Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques, and Applications - Tapa dura

Acerca del autor

Subhendu Kumar Pani received his PhD from Utkal University Odisha, India in 2013. He is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC), Bhubaneswar, India. He has published more than 50 articles in international journals, authored 5 books and edited 2 volumes.

Sanjay Kumar Singh is a professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Varanasi. He has published more than 130 international publications, 4 edited books and 2 patents.

Lalit Garg received his PhD from the University of Ulster, UK in Computing and Information Engineering. He is a senior lecturer in Computer Information Systems, University of Malta, Malta.

Ram Bilas Pachori received his PhD degree in Electrical Engineering from the Indian Institute of Technology (IIT) Kanpur, India in 2008. He is now a professor of Electrical Engineering, IIT Indore, India. He has more than 170 publications which include journal papers, conference papers, books, and book chapters.

Xiaobo Zhang obtained his Master of Computer Science, Doctor of Engineering (Control Theory and Control Engineering) and is now working in the Internet of Things Department of Automation, Guangdong University of Technology, China. He has published more than 30 journal articles, edited 3 books, and has applied for more than 40 invention patents and obtained 6 software copyrights.

De la contraportada

Few books on the market provide such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is an emerging field and research in data mining and machine learning are still in early stage of development.

Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science. Intelligent data analytics for terror threat prediction is a new era that brings tremendous opportunities and challenges due to easily available criminal data for further analysis. The aim of this data analytics is to prevent threats before they happen using classical statistical issues, machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods on various data sources including social media, GPS devices, video feeds from street cameras and license plate readers, travel and credit-card records and the news media, as well as government and propriety systems.

Intelligent Data Analytics for Terror Threat Prediction seeks to realize the nature, scope and the level of impact of present crime mining solutions across various domains and to develop novel paradigms for a more comprehensive solution. It presents innovative insights to help to obtain interventions of criminal activities, as well as emerging issues, challenges and management strategies in public safety and crime control development across the various domains.

This ground-breaking book covers:

• New AI related data analysis architectures, methodologies, and techniques and their applications to various domains. Different types of context such as text data, web data, social data, time series data, and trustworthiness are explored.
• Various robust aspects of intelligent data analytics, such as AI Framework to predict crime, predictive analytics with GIS, sentiment analysis on online social networks, and crime trends prediction using time series techniques.
• Current topics such as Internet of Things (IoT) and Machine to Machine Communication (M2M) techniques for cybercrime prediction are introduced together with applications.

Audience

Research scholars, industry professionals and postgraduate students across all engineering branches in artificial intelligence, machine learning, data mining, intelligent systems, electrical and electronics engineering, as well as homeland security.