Academic Journal
Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation.
| Τίτλος: | Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation. |
|---|---|
| Συγγραφείς: | Miah, Mohammed Nazmul Islam, Uddin, Joshim, Kakumani, Manohar |
| Πηγή: | Frontiers in Computer Science & Artificial Intelligence; 2026, Vol. 5 Issue 8, p43-57, 15p |
| Θεματικοί όροι: | Machine learning, Ensemble learning, Prediction models, Discrete event simulation, Artificial intelligence, Crime scene searches, Forensic sciences, Deep learning |
| Περίληψη: | Crime scene reconstruction remains a critical yet complex component of forensic investigation, requiring the integration of heterogeneous evidence sources to infer plausible sequences of criminal events. Traditional reconstruction methods rely heavily on expert interpretation, which can introduce subjectivity and limitations when dealing with large-scale or incomplete forensic data. This study proposes a machine learning-based framework for automated crime scene reconstruction, integrating predictive analytics and scenario simulation to enhance investigative decision-making. The framework leverages structured forensic features, including evidence distribution, temporal response characteristics, and scene complexity indicators, to model reconstruction outcomes using a combination of classical machine learning, ensemble methods, and deep learning architectures. In addition, sequential models and hybrid neural networks are employed to capture temporal dependencies and local evidence patterns, enabling more robust interpretation of event sequences. Scenario simulation is incorporated to generate multiple plausible crime narratives under varying evidence conditions, supporting probabilistic reasoning in uncertain investigative environments. The findings indicate that advanced ensemble and hybrid deep learning models offer superior capability for capturing nonlinear and temporal relationships in forensic datasets, leading to more consistent and reliable reconstruction outcomes. The proposed approach demonstrates the potential of artificial intelligence to augment forensic investigation processes by improving reconstruction accuracy, enhancing scenario exploration, and supporting more structured evidence interpretation in complex crime scenes. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Computer Science & Artificial Intelligence is the property of Al-Kindi Center for Research & Development and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 196872203 RelevancyScore: 1067 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1066.68798828125 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Miah%2C+Mohammed+Nazmul+Islam%22">Miah, Mohammed Nazmul Islam</searchLink><br /><searchLink fieldCode="AR" term="%22Uddin%2C+Joshim%22">Uddin, Joshim</searchLink><br /><searchLink fieldCode="AR" term="%22Kakumani%2C+Manohar%22">Kakumani, Manohar</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Computer Science & Artificial Intelligence; 2026, Vol. 5 Issue 8, p43-57, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+event+simulation%22">Discrete event simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Crime+scene+searches%22">Crime scene searches</searchLink><br /><searchLink fieldCode="DE" term="%22Forensic+sciences%22">Forensic sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Crime scene reconstruction remains a critical yet complex component of forensic investigation, requiring the integration of heterogeneous evidence sources to infer plausible sequences of criminal events. Traditional reconstruction methods rely heavily on expert interpretation, which can introduce subjectivity and limitations when dealing with large-scale or incomplete forensic data. This study proposes a machine learning-based framework for automated crime scene reconstruction, integrating predictive analytics and scenario simulation to enhance investigative decision-making. The framework leverages structured forensic features, including evidence distribution, temporal response characteristics, and scene complexity indicators, to model reconstruction outcomes using a combination of classical machine learning, ensemble methods, and deep learning architectures. In addition, sequential models and hybrid neural networks are employed to capture temporal dependencies and local evidence patterns, enabling more robust interpretation of event sequences. Scenario simulation is incorporated to generate multiple plausible crime narratives under varying evidence conditions, supporting probabilistic reasoning in uncertain investigative environments. The findings indicate that advanced ensemble and hybrid deep learning models offer superior capability for capturing nonlinear and temporal relationships in forensic datasets, leading to more consistent and reliable reconstruction outcomes. The proposed approach demonstrates the potential of artificial intelligence to augment forensic investigation processes by improving reconstruction accuracy, enhancing scenario exploration, and supporting more structured evidence interpretation in complex crime scenes. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Computer Science & Artificial Intelligence is the property of Al-Kindi Center for Research & Development and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=196872203 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.32996/fcsai.2026.5.8.4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 43 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Discrete event simulation Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Crime scene searches Type: general – SubjectFull: Forensic sciences Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Miah, Mohammed Nazmul Islam – PersonEntity: Name: NameFull: Uddin, Joshim – PersonEntity: Name: NameFull: Kakumani, Manohar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 29788048 Numbering: – Type: volume Value: 5 – Type: issue Value: 8 Titles: – TitleFull: Frontiers in Computer Science & Artificial Intelligence Type: main |
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