Edge assisted crime prediction and evaluation framework for machine learning algorithms

• Main Authors: Adhikary, Apurba, Murad, Saydul Akbar, Munir, Md Shirajum, Choong Seon, Hong Seong
• Format: Conference or Workshop Item
• Language: English; English
• Published: IEEE Computer Society 2022
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• 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; http://umpir.ump.edu.my/id/eprint/39595/; https://doi.org/10.1109/ICOIN53446.2022.9687156

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...