Edge assisted crime prediction and evaluation framework for machine learning algorithms

Adhikary, Apurba and Murad, Saydul Akbar and Munir, Md Shirajum and Choong Seon, Hong Seong (2022) Edge assisted crime prediction and evaluation framework for machine learning algorithms. In: International Conference on Information Networking; 36th International Conference on Information Networking, ICOIN 2022 , 12-15 January 2022 , Virtual, Jeju Island. pp. 417-422., 2022 (176661). ISSN 1976-7684 ISBN 978-166541332-9

[img] Pdf
Edge Assisted Crime Prediction and Evaluation Framework for Machine.pdf
Restricted to Repository staff only

Download (411kB) | Request a copy
Edge assisted crime prediction and evaluation framework for machine learning algorithms_ABS.pdf

Download (96kB) | Preview


The growing global populations, particularly in major cities, have created new problems, notably in terms of public safety regulation and optimization. As a result, in this paper, a strategy is provided for predicting crime occurrences in a city based on historical events and demographic observation. In particular, this study proposes a crime prediction and evaluation framework for machine learning algorithms of the network edge. Thus, a complete analysis of four distinct sorts of crimes, such as murder, rapid trial, repression of women and children, and narcotics, validates the efficiency of the proposed framework. The complete study and implementation process have shown a visual representation of crime in various areas of country. The total work is completed by the selection, assessment, and implementation of the Machine Learning (ML) model, and finally, proposed the crime prediction. Criminal risk is predicted using classification models for a particular time interval and place. To anticipate occurrences, ML methods such as Decision Trees, Neural Networks, K-Nearest Neighbors, and Impact Learning are being utilized, and their performance is compared based on the data processing and modification used. A maximum accuracy of 81% is obtained for Decision Tree algorithm during the prediction of crime. The findings demonstrate that employing Machine Learning techniques aids in the prediction of criminal events, which has aided in the enhancement of public security.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Crime Prediction; Decision Tree; Edge Computing; Impact Learning; KNN; Machine Learning; MLP
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: College of Engineering
Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 11 Dec 2023 04:31
Last Modified: 11 Dec 2023 04:31
URI: http://umpir.ump.edu.my/id/eprint/39595
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item