The use of machine learning for the determination of a type/model of firearms by the characteristics on cartridge cases.

Cartridge cases are commonly collected at crime scenes involving firearms. One of the stages in forensic examination is the determination of the type and model of firearms based on the class characteristics of these cartridge cases. A firearm examiner evaluates the class characteristics on the basis of their knowledge and experience, and by referring to collections of cartridge cases representing class characteristics of different firearms, special databases and reference books.

Machine learning applications in forensic DNA profiling: A critical review.

Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone.

Examination of the possibility to use Siamese networks for the comparison of firing pin marks.

One of the most discussed issues in forensic firearms identification is the subjectivity of conclusions. The main part of firearms examiners' work is to make a microscopic comparison of the marks on cartridge cases and bullets. In this process, examiners have to decide if the quantity and the quality of the observed characteristics are sufficient for identification.

Firearms identification by the acoustic signals of their mechanisms.

During the operation of firearms different sounds are made. The sound of a shot, the sound of a flying bullet as well as the sound of the bullet's impact have all been investigated. However, less attention has been given to the sound of the mechanical operation of firearms.