Research on Victim Genders in LA based-on Machine Learning
DOI:
https://doi.org/10.54097/ggrkeb45Keywords:
Machine Learning, Crime Victim, Gender, KNN, Logistic Regression, Neural NetworkAbstract
Recent years, more and more victims in crime in Los Angeles, especially after COVID-19. And women have more fear of crime and feel that they are more vulnerable than men. This article studies the future victim gender prediction by using algorithms in machine learning, like KNN, logistic regression and neural network. Machine learning has become more and more popular in the past few years. The results show that females are not seem as more vulnerable in crime than males in LA region. In addition, neural network method performs better than KNN and logistic regression, showing higher accuracy and f1-score. The accuracy of KNN is 59.82% and the f1-score is 65.36%. The accuracy of LR is 61.31% and f1-score is 67.76%. The accuracy of neural network is 62.58%, the f1-score is 70.22%. The result in this article can help the government to mitigate the heavy anxiety of females about being a victim in crimes.
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References
Bianca Pallaro, Tami Abdollah. Which Los Angeles neighborhoods are safest? See the latest trends in the LA crime rates. 2023.03.20. Retrieved at: https://www.usatoday.com/story/news/2023/03/20/los-angeles-crime-rates-map-safest-neighborhoods/11032761002/.
Raqota Berger. Crime in Greater Los Angeles: Experiences and Perceptions of Local Urbane Residents. Center for Criminal and Psychological Studies, Los Angeles, USA, 2018.
Varun Mandalapu, Lavanya Elluri, Piyush Vyas, Nirmalya Roy. Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions. 2023.
Paidipati Dinesh, Kalyanasundaram P. Medical Image Prediction for Diagnosis of Breast Cancer Disease Comparing the Machine Learning Algorithms: SVM, KNN, Logistic Regression, Random Forest, and Decision Tree to Measure Accuracy. ECS Transactions, 2022, 1(107).
Mohammed Marouane Saim, Hassan Ammor. Comparative Study of Machine Learning Algorithms (SVM, Logistic Regression and KNN) to Predict Cardiovascular Diseases. ERSC, E3S Research Center, Mohammed V University of Rabat, Morocco. 2022.
Elias Dritsas, Maria Trigka. Stroke Risk Prediction with Machine Learning Techniques. Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece. 2022.
A. B. Amjoud and M. Amrouch, Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic Regression, in IEEE Access, 2022, (10), 128543-128553.
Aygyil Epimakhova. Using machine learning to classify and interpret wordplay. DATA LAB du master BIHAR de l’ESTIA, ESTIA BERRI-Technopole Izarbel 90 allée Fauste d’Elhuyar, BIDART, 64210, France.
Z. Jiang and G. Shen, Prediction of House Price Based on The Back Propagation Neural Network in The Keras Deep Learning Framework, 2019 6th International Conference on Systems and Informatics (ICSAI), Shanghai, China, 2019, 1408-1412.
Mall PK, Yadav RK, Rai AK, Narayan V, Srivastava S. Early Warning Signs of Parkinson’s Disease Prediction Using Machine Learning Technique. Journal of Pharmaceutical Negative Results. 2022 Dec 31: 4784-92.
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