Machine Learning Forensics for Law Enforcement, Security, and Intelligence - Mena, Jesus
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Increasingly, crimes and fraud are digital in nature, occurring at breakneck speed and encompassing large volumes of data. To combat this unlawful activity, knowledge about the use of machine learning technology and software is critical. This volume integrates an assortment of deductive and instructive tools, techniques, and technologies to arm professionals with the tools they need to be prepared and stay ahead of the game. It is a practical guide on how to conduct forensic investigations using self-organizing clustering map (SOM) neural networks, text extraction, and rule generating software to "interrogate the evidence."…mehr

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Produktbeschreibung
Increasingly, crimes and fraud are digital in nature, occurring at breakneck speed and encompassing large volumes of data. To combat this unlawful activity, knowledge about the use of machine learning technology and software is critical. This volume integrates an assortment of deductive and instructive tools, techniques, and technologies to arm professionals with the tools they need to be prepared and stay ahead of the game. It is a practical guide on how to conduct forensic investigations using self-organizing clustering map (SOM) neural networks, text extraction, and rule generating software to "interrogate the evidence."
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Autorenporträt
Jesús Mena is a former Internal Revenue Service Artificial Intelligence specialist and the author of numerous data mining, web analytics, law enforcement, homeland security, forensic, and marketing books. Mena has also written dozens of articles and consulted with several businesses and governmental agencies. He has over 20 years' experience in expert systems, rule induction, decision trees, neural networks, self-organizing maps, regression, visualization, and machine learning and has worked on data mining projects involving clustering, segmentation, classification, profiling and personalization with government, web, retail, insurance, credit card, financial and healthcare data sets. He has worked, written, and lectured on various behavioral analytics and social networking techniques, personalization mechanisms, web and mobile networks, real-time psychographics, tracking and profiling engines, log analyzing tools, packet sniffers, voice and text recognition software, geolocation and behavioral targeting systems, real-time streaming analytical software, ensemble techniques, and digital fingerprinting.