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This unique compendium provides a comprehensive overview of the state-of-the-art methods in the extremely "hot" area of online propaganda detection and monitoring. It discusses the difficulties of developing automated methods and systems for online propaganda and misinformation detection, along with the potential contributions of machine learning techniques.The presented methods are built upon the latest AI and Natural Language Processing technologies. Case studies refer to recent events such as the COVID-19 pandemic and the Russian-Ukrainian military conflict.This useful reference text benefits academics, researchers, professionals and graduate students in AI, NLP, machine learning and information sciences.

An inadequate infrastructure for software testing is causing major losses to the world economy. The characteristics of software quality problems are quite similar to other tasks successfully tackled by artificial intelligence techniques. The aims of this book are to present state-of-the-art applications of artificial intelligence and data mining methods to quality assurance of complex software systems, and to encourage further research in this important and challenging area.

As became apparent after the tragic events of September 11, 2001, terrorist groups are increasingly using the Internet as a communication and propaganda tool where they can safely communicate with their affiliates, coordinate action plans, raise funds, and introduce new supporters to their networks. This is evident from the large number of web sites run by different terrorist organizations, though the URLs and geographical locations of these web sites are frequently moved around the globe. The wide use of the Internet by terrorists makes some people think that the risk of a major cyber-attack against the communication infrastructure is low. However, this situation may change abruptly once the terrorists decide that the Net does not serve their purposes anymore and, like any other invention of our civilization, deserves destruction.Fighting Terror in Cyberspace is a unique volume, which provides, for the first time, a comprehensive overview of terrorist threats in cyberspace along with state-of-the-art tools and technologies that can deal with these threats in the present and in the future. The book covers several key topics in cyber warfare such as terrorist use of the Internet, the Cyber Jihad, data mining tools and techniques of terrorist detection on the web, analysis and detection of terror financing, and automated identification of terrorist web sites in multiple languages. The contributors include leading researchers on international terrorism, as well as distinguished experts in information security and cyber intelligence. This book represents a valuable source of information for academic researchers, law enforcement and intelligence experts, and industry consultants who are involved in detection, analysis, and prevention of terrorist activities on the Internet.

This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining.The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic.