Название: Artificial Intelligence in Forensic Science: An Emerging Technology in Criminal Investigation SystemsАвтор: Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, Naveen Kumar
Издательство: CRC Press
Год: 2024
Страниц: 206
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Artificial Intelligence in Forensic Scienceaddresses the current and emerging opportunities being utilized to apply modern Artificial Intelligence (AI) technologies to current forensic and investigation practices. The book also showcases the increasing benefits of AI where and when it can be applied to various techniques and forensic disciplines. The increasing rate of sophisticated crimes has increased the opportunity and need for the forensic field to explore a variety of emerging technologies to counter criminals—and artificial intelligence is no exception. There are many current investigative challenges that, with ingenuity and application, can be helped with the application of AI, especially in the digital forensic and cyber-crime arena. The book also explains many practical studies that have been carried out to test AI technologies in crime detection, uncovering evidence, and identifying perpetrators. In the last decade, the use of AI is common in many fields and now is an ideal time to look at the various ways AI can be integrated into judicial, forensic, and criminal cases to better collect and analyze evidence, thereby improving outcomes.
Artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA) are examples of Machine Learning approaches that play a significant role in providing unconventional solutions for fingerprint identification challenges. Making a feature vector and tutoring (teaching) the computer how to process it in accordance with predetermined criteria are the objectives of these techniques. This chapter covers the AI-based models used in fingerprint biometrics, latent fingerprint analysis, and various software where artificial intelligence has made it more efficient for forensic scientists to identify fingerprints. Possible future prospects of AI in fingerprint identification and development have also been discussed. Many algorithms have been developed, with some of them involving the use of a software procedure that incorporates an artificial neural network. Automatic Fingerprint Verification (AFV) uses a Learning Vector Quantization (LVQ) neural network (NN).
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