics, 1 ( 1 ), 27-34.
Chicago Hamzah A. Alsayadi, Nima Khodadadi,Sunil Kumar. "Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models." Journal of Journal of Artificial Intelligence and Metaheuristics, 1 no. 1 (): 27-34.
Harvard Hamzah A. Alsayadi, Nima Khodadadi,Sunil Kumar. (). Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models. Journal of Journal of Artificial Intelligence and Metaheuristics, 1 ( 1 ), 27-34.
Vancouver Hamzah A. Alsayadi, Nima Khodadadi,Sunil Kumar. Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models. Journal of Journal of Artificial Intelligence and Metaheuristics, (); 1 ( 1 ): 27-34.
IEEE Hamzah A. Alsayadi, Nima Khodadadi,Sunil Kumar, Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 1 , No. 1 , () : 27-34 (Doi   :  https://doi.org/10.54216/JAIM.010103)