The world today is becoming a highly connected place. Firms, consumers and the devices they use are increasingly part of a complex, global network of connected entities. These networks represent a gold mine for marketing scholars that may be interested in developing a better understanding of consumer behavior, and for practitioners who are keen to discover new ways of gaining and retaining customers. This cumulative dissertation focuses on the use of network analysis to generate and analyze novel data in marketing. The work moves beyond the scope of traditional social network analysis to consider networks composed of different types of nodes at varying levels of granularity. The key aim is to demonstrate that network analysis can be used to investigate novel explanatory and outcome variables that hold contextual meaning and can deepen our understanding of the research question at hand. Essay 1 develops a predictive method based on product networks to identify customer projects in a retail setting. Essays 2 and 3 show the value of network analysis in new product development by recasting product ideas as networks of their constituent features. Essay 4 leverages network thinking to enable novel analyses of the fragmented and anonymized event data that will be generated in abundance by the Internet of Things. Ultimately, the essays showcase a selection of advanced applications of network analysis in marketing science that are arguably of value to scholars as well as practitioners, and likely to gain in relevance in the future.

Systematically introduces self-healing control theory for distribution networks, rigorously supported by simulations and applications • A comprehensive introduction to self-healing control for distribution networks • Details the construction of self-healing control systems with simulations and applications • Provides key principles for new generation protective relay and network protection • Demonstrates how to monitor and manage system performance • Highlights practical implementation of self-healing control technologies, backed by rigorous research data and simulations

Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.

The study of social networks was originated in social and business communities. In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social Web sites, email logs, phone logs and instant messaging systems, which are widely analyzed using graph theory and machine learning techniques. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of SNAM in academia, politics, homeland security and business. This follows the pattern of known entities of our society that have evolved into networks in which actors are increasingly dependent on their structural embedding General areas of interest to the book include information science and mathematics, communication studies, business and organizational studies, sociology, psychology, anthropology, applied linguistics, biology and medicine.

This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends. In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

Informatics and technology have long been indispensable to the provision of healthcare and their importance continues to grow in this field. This book presents the 65 full papers presented at the 13th annual International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH 2015), held in Athens, Greece, in July 2015. The conference attracts scientists and practitioners from all continents and treats the field of biomedical informatics in a very broad framework, examining the research and applications outcomes of informatics from cell to population, and covering a number of technologies such as imaging, sensors and biomedical equipment as well as management and organizational subjects such as legal and social issues. The conference also aims to set research priorities in health informatics. This overview of current research and development will be of interest to all those whose work involves the use of biomedical informatics in the planning, provision and management of healthcare.

In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation. Each chapter illustrates a specific technique, from Stochastic Point Processes on a Network and Network Voronoi Diagrams, to Network K-function and Point Density Estimation Methods, and the Network Huff Model. The authors also discuss and illustrate the undertaking of the statistical tests described in a Geographical Information System (GIS) environment as well as demonstrating the user-friendly free software package SANET. Spatial Analysis Along Networks: Presents a much-needed practical guide to statistical spatial analysis of events on and alongside a network, in a logical, user-friendly order. Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics. Dedicates a separate chapter to each of the major techniques involved. Demonstrates the practicalities of undertaking the tests described in the book, using a GIS. Is supported by a supplementary website, providing readers with a link to the free software package SANET, so they can execute the statistical methods described in the book. Students and researchers studying spatial statistics, spatial analysis, geography, GIS, OR, traffic accident analysis, criminology, retail marketing, facility management and ecology will benefit from this book.

The field of agent and multi-agent systems is concerned with the development and evaluation of sophisticated, AI-based, problem solving and control architectures for both single and multi-agent systems. This book presents the proceedings of the 7th KES Conference on Agent and Multi-agent Systems – Technologies and Applications (KES-AMSTA 2013), held in Hue City, Vietnam, in May 2013. The KES-AMSTA 2013 conference provides an internationally respected forum for scientific research in the technologies and applications of agent and multi-agent systems. In all, 44 papers were selected for oral presentation and publication in this volume. Special attention is paid to the feature topics of intelligent technologies and applications in the area of e-health, social networking, self-organizing systems, economics and trust management. Other topics covered include: agent oriented software engineering; beliefs engineering; desires and intentions representation; agent cooperation, coordination, negotiation, organization and communication; distributed problem-solving; specification of agent communication languages; formalization of ontologies; and conversational agents. The book highlights new trends and challenges in agent and multi-agent research, and will be of interest to the research community working in the fields of artificial intelligence, collective computational intelligence, robotics, dialogue systems and, in particular, agent and multi-agent systems, technologies and applications.

Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer programming.

This volume constitutes the thoroughly refereed conference proceedings of the 26th International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2013, held in Amsterdam, The Netherlands, in June 2013. The total of 71 papers selected for the proceedings were carefully reviewed and selected from 185 submissions. The papers focus on the following topics: auctions and negotiation, cognitive modeling, crowd behavior modeling, distributed systems and networks, evolutionary algorithms, knowledge representation and reasoning, pattern recognition, planning, problem solving, robotics, text mining, advances in recommender systems, business process intelligence, decision support for safety-related systems, innovations in intelligent computation and applications, intelligent image and signal processing, and machine learning methods applied to manufacturing processes and production systems.