Crime Analysis Using Computer Vision Approach with Machine Learning

William, P.; Shrivastava, Anurag; Karpagam, N. Shunmuga; Mohanaprakash, T. A.; Tongkachok, Korakod; Kumar, Keshav

doi:10.1007/978-981-19-7982-8_25

Cited by 44 publications

(4 citation statements)

References 59 publications

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“…This supports law enforcement agencies to act quickly if a criminal activity is being committed. Additionally, integrating decentralized machine learning algorithms with wearable technology, such as body cameras and smartwatches [1], [66], provides new opportunities to collect and analyze data related to criminal activities.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

et al. 2023

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Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.

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Section: Discussion and Future Workmentioning

confidence: 99%

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

et al. 2023

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“…Clustering techniques such as K-means and DBSCAN aid in the collection of significant insights from past crime data, hence helping to build precise and efficient criminal profiles. In addition, these profiles enable law enforcement organizations to make more informed judgments, distribute resources more effectively, and improve public safety precautions [146][147][148].…”

Section: Criminal Profilingmentioning

confidence: 99%

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Chaudhry,

Shafi,

Mahnoor

et al. 2023

Symmetry

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Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

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“…Decision Trees are recognized as models based on structure for classification and are represented or shown as hierarchies that create trees in which nodes represent data features. Possible values are represented by arcs that come from a specific node (William et al, 2023). Moving down to the hierarchy's lowest level represents leaves, which indicates possible classifications of different data elements.…”

Section: Decision Treesmentioning

confidence: 99%

Crime Prediction Using Machine Learning: A Comparative Analysis

Alsubayhin,

Ramzan,

Alzahrani

2023

Journal of Computer Science

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In the past few years, Machine Learning (ML) methods have acquired extensive attention from researchers and even criminology practitioners for their potential in crime prediction and crime forecast. To date, many research studies have already been performed on crime prediction using machine learning techniques. Some common methods that have been used include KNNs, Decision trees, Naïve Bayes, and random forest, among others. In this research study, a comparative analysis of 51 research studies has been performed. The results indicate that the supervised learning approach is the most commonly used by researchers. In addition, random forest was the most commonly used method. In various studies, almost all proposed models and methods produced accurate results and outcomes with the exception of one study. In the future, it is critical to evaluate these ML based algorithms in a realworld situation and identify which situations or factors affect their accuracy. At the same time, it is also necessary to identify and evaluate which techniques can be used to improve the accuracy of each machine learning method and which algorithm or method might prove to be the best choice for police departments to use for predicting crime.

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Crime Analysis Using Computer Vision Approach with Machine Learning

Cited by 44 publications

References 59 publications

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Crime Prediction Using Machine Learning: A Comparative Analysis

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