The development of business intelligence has enhanced the visualization of data to inform and facilitate business management and strategizing. By implementing effective data-driven techniques, this allows for advance reporting tools to cater to company-specific issues and challenges. The Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners.

Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.

Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.

The main problems that prevent fast and high-quality document processing in electronic document management systems are insufficient and unstructured information, information redundancy, and the presence of large amounts of undesirable user information. The human factor has a significant impact on the efficiency of document search. An average user is not aware of the advanced option of a query language and uses typical queries. Development of a specialized software toolkit intended for information systems and electronic document management systems can be an effective solution of the tasks listed above. Such toolkits should be based on the means and methods of automatic keyword extraction and text classification. The categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years due to the increased availability of documents in digital form and the ensuing need to organize them. Thus, research on keyword extraction, advancements in the field, and possible future solutions is of great importance in current times. Developing a Keyword Extractor and Document Classifier: Emerging Research and Opportunities presents an information extraction mechanism that can process many kinds of inputs, realize the type of text, and understand the percentage of the keywords that has to be stored. This mechanism then supports information extraction and information categorization mechanisms. This module is used to support a text summarization mechanism, which leads—with the help of the keyword extraction module—to text categorization. It employs lexical and information retrieval techniques to extract phrases from the document text that are likely to characterize it and determines the category of the retrieved text to present a summary to the users. This book is ideal for practitioners, stakeholders, researchers, academicians, and students who are interested in the development of a new keyword extractor and document classifier method.

This book devises an alternative conceptual framework to understand digital transformation in the cultural heritage sector. It achieves this by placing a high importance on the role of technology in the strategic process of modeling and developing cultural services in the digital era. The focus is on how marketing activities and customer processes are being transformed by digital technologies to create better value, which can also be communicated to customers through an engaged and personalized approach. Much of the digital debate in cultural heritage is still in infancy. Some existing studies are anecdotal and often developed within the domain of established research streams, including studies with some technological aspects addressed partially and from an episodic or periodic perspective. Moreover, the critical changes that have emerged in the cultural management landscape are yet to be highlighted. This book fills that gap and provides a perspective on the cultural heritage sector, which uses the new social and technology landscape to describe the digital transformation in cultural heritage sectors. The authors highlight an inclusive perspective that addresses marketing strategy in the digital era as a proactive, technology-enabled process by which firms collaborate with customers to jointly create, communicate, deliver, and sustain experience and value co-creation.

The integration of recent technological advances into modern business processes has allowed for greater efficiency and productivity. However, while such improvements are immensely beneficial, the modeling and coordination of these activities offers a unique set of challenges that must be addressed. Business Architectures for Risk Assessment and Strategic Planning: Emerging Research and Opportunities is a pivotal reference source for the latest research findings on the utilization of business models and value propositions in modern enterprises. Featuring extensive coverage on relevant areas such as management information systems, business planning, and value analysis, this publication is an ideal resource for information system practitioners, universities and colleges, business managers, and advanced-level students seeking the latest research on achievements in this field.

The two volume set CCIS 775 and 776 constitutes the refereed proceedings of the First International Conference on Computational Intelligence, Communications, and Business Analytics, CICBA 2017, held in Kolkata, India, in March 2017. The 90 revised full papers presented in the two volumes were carefully reviewed and selected from 276 submissions. The papers are organized in topical sections on data science and advanced data analytics; signal processing and communications; microelectronics, sensors, intelligent networks; computational forensics (privacy and security); computational intelligence in bio-computing; computational intelligence in mobile and quantum computing; intelligent data mining and data warehousing; computational intelligence.

It is expected that the metaverse will be the next generation of the internet and its online activities and will serve as the foundation for a massive online business platform. Furthermore, it will alter people's behavior, which will have an impact on businesses' decisions to choose the metaverse as their primary business platform. Metaverse Applications for New Business Models and Disruptive Innovation showcases the most recent developments in the metaverse and their implications for business and considers the challenges and best practices of implementing this technology in business. Covering topics such as augmented reality, economics, security, and business models, this reference work is ideal for business owners, managers, computer scientists, researchers, scholars, academicians, practitioners, educators, and students.

This book presents select proceedings of the Electric Power and Renewable Energy Conference 2020 (EPREC-2020). It provides rigorous discussions, case studies, and recent developments in the emerging areas of power electronics, especially, power inverter and converter, electrical drives, regulated power supplies, operation of FACTS & HVDC, etc. The readers would be benefited in enhancing their knowledge and skills in these domain areas. The book will be a valuable reference for beginners, researchers, and professionals interested in advancements in power electronics and drives.

Risk management is a vital concern in any organization. In order to succeed in the competitive modern business environment, the decision-making process must be effectively governed and managed. Research, Practices, and Innovations in Global Risk and Contingency Management is a critical scholarly resource that provides an all-encompassing holistic discussion of risk management and perception, while giving readers innovations on empirical risk-contingency management research and case studies. Featuring coverage on a broad range of topics, such as contingency planning, project management, and risk mitigation, this book is geared towards academicians, practitioners, and researchers seeking current research on risk and contingency management issues.