Intelligent data analytics for terror threat prediction: architectures, methodologies, techniques and applications /

"Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis....

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Bibliographic Details
Corporate Authors: Wiley Online Library (Online service)
Group Author: Pani, Subhendu Kumar, 1980-; Singh, Sanjay Kumar, 1963-; Garg, Lalit, 1977-; Pachori, Ram Bilas; Zhang, Xiaobo
Published: Wiley,
Publisher Address: Hoboken :
Publication Dates: 2021.
Literature type: eBook
Language: English
Subjects:
Online Access: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119711629
Summary: "Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. The aim of data analytics is to prevent threats before they happen using classical statistical issues, machine learning, artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods on various data sources, including social media, GPS devices, video feed from street cameras; and license plate readers, travel and credit card records and the news media, as well as government and proprietary systems. Intelligent data analytics ensures efficient data mining techniques to solve criminal investigations. Prediction of future terrorist attacks according to city, type of attack, target and weapon, claim mode, and motive for attack through classification techniques will facilitate the decision-making process of security organizations so as to learn from previously stored attack information; and then rate the targeted sectors/areas accordingly for security measures. By using intelligent data analytics models with multiple levels of representation, raw to higher abstract level representation can be learned at each level of the system. Algorithms based on intelligent data analytics have demonstrated great performance in a variety of areas, including data visualization, data pre-processing (fusion, editing, transformation, filtering, and sampling), data engineering, database mining techniques, tools and applications, etc"--
Item Description: Description based on print version record.
Carrier Form: 1 online resource (352 pages)
Also available in print.
Bibliography: Includes bibliographical references and index.
ISBN: 9781119711629 (electronic bk.)
9781119711094
Index Number: HV6431
CLC: TP309