Applications of Machine Learning in Public Security Information and Resource Management

Wang, Zhihui; Wang, Jinyu

doi:10.1155/2021/4734187

“…LL Xing reviewed analyzing China's network security governance policies through the lens of big data, and put forward optimization suggestions [5] to provide references for policy improvement. Z Wang used data mining and big data technology to build a public security information analysis system to quickly analyze police data, provide a basis for public security decision-making [6]. X Li proposed a network security framework, developing an efficient security model leveraging artificial intelligence technology,and verified its feasibility and effectiveness through typical cases [7].…”

Section: Related Researchmentioning

confidence: 99%

Application Analysis of Computer Information Security under Big Data

2024

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With the wide application of Internet technology, the application scope of big data technology in all walks of life is expanding. The widespread use of the Internet has led to the generation and accumulation of massive data, which is not only a valuable information resource, but also a challenge to information security. In the current big data environment, computer information security faces many key technologies and challenges, such as data privacy protection, network attack prevention, identity authentication, and so on. To address these challenges, this article will focus on exploring the application of big data technology in the field of computer information security. In the big data environment, data storage, transmission, and processing face more complex security issues. At the same time, the application of big data technology also provides new possibilities for information security, such as security event detection and response systems based on big data analysis and network intrusion detection systems based on behavior analysis. Through in-depth analysis and effective protection strategies in this article, the aim is to provide practical references for related research and promote the continuous development and innovation in the field of computer information security. In the research, we will further explore the integration of big data technology with emerging technologies such as artificial intelligence and blockchain to address the increasingly complex challenges in the field of information security and achieve a positive interaction between information security and technological innovation.

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“…Proper data collection techniques are essential to design high-performing models. The data should be without errors and include pertinent information for the assignment (Albahli et al, 2020;Zhihui Wang & Wang, 2021).…”

Section: 1data Collection and Study Areamentioning

confidence: 99%

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Divyasri¹,

Saranya

²

,

P.Kathiravan

³

2023

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Crimes are a social issue that affects not only an individual but also humanity. Crime classification techniques for crime forecasting are an emerging research area. generally, Crime data are centrally organized with regular maintenance of the criminal registers that can aid officers in sharing observations and improve early alert approaches to keep the citizens secure within their towns. Hence, the aim of this study is to compare the performance of the state-of-the-art Dynamic Ensemble Selection of Classifier algorithms for predicting crime. We used five different benchmark crime datasets (Chicago, San Francisco, Pheonix, Boston, and Vancouver) for this experimental research work. The performance of the state-of-the-art dynamic ensemble selection of classifiers algorithms was evaluated and compared using various performance evaluation metrics such as accuracy, F1-score, precision, and recall. The KNORA Dynamic ensemble algorithms, which select the subset of ensemble members before the forecasting, outperformed the typical machine learning algorithms, and also the traditional ensemble algorithm techniques in terms of accuracy showed that the dynamic ensemble algorithms are more powerful. This ability to predict crimes within urban societies can help citizens, and law enforcement makes precise informed conclusions and preserves the neighborhoods more unassailably to improve the quality of life for humans.

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Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Divyasri¹,

Saranya

²

,

P.Kathiravan

³

2023

Preprint

0

View full text Add to dashboard Cite

Crimes are a social issue that affects not only an individual but also humanity. Crime classification techniques for crime forecasting are an emerging research area. generally, Crime data are centrally organized with regular maintenance of the criminal registers that can aid officers in sharing observations and improve early alert approaches to keep the citizens secure within their towns. Hence, the aim of this study is to compare the performance of the state-of-the-art Dynamic Ensemble Selection of Classifier algorithms for predicting crime. We used five different benchmark crime datasets (Chicago, San Francisco, Pheonix, Boston, and Vancouver) for this experimental research work. The performance of the state-of-the-art dynamic ensemble selection of classifiers algorithms was evaluated and compared using various performance evaluation metrics such as accuracy, F1-score, precision, and recall. The KNORA Dynamic ensemble algorithms, which select the subset of ensemble members before the forecasting, outperformed the typical machine learning algorithms, and also the traditional ensemble algorithm techniques in terms of accuracy showed that the dynamic ensemble algorithms are more powerful. This ability to predict crimes within urban societies can help citizens, and law enforcement makes precise informed conclusions and preserves the neighborhoods more unassailably to improve the quality of life for humans.

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Applications of Machine Learning in Public Security Information and Resource Management

Cited by 3 publications

References 22 publications

Application Analysis of Computer Information Security under Big Data

Application Analysis of Computer Information Security under Big Data

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

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