Machine Learning Forensic Evidence (15 results)

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New. In.

Hardback. Condition: New. The availability of machine-learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science.Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with multiple features, enhancing accuracy and speed in forensic analysis to potentially mitigate human errors. Algorithms have the potential to identify patterns and features in evidence such as firearms, explosives, trace evidence, narcotics, body fluids, etc. and catalogue them in various databases. Additionally, they can be useful in the reconstruction and detection of complex events, such as accidents and crimes, both during and after the event. This book provides readers with consolidated research data on the potential applications and use of machine learning for analyzing various types of evidence. Chapters focus on different methodologies of machine learning applied in different domains of forensic sciences such as biology, serology, physical sciences, fingerprints, trace evidence, ballistics, anthropology, odontology, digital forensics, chemistry and toxicology, as well as the potential use of big data analytics in forensics. Exploring recent advancements in machine learning, coverage also addresses the challenges faced by experts during routine examinations and how machine learning can help overcome these challenges.Machine Learning in Forensic Evidence Examination is a valuable resource for academics, forensic scientists, legal professionals and those working on investigations and analysis within law enforcement agencies.…

Hardcover. Condition: Brand New. 336 pages. 9.18x6.12x9.21 inches. In Stock.

Hardback. Condition: New. The availability of machine-learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science.Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with multiple features, enhancing accuracy and speed in forensic analysis to potentially mitigate human errors. Algorithms have the potential to identify patterns and features in evidence such as firearms, explosives, trace evidence, narcotics, body fluids, etc. and catalogue them in various databases. Additionally, they can be useful in the reconstruction and detection of complex events, such as accidents and crimes, both during and after the event. This book provides readers with consolidated research data on the potential applications and use of machine learning for analyzing various types of evidence. Chapters focus on different methodologies of machine learning applied in different domains of forensic sciences such as biology, serology, physical sciences, fingerprints, trace evidence, ballistics, anthropology, odontology, digital forensics, chemistry and toxicology, as well as the potential use of big data analytics in forensics. Exploring recent advancements in machine learning, coverage also addresses the challenges faced by experts during routine examinations and how machine learning can help overcome these challenges.Machine Learning in Forensic Evidence Examination is a valuable resource for academics, forensic scientists, legal professionals and those working on investigations and analysis within law enforcement agencies.…

Hardcover. Condition: new. Hardcover. The availability of machine-learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science.Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with multiple features, enhancing accuracy and speed in forensic analysis to potentially mitigate human errors. Algorithms have the potential to identify patterns and features in evidence such as firearms, explosives, trace evidence, narcotics, body fluids, etc. and catalogue them in various databases. Additionally, they can be useful in the reconstruction and detection of complex events, such as accidents and crimes, both during and after the event. This book provides readers with consolidated research data on the potential applications and use of machine learning for analyzing various types of evidence. Chapters focus on different methodologies of machine learning applied in different domains of forensic sciences such as biology, serology, physical sciences, fingerprints, trace evidence, ballistics, anthropology, odontology, digital forensics, chemistry and toxicology, as well as the potential use of big data analytics in forensics. Exploring recent advancements in machine learning, coverage also addresses the challenges faced by experts during routine examinations and how machine learning can help overcome these challenges.Machine Learning in Forensic Evidence Examination is a valuable resource for academics, forensic scientists, legal professionals and those working on investigations and analysis within law enforcement agencies. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Niha Ansari is Assistant Professor at the National Forensic Science University in Gandhinagar. She earned her Ph.D. in Forensic Science from Gujarat University, where she conducted the pioneering research `Study on Changes in Vitreous Humours conc.…

Hardcover. Condition: new. Hardcover. The availability of machine-learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science.Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with multiple features, enhancing accuracy and speed in forensic analysis to potentially mitigate human errors. Algorithms have the potential to identify patterns and features in evidence such as firearms, explosives, trace evidence, narcotics, body fluids, etc. and catalogue them in various databases. Additionally, they can be useful in the reconstruction and detection of complex events, such as accidents and crimes, both during and after the event. This book provides readers with consolidated research data on the potential applications and use of machine learning for analyzing various types of evidence. Chapters focus on different methodologies of machine learning applied in different domains of forensic sciences such as biology, serology, physical sciences, fingerprints, trace evidence, ballistics, anthropology, odontology, digital forensics, chemistry and toxicology, as well as the potential use of big data analytics in forensics. Exploring recent advancements in machine learning, coverage also addresses the challenges faced by experts during routine examinations and how machine learning can help overcome these challenges.Machine Learning in Forensic Evidence Examination is a valuable resource for academics, forensic scientists, legal professionals and those working on investigations and analysis within law enforcement agencies. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…