EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 79 (2025) ITM Web Conf., 79 (2025) 01051 References
Error
  • The authentification system partialy failed, sorry for the inconvenience. Please try again later.
Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01051
Number of page(s) 7
DOI https://doi.org/10.1051/itmconf/20257901051
Published online 08 October 2025
  1. M. Albeedan, H. Kolivanda, R. Hammady, Designing and evaluation of a mixed reality system for crime scene investigation training: a hybrid approach. Virtual Reality 28, 127 (2024). https://doi.org/10.1007/s10055-024-01018-8 [Google Scholar]
  2. C. Roux, R. Bucht, F. Crispino, P. De Forest, C. Lennard, P. Margot, M.D. Miranda, N. NicDaeid, O. Ribaux, A. Ross, S. Willis, The Sydney declaration–Revisiting the essence of forensic science through its fundamental principles. Forensic Sci. Int. 332, 111182 (2022). https://doi.org/10.1016/j.forsciint.2022.111182 [Google Scholar]
  3. S.H. Yu, G. Thomson, V. Rinaldi, C. Rowland, N. Nic Daeid, Development of a Dundee Ground Truth imaging protocol for recording indoor crime scenes to facilitate virtual reality reconstruction. Sci. Justice 63, 238–250 (2023). https://doi.org/10.1016/j.scijus.2023.01.001 [Google Scholar]
  4. P.H. Home, D.G. Norman, K. Wade, E. Spearing, M.A. Williams, 3D scanning a crime scene to enhance juror understanding of Bloodstain Pattern Analysis evidence. Sci. Justice 64, 333–338 (2024). https://doi.org/10.1016/j.scijus.2024.04.007 [Google Scholar]
  5. A. Georgiou, P. Masters, S. Johnson, L. Feetham, UAV‐assisted real‐time evidence detection in outdoor crime scene investigations. J. Forensic Sci. 67, 1221–1232 (2022). https://doi.org/10.1111/1556-4029.15009 [Google Scholar]
  6. H. Swofford, S. Lund, H. Iyer, J. Butler, J. Soons, R. Thompson, V. Desiderio, J.P. Jones, R. Ramotowski, Inconclusive decisions and error rates in forensic science. Forensic Sci. Int. Synergy 8, 100472 (2024). https://doi.org/10.1016/j.fsisyn.2024.100472 [Google Scholar]
  7. A.S. Knes, M. de Gruijter, M.C. Zuidberg, C.J. de Poot, CSI-CSI: Comparing several investigative approaches toward crime scene improvement. Sci. Justice 64, 63–72 (2024). https://doi.org/10.1016/j.scijus.2023.11.009 [Google Scholar]
  8. H.V. Wilkins, V. Spikmans, R. Ebeyan, B. Riley, Application of augmented reality for crime scene investigation training and education. Sci. Justice 64, 289–296 (2024). https://doi.org/10.1016/j.scijus.2023.11.009 [Google Scholar]
  9. M.A. Daibes, M.R. Alsadi, M.M. Alruwaili, M.Y. Alzaatreh, M.T. Mrayyan, H.Y. Abunab, M.J. ALhemedi, Knowledge about crime scenes and evidence management among emergency medical team professionals. BMC Emerg. Med. 25, 75 (2025). https://doi.org/10.1186/s12873-025-01230-y [Google Scholar]
  10. R.J. Nathan, B. Okeleye, R. Abdullahi, W. Oyebode, Identification of ketamine and norketamine in dried bloodstains on crime-scene surfaces. Egypt. J. Forensic Sci. 15, 1 (2025). https://doi.org/10.1186/s41935-024-00418-w [Google Scholar]
  11. A. Sedik, H. Kolivand, M. Albeedan, An efficient image classification and segmentation method for crime investigation applications. Multimed. Tools Appl. 84, 19399–19423 (2025). https://doi.org/10.1007/s11042-024-19773-w [Google Scholar]
  12. S. Natha, F. Ahmed, M. Siraj, M. Lagari, M. Altamimi, A.A. Chandio, Deep BiLSTM attention model for spatial and temporal anomaly detection in video surveillance. Sensors 25, 251 (2025). https://doi.org/10.3390/s25010251 [Google Scholar]
  13. A. Rehman, T. Saba, M.Z. Khan, R. Damaševičius, S.A. Bahaj, Internet‐of‐Things‐Based Suspicious Activity Recognition Using Multimodalities of Computer Vision for Smart City Security. Secur. Commun. Netw. 2022, 8383461 (2022). https://doi.org/10.1155/2022/8383461 [Google Scholar]
  14. A. Alshalawi, W. Abdul, G. Muhammad, Fire Swin: Video Anomaly Detection Using a hybrid model with convolutional layers, fire module, and Swin transformer. J. Eng. Res. (2025). https://doi.org/10.1016/j.jer.2025.08.016 [Google Scholar]
  15. L. Yang, J. Guofan, Z. Yixin, W. Qianze, Z. Jian, R. Alizadehsani, P. Pławiak, A reinforcement learning approach combined with scope loss function for crime prediction on Twitter (X). IEEE Access. 12, 149502–149527 (2024). https://doi.org/10.1109/ACCESS.2024.3473296 [Google Scholar]
  16. UCF crime dataset Link: https://www.kaggle.com/datasets/odins0n/ucf-crime-dataset (Accessed on 07.09.2025) [Google Scholar]
  17. Chicago crime dataset Link: https://www.kaggle.com/datasets/chicago/chicago-crime (Accessed on 07.09.2025) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.