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220317 ||| eng |
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|a 9781119711513
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|a 9781119711629
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|a 1119711517
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|a 1119711622
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|a 1119711614
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|a 9781119711612
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| 050 |
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4 |
|a HV6431
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| 100 |
1 |
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|a Pani, Subhendu Kumar
|e editor
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| 245 |
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|a Intelligent data analytics for terror threat prediction
|b architectures, methodologies, techniques and applications
|c edited by Subhendu Kumar Pani, Sanjay Kumar Singh, Lalit Garg, Ram Bilas Pachori, and Xiaobo Zhang
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| 260 |
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|a Hoboken, NJ
|b John Wiley & Sons, Inc.
|c 2021
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| 300 |
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|a 1 online resource
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|a Includes bibliographical references and index
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|a Rumor Detection and Tracing its Source to Prevent Cyber-Crimes on Social Media / Ravi Kishore Devarapalli, Anupam Biswas -- Internet of Things (IoT) and Machine to Machine (M2M) Communication Techniques for Cyber Crime Prediction / Jaiprakash Narain Dwivedi -- Crime Predictive Model Using Big Data Analytics / Hemanta Kumar Bhuyan, Subhendu Kumar Pani -- The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks / Sushobhan Majumdar -- Text Mining for Secure Cyber Space / Supriya Raheja, Geetika Munjal -- Analyses on Artificial Intelligence Framework to Detect Crime Pattern / R Arshath Raja, N Yuvaraj, NV Kousik -- A Biometric Technology-Based Framework for Tackling and Preventing Crimes / Ebrahim AM Alrahawe, Vikas T Humbe, GN Shinde -- Rule-Based Approach for Botnet Behavior Analysis / Supriya Raheja, Geetika Munjal, Jyoti Jangra, Rakesh Garg -- Securing Biometric Framework with Cryptanalysis / Abhishek Goel, Siddharth Gautam, Nitin Tyagi, Nikhil Sharma, Martin Sagayam -- The Role of Big Data Analysis in Increasing the Crime Prediction and Prevention Rates / Galal A AL-Rummana, Abdulrazzaq H A Al-Ahdal, GN Shinde -- Crime Pattern Detection Using Data Mining / Dipalika Das, Maya Nayak -- Attacks and Security Measures in Wireless Sensor Network / Nikhil Sharma, Ila Kaushik, Vikash Kumar Agarwal, Bharat Bhushan, Aditya Khamparia -- Large Sensing Data Flows Using Cryptic Techniques / Hemanta Kumar Bhuyan -- Cyber-Crime Prevention Methodology / Chandra Sekhar Biswal, Subhendu Kumar Pani
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| 653 |
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|a Terrorism / Prevention / http://id.loc.gov/authorities/subjects/sh00007517
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| 653 |
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|a Data mining / fast
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| 653 |
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|a Data Mining
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| 653 |
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|a Terrorism / Prevention / fast
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| 653 |
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|a Computer Communication Networks
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| 653 |
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|a Terrorisme / Prévention
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| 653 |
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|a Réseaux d'ordinateurs
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| 653 |
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|a Computer networks / fast
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| 653 |
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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| 653 |
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|a Exploration de données (Informatique)
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| 653 |
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|a Computer networks / http://id.loc.gov/authorities/subjects/sh85029513
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| 700 |
1 |
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|a Singh, Sanjay Kumar
|e editor
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| 700 |
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|a Garg, Lalit
|e editor
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 028 |
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|a 10.1002/9781119711629
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|z 1119711517
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|z 9781119711094
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|u https://learning.oreilly.com/library/view/~/9781119711094/?ar
|x Verlag
|3 Volltext
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|a 384
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|a 331
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|a 363.325/1702856312
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|a "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"--
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