Smart City Gnosys

Smart city article details

Title	Predictive Analytics For Crime Prevention In Smart Cities Using Machine Learning
ID_Doc	42868
Authors	Kalpana P.; Kodati S.; Sreekanth N.; Ali H.M.; Ramachandra A.C.
Year	2024
Published	International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024
DOI	http://dx.doi.org/10.1109/IACIS61494.2024.10721948
Abstract	Crime prevention in smart city infrastructure significantly impacts improving quality of human life. However, the substantial increase in the urban population in recent years affects accuracy, safety, and security. This paper presents a solution using the Hyperplane to improve the Radial Basis Function with Support Vector Machine (SVM) based on Machine Learning techniques to enhance accuracy, safety, and security. The SVM method aims to reduce crime rates through proactive measures based on predictive insights. The RBF kernel handles nonlinear relationships by mapping input features into a higher-dimensional space, allowing the model to capture complex patterns. Initially, data is obtained from the Chicago crime dataset and pre-processed by checking for missing values and performing feature scaling to ensure consistency. Feature selection is conducted using Principal Component Analysis (PCA) to identify relevant features from the data. Crime prediction using RBF with SVM is performed efficiently to enhance accuracy. The proposed RBF-SVM model is evaluated using the Chicago crime dataset, achieving a high accuracy of 0.89, a precision of 0.80, a recall of 0.82, and an F1-score of 0.85. The performance is compared to existing techniques such as Decision Tree (DT) and Long Short-Term Memory (LSTM). © 2024 IEEE.
Author Keywords	chicago crime dataset; crime predication; principal component analysis; radial basis function and support vector machine

Similar Articles

Id	Similarity	Authors	Title	Published
42718	0.883	Moeinizade S.; Hu G.	Predicting Metropolitan Crime Rates Using Machine Learning Techniques	Springer Proceedings in Business and Economics (2020)
25468	0.874	Pradhan I.; Eirinaki M.; Potika K.; Potikas P.	Exploratory Data Analysis And Crime Prediction For Smart Cities	ACM International Conference Proceeding Series (2019)
16545	0.871	Rojo J.F.S.; Bocanegra L.A.F.; Vega J.F.B.; Gomez N.G.	Crime Prediction Using Support Vector Machine And Extracted Twitter Features	2023 IEEE Colombian Conference on Communications and Computing, COLCOM 2023 - Proceedings (2023)
57814	0.861	Araujo A.; Cacho N.; Bezerra L.; Vieira C.; Borges J.	Towards A Crime Hotspot Detection Framework For Patrol Planning	Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 (2019)
52552	0.858	Butt, UM; Letchmunan, S; Hassan, FH; Ali, M; Baqir, A; Koh, TW; Sherazi, HHR	Spatio-Temporal Crime Predictions By Leveraging Artificial Intelligence For Citizens Security In Smart Cities	IEEE ACCESS, 9 (2021)
3433	0.858	Nedeljkovic D.; Fares N.Y.; Jammal M.	A Novel Machine Learning-Based Approach To City Crime Sensor Placement Prediction	2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023 (2023)
8874	0.857	Ramji R.; Devi S.	An Optimized Clisteer Long-Range Kookaburra Attention Module Steerable Convolutional Neural Networks For Crime Predictions In Smart Cities	2025 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2025 (2025)
11185	0.852	Singhal A.; Singhal N.; Kumar P.	Automated Crime Anomaly Detection In Smart Cities Using Sharkprey Optimization Algorithm And Ensembled-Machine Learning Approach	Nanotechnology Perceptions, 20, S7 (2024)
16543	0.851	David M.; Mbabazi E.S.; Nakatumba-Nabende J.; Marvin G.	Crime Forecasting Using Interpretable Regression Techniques	7th International Conference on Trends in Electronics and Informatics, ICOEI 2023 - Proceedings (2023)