Elsevier has released four new books on the subject of Artificial Intelligence. Below, the publisher provides an overview of each of the books
Sentiment Analysis in Social Networks
by Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina and Bing Liu
This book begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions.
The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature.
It takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies.
The book provides insights into opinion spamming, reasoning and social network analysis, and shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences.
Quantum Inspired Computational Intelligence Research and Applications
by Siddhartha Bhattacharyya, Ujjwal Maulik and Paramartha Dutta
This book explores the latest quantum computational intelligence approaches, initiatives and applications in computing, engineering, science and business.
It looks at this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence-or soft computing-is conjoined with quantum computing to achieve this objective.
The models covered can be applied to any endeavor which handles complex and meaningful information.
The book is written for academic and corporate researchers in artificial intelligence, computational intelligence, applied computing, data science, data mining, signal processing, pattern recognition, machine learning and quantum computing.
This reference book reviews previous text summarization approaches in a multi-dimensional category space, introduces a multi-dimensional methodology for research and development, unveils the basic characteristics and principles of language use and understanding, investigates some fundamental mechanisms of summarization, studies dimensions on representations, and proposes a multi-dimensional evaluation mechanism.
Investigation extends to incorporating pictures into summary and to the summarization of videos, graphs and pictures, and converges to a general summarization method.
Some basic behaviors of summarization are studied in the complex cyber-physical-social space, and a creative summarization mechanism is proposed.
This book offers the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives.
By providing three proposed ensemble approaches of temporal data clustering, it presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice.
The book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods.
As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem.