Edge assisted crime prediction and evaluation framework for machine learning algorithms
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...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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IEEE Computer Society
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39595/1/Edge%20Assisted%20Crime%20Prediction%20and%20Evaluation%20Framework%20for%20Machine.pdf http://umpir.ump.edu.my/id/eprint/39595/2/Edge%20assisted%20crime%20prediction%20and%20evaluation%20framework%20for%20machine%20learning%20algorithms_ABS.pdf |
| _version_ | 1825815324851699712 |
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| author | Adhikary, Apurba Murad, Saydul Akbar Munir, Md Shirajum Choong Seon, Hong Seong |
| author_facet | Adhikary, Apurba Murad, Saydul Akbar Munir, Md Shirajum Choong Seon, Hong Seong |
| author_sort | Adhikary, Apurba |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T13:11:49Z |
| format | Conference or Workshop Item |
| id | UMPir39595 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:11:49Z |
| publishDate | 2022 |
| publisher | IEEE Computer Society |
| record_format | dspace |
| spelling | UMPir395952023-12-11T04:31:33Z http://umpir.ump.edu.my/id/eprint/39595/ Edge assisted crime prediction and evaluation framework for machine learning algorithms Adhikary, Apurba Murad, Saydul Akbar Munir, Md Shirajum Choong Seon, Hong Seong QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. IEEE Computer Society 2022-01 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39595/1/Edge%20Assisted%20Crime%20Prediction%20and%20Evaluation%20Framework%20for%20Machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/39595/2/Edge%20assisted%20crime%20prediction%20and%20evaluation%20framework%20for%20machine%20learning%20algorithms_ABS.pdf 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 (Published) https://doi.org/10.1109/ICOIN53446.2022.9687156 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Adhikary, Apurba Murad, Saydul Akbar Munir, Md Shirajum Choong Seon, Hong Seong Edge assisted crime prediction and evaluation framework for machine learning algorithms |
| title | Edge assisted crime prediction and evaluation framework for machine learning algorithms |
| title_full | Edge assisted crime prediction and evaluation framework for machine learning algorithms |
| title_fullStr | Edge assisted crime prediction and evaluation framework for machine learning algorithms |
| title_full_unstemmed | Edge assisted crime prediction and evaluation framework for machine learning algorithms |
| title_short | Edge assisted crime prediction and evaluation framework for machine learning algorithms |
| title_sort | edge assisted crime prediction and evaluation framework for machine learning algorithms |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/39595/1/Edge%20Assisted%20Crime%20Prediction%20and%20Evaluation%20Framework%20for%20Machine.pdf http://umpir.ump.edu.my/id/eprint/39595/2/Edge%20assisted%20crime%20prediction%20and%20evaluation%20framework%20for%20machine%20learning%20algorithms_ABS.pdf |
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