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J. Imaging
Special Issues
Crime Prevention, Detection and Investigation Using Digital Evidence and Artificial...
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Crime Prevention, Detection and Investigation Using Digital Evidence and Artificial Intelligence

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Biometrics, Forensics, and Security".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 4048

Special Issue Editors

Prof. Dr. Sule Yildirim Yayilgan

E-Mail Website
Guest Editor
Department of Information Security and Communication Technology, Norwegian University of Science and Technology, 2815 Gjøvik, Norway
Interests: AI and machine learning; image processing and analysis; information security
Special Issues, Collections and Topics in MDPI journals
Dr. Edlira Kalemi Vakaj

E-Mail Website
Guest Editor
Department of Computing and Data Science, College of Computing, Birmingham City University, Millennium Point, 1 Curzon Street, Birmingham B4 7XG, UK
Interests: semantic web technologies; knowledge graphs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There are numerous social networks such as Facebook, LinkedIn, Google Plus and Twitter and an abundance of valuable information in the form of chat text, images, videos, networking graphs of individuals, etc., which can be used to prevent, detect and solve crimes. Moreover, there are digital documents, reports, activity logs, images and biometrics information that are left as traces by criminals and there is also information archived from police entities. All of these data can be collected, represented and used as digital crime evidence in preventing and solving crimes. Crimes are not necessarily digital, but the traces left behind are or they can be digitized. There is a need for representing such digital information in a meaningful way and making sense of it in order to support the process of crime investigation and prevention. Ontologies and knowledge graphs, semantic similarity, text mining and other Artificial Intelligence approaches are useful for such purposes. It is possible to benefit from ontologies and knowledge graphs in different ways: intelligence gathering, reasoning over data, smarter searches and comparisons, open data publication purposes and the overall management of the crime solving and prevention process. In this Special Issue, we are looking for contributions that provide innovative methodologies, techniques, tools, approaches and insights into representing and reasoning with crime information. Keeping the chain of custody for all types of digital evidence and authenticating it in order to be admitted in court is also important to provide solutions. Biometrics, GDPR, data privacy issues, ethics and the legal aspects of crime prevention, detection and investigation are also of interest.

Dr. Sule Yildirim Yayilgan
Dr. Edlira Kalemi Vakaj
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crime representation digitally
  • crime ontology development approaches
  • crime ontology enhancement
  • crime ontology reasoning
  • evidence collection digitally
  • evidence collection from online social networks
  • digital court evidence
  • data privacy
  • GDPR and legal aspects

Published Papers (1 paper)

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Research

27 pages, 733 KiB  
Article
An Ontological Framework to Facilitate Early Detection of ‘Radicalization’ (OFEDR)—A Three World Perspective
by Linda Wendelberg
J. Imaging 2021, 7(3), 60; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7030060 - 22 Mar 2021
Cited by 4 | Viewed by 2955
Abstract
This paper presents an ontology that involves using information from various sources from different disciplines and combining it in order to predict whether a given person is in a radicalization process. The purpose of the ontology is to improve the early detection of [...] Read more.
This paper presents an ontology that involves using information from various sources from different disciplines and combining it in order to predict whether a given person is in a radicalization process. The purpose of the ontology is to improve the early detection of radicalization in persons, thereby contributing to increasing the extent to which the unwanted escalation of radicalization processes can be prevented. The ontology combines findings related to existential anxiety that are related to political radicalization with well-known criminal profiles or radicalization findings. The software Protégé, delivered by the technical field at Stanford University, including the SPARQL tab, is used to develop and test the ontology. The testing, which involved five models, showed that the ontology could detect individuals according to “risk profiles” for subjects based on existential anxiety. SPARQL queries showed an average detection probability of 5% including only a risk population and 2% on a whole test population. Testing by using machine learning algorithms proved that inclusion of less than four variables in each model produced unreliable results. This suggest that the Ontology Framework to Facilitate Early Detection of ‘Radicalization’ (OFEDR) ontology risk model should consist of at least four variables to reach a certain level of reliability. Analysis shows that use of a probability based on an estimated risk of terrorism may produce a gap between the number of subjects who actually have early signs of radicalization and those found by using probability estimates for extremely rare events. It is reasoned that an ontology exists as a world three object in the real world. Full article
(This article belongs to the Special Issue Crime Prevention, Detection and Investigation Using Digital Evidence and Artificial Intelligence)
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