Computing Predictive Analytics Business Intelligence And Economics

This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling. Applications for management science and IT have been of interest areas for business schools and computing experts during recent years. Among the areas that are being treated are modern analytics, heterogeneous computing, business intelligence, ERP (enterprise resource planning), and decision science. Consumers have been pledging their love for data visualizations for a while now, and data is the area being explored, such as B2B and EC (E-commerce), E-business and the Intelligent Web, CRM (customer relationship management), infrastructures, and more. The digitization implications of these many new applications are described and explored in this informative volume.

This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling. Applications for management science and IT have been of interest areas for business schools and computing experts during recent years. Among the areas that are being treated are modern analytics, heterogeneous computing, business intelligence, ERP (enterprise resource planning), and decision science. Consumers have been pledging their love for data visualizations for a while now, and data is the area being explored, such as B2B and EC (E-commerce), E-business and the Intelligent Web, CRM (customer relationship management), infrastructures, and more. The digitization implications of these many new applications are described and explored in this informative volume.

With the rapid development of economic globalization and information technology, the field of economic forecasting continues its expeditious advancement, providing business and government with applicable technologies. This book discusses various business intelligence techniques including neural networks, support vector machine, genetic programming, clustering analysis, TEI@I, fuzzy systems, text mining, and many more. It serves as a valuable reference for professionals and researchers interested in BI technologies and their practical applications in economic forecasting, as well as policy makers in business organizations and governments.

Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.

Corporate success has been changed by the importance of new developments in Business Analytics (BA) and furthermore by the support of computational intelligence- based techniques. This book opens a new avenues in these subjects, identifies key developments and opportunities. The book will be of interest for students, researchers and professionals to identify innovative ways delivered by Business Analytics based on computational intelligence solutions. They help elicit information, handle knowledge and support decision-making for more informed and reliable decisions even under high uncertainty environments.Computational Intelligence for Business Analytics has collected the latest technological innovations in the field of BA to improve business models related to Group Decision-Making, Forecasting, Risk Management, Knowledge Discovery, Data Breach Detection, Social Well-Being, among other key topics related to this field.

This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains.

Focusing on the cutting-edge applications of AI cognitive computing from neuromorphic to quantum cognition as applied to AI business analytics, this new volume explores AI’s importance in managing cognitive processes along with ontological modeling concepts for venturing into new business frontiers. The volume presents a selection of significant new accomplishments in the areas of AI cognitive computing ranging from neurocognition perception and decision-making in the human brain—combining neurocognitive techniques and effective computing—to basic facial recognition computing models. Topics include: Agent neurocomputing techniques for facial expression recognition Computing haptic motion and ontology epistemic Characterizations of morph schemas for visual analytics Learning and perceptive computing Functional and structural neuroimaging modeling Observed links between facial recognition and affective emotional processes Interaction of cognitive and emotional processes during social decision-making Neurocognitive processing of emotional facial expressions in individuals Neurocognitive affective system for emotive robot androids Virtual reality-based affect adaptive neuromorphic computing Executive surveys indicate that cognitive adoption is very important in business strategy for success and to remain competitive. Employing cognitive-based processes provides the way to get the right information in the right hands at the right time, which is the key to winning in the digital era and to driving business value that emphasizes competitive differentiation. Several chapters of the volume address the goal of using cognitive technology to improve search capabilities, to provide personalized customer service in business and in health and wellness, and to create better workflow management. Key features: Looks at the newest frontiers on very popular AI and analytics topics Discusses new techniques for visual analytics and data filtering Shows how AI and cognitive science merges with quantum neurocognitive computing Presents ontology models with ontology preservation data filtering techniques Provides a cross-transposition on AI and digitizations for business model innovations Artificial Intelligence and Computing Logic: Cognitive Technology for AI Business Analytics is a valuable resource that informs businesses and other enterprises the value of artificial intelligence and computing logic applications.

If you want to learn about business intelligence and predictive analytics, then keep reading... Two manuscripts in one book: Business Intelligence: An Essential Beginner's Guide to BI, Big Data, Artificial Intelligence, Cybersecurity, Machine Learning, Data Science, Data Analytics, Social Media and Internet Marketing Predictive Analytics: The Secret to Predicting Future Events Using Big Data and Data Science Techniques Such as Data Mining, Predictive Modelling, Statistics, Data Analysis, and Machine Learning In the modern business world, the pace of action continues to quicken. Businesses need to be able to get actionable insights from their data in order to make the right decisions to act rapidly and effectively. In this book, you will be introduced to the concept of business intelligence. Business intelligence is closely connected to data, and so as part of our journey, we are going to see how modern companies are using big data, together with artificial intelligence, machine learning, and pattern recognition in order to build systems of data-driven decision making. After we get a handle on what business intelligence is, we will learn how it ties together with the next computer revolution (happening right before our eyes). We will discuss the use of artificial intelligence, machine learning, and pattern recognition to retrieve insights from the large amounts of data that companies are routinely collecting, storing, and using. We will also explore the roles that social media and internet marketing are playing in the growth of business intelligence, and how companies like Facebook are helping other companies act on data in powerful ways. Some of the topics covered in part 1 of this book include: An Introduction to BI, Big Data, AI, and More An Overview of Business Intelligence BI and AI BI and Big Data BI And Machine Learning Data Science Cybersecurity BI and Data Mining BI and Social Media BI and Internet Marketing And much much more! Some of the topics covered in part 2 of this book include: The basics of predictive analysis. How to predict events that are going to happen in the future with big data and data mining. How to predict events that are going to happen in the future with the help of data analysis and statistics. A look at machine learning and how this process can help make predictions. How to avoid prediction traps, avoid bias, and make the best decisions with this analysis. Some of the top reasons to implement this kind of analysis in your business. The steps you can take to create your own predictive analysis model. And much, much more! So if you want to learn more about business intelligence and predictive analytics, click the "add to cart" button!