Visual Analysis of Predictive Policing to Improve Crime Investigation

Sathiyanarayanan, Mithileysh; Junejo, Aisha Kanwal; Fadahunsi, Odunayo

doi:10.1109/ic3i46837.2019.9055515

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…As we know, the Internet of Medical Things (IoMT) is an integration of user devices, medical devices, web/mobile applications connecting healthcare servers where privacy and security is the primary key. We will focus on data hacking and data secure transmission by allowing the transfer of medical data over a secure network [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. We will also aim to evaluate the effectiveness and shortfalls of current vital sign monitoring systems in place some of the hospitals, then illustrate the significance of our multifunctional vital sign monitoring in detecting patient deterioration in a hospital setting.…”

Section: Discussionmentioning

confidence: 99%

Smart Multifunctional Vital Testing Kit for Patients

Duraiselvam

Gayathri²,

Ranjana³

et al. 2022

2022 IEEE Delhi Section Conference (DELCON)

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Section: Discussionmentioning

confidence: 99%

Smart Multifunctional Vital Testing Kit for Patients

Duraiselvam

Gayathri²,

Ranjana³

et al. 2022

2022 IEEE Delhi Section Conference (DELCON)

Self Cite

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“…Traditional regression techniques can successfully check the variables' significance but, they must be more reliable for crime prediction. In many research works [32], [33], [34] mentioned in section V, authors have proven that machine learning models effectively predict crimes. Still, they could be more efficient in which variables are significant in predicting crimes.…”

Section: ) Machine Learning Based Classification Methods For Crime Pr...mentioning

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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“…For example, Malik et al presented an instrument for police resource allocation and predictive analytics [ 34 ]. Various other works also deal with the identification of criminal hotspots and use visual analytics procedures to facilitate the process [ 35 , 36 ]. Sacha et al introduced a tool for the interactive analysis of spatio-temporal metadata of crime reports using abstract data visualizations such as correlation matrices and scatterplots [ 37 ].…”

Section: Related Workmentioning

confidence: 99%

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

Kraus

Pollok

Miller

et al. 2020

Sensors

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The technical progress in the last decades makes photo and video recording devices omnipresent. This change has a significant impact, among others, on police work. It is no longer unusual that a myriad of digital data accumulates after a criminal act, which must be reviewed by criminal investigators to collect evidence or solve the crime. This paper presents the VICTORIA Interactive 4D Scene Reconstruction and Analysis Framework (“ISRA-4D” 1.0), an approach for the visual consolidation of heterogeneous video and image data in a 3D reconstruction of the corresponding environment. First, by reconstructing the environment in which the materials were created, a shared spatial context of all available materials is established. Second, all footage is spatially and temporally registered within this 3D reconstruction. Third, a visualization of the hereby created 4D reconstruction (3D scene + time) is provided, which can be analyzed interactively. Additional information on video and image content is also extracted and displayed and can be analyzed with supporting visualizations. The presented approach facilitates the process of filtering, annotating, analyzing, and getting an overview of large amounts of multimedia material. The framework is evaluated using four case studies which demonstrate its broad applicability. Furthermore, the framework allows the user to immerse themselves in the analysis by entering the scenario in virtual reality. This feature is qualitatively evaluated by means of interviews of criminal investigators and outlines potential benefits such as improved spatial understanding and the initiation of new fields of application.

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Visual Analysis of Predictive Policing to Improve Crime Investigation

Cited by 13 publications

References 9 publications

Smart Multifunctional Vital Testing Kit for Patients

Smart Multifunctional Vital Testing Kit for Patients

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

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

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