| _version_ | 1866901781519269888 |
|---|---|
| author | Mayur Sanga, Manjesh M,Sona P |
| author_facet | Mayur Sanga, Manjesh M,Sona P |
| contents | <p>Artificial Intelligence (AI), Machine learning (ML), Deep Learning (DL) and Neural Networks <br>(NNs) are overhauling pattern recognition and analysis in Forensic science. Their capacity to reveal complex <br>patterns within high dimensional data has advanced in individualization, classification and interpretation of <br>forensic evidence. This systematic review analyses recent research on AI, ML applications in forensic <br>pattern analysis and recognition marking key trends, obstacles and future directions based on 70 original <br>studies from 2020 to 2025, provided it follows the PRISMA 2020 framework. These techniques have <br>applications in deepfake detection, malware detection, forensic biometrics, social media forensics, forensic <br>anthropology, forensic geoscience, chemistry and device forensics. Nonetheless there are challenges in <br>integrating these techniques into operational protocols of Forensic Science Laboratories in India due to <br>infrastructure and legal admissibility issues. However, AI and ML are widening the accuracy and precision <br>of forensic pattern analysis. Future work in India, should give importance in development and practical <br>usage of ethically sound AI models, along with standardizing protocols to support legal admissibility in <br>forensic cases. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17667794 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI, Machine Learning, and Deep Learning in Forensic Pattern Analysis: A Comprehensive Review Mayur Sanga, Manjesh M,Sona P <p>Artificial Intelligence (AI), Machine learning (ML), Deep Learning (DL) and Neural Networks <br>(NNs) are overhauling pattern recognition and analysis in Forensic science. Their capacity to reveal complex <br>patterns within high dimensional data has advanced in individualization, classification and interpretation of <br>forensic evidence. This systematic review analyses recent research on AI, ML applications in forensic <br>pattern analysis and recognition marking key trends, obstacles and future directions based on 70 original <br>studies from 2020 to 2025, provided it follows the PRISMA 2020 framework. These techniques have <br>applications in deepfake detection, malware detection, forensic biometrics, social media forensics, forensic <br>anthropology, forensic geoscience, chemistry and device forensics. Nonetheless there are challenges in <br>integrating these techniques into operational protocols of Forensic Science Laboratories in India due to <br>infrastructure and legal admissibility issues. However, AI and ML are widening the accuracy and precision <br>of forensic pattern analysis. Future work in India, should give importance in development and practical <br>usage of ethically sound AI models, along with standardizing protocols to support legal admissibility in <br>forensic cases. </p> |
| title | AI, Machine Learning, and Deep Learning in Forensic Pattern Analysis: A Comprehensive Review |
| url | https://doi.org/10.5281/zenodo.17667794 |