Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

This book describes how companies can easily and pragmatically set up and realize the path to a data-driven enterprise, especially in the marketing practice, without external support and additional investments. Using a predictive intelligence (PI) ecosystem, the book first introduces and explains the most important concepts and terminology. The PI maturity model then describes the phases in which you can build a PI ecosystem in your company. The book also demonstrates a PI self-test which helps managers identify the initial steps. In addition, a blueprint for a PI tech stack is defined for the first time, showing how IT can best support the topic. Finally, the PI competency model summarizes all elements into an action model for the company. The entire book is underpinned with practical examples, and case studies show how predictive intelligence, in the spirit of data-driven management, can be used profitably in the short, medium, and long terms.

Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you're new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you're already a modeler, programmer, or manager, it will teach you crucial skills you don't yet have. This guide illuminates the discipline through realistic vignettes and intuitive data visualizations-not complex math. Thomas W. Miller, leader of Northwestern University's pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today's key applications for predictive analytics, delivering skills and knowledge to put models to work-and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively.

"What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models"--Publisher's description.

This book covers the basics of machine vision and robotics in the manufacturing industry. Major applicability of intelligent machines and robotics in the manufacturing sector are explored in three major areas of product traceability, remote product-monitoring, supply chain, logistics, and product record management. Machine Vision and Industrial Robotics in Manufacturing: Approaches, Technologies, and Applications explains advanced technologies based on Artificial Intelligence and Industrial Internet of Things related to smart manufacturing applications. The book introduces the emerging machines and robotics applications that are enabling smart factories initiatives worldwide. The chapters examine labor productivity, factory device installation, and defective product detection. The authors share modern models, emerging technologies, designs, frameworks, theories, practices, and sustainable approaches to design and implement machine vision with industrial robotics. They also examine the challenging issues associated with the leveraging of technologies related to machine vision, computer vision, robotics, Internet of Things, Industrial Internet of Things technologies, Artificial Intelligence-equipped machines, applications, and automatic techniques for intelligent manufacturing systems and smart factory infrastructure in the era of Industry 4.0 manufacturing. The authors also examine topics such as the role of existing management and production solutions, their limitations, and future directions in manufacturing industry. This book targets a mixed audience of students, engineers, scholars, researchers, academics, and professionals who are learning, researching, and working in the field of machine vision, Artificial Intelligence, Industrial Internet of Things, computer vision, and robotic technologies from different industries and economics.

This book investigates the growing influence of artificial intelligence in the marketing sphere, providing insights into how AI can be harnessed for developing more effective and efficient marketing strategies. In addition, the book will also offer a comprehensive overview of the various digital marketing tools available to entrepreneurs, discussing their features, benefits, and potential drawbacks. This will help entrepreneurs make well-informed decisions when selecting the tools most suited to their needs and objectives. It is designed to help entrepreneurs develop and implement successful strategies, leveraging the latest tools and technologies to achieve their business goals. As the digital landscape continues to evolve rapidly, this book aims to serve as a valuable resource for entrepreneurs looking to stay ahead of the curve and capitalize on new opportunities. The book's scope encompasses a wide range of topics, including customer experience, content marketing, AI strategy, and digital marketing tools.

This book shares new methodologies, technologies, and practices for resolving issues associated with leveraging AI-centric modeling, data analytics, machine learning-aided models, Internet of Things-driven applications, and cybersecurity techniques in the era of Industrial Revolution 4.0. AI-Centric Modeling and Analytics: Concepts, Technologies, and Applications focuses on how to implement solutions using models and techniques to gain insights, predict outcomes, and make informed decisions. This book presents advanced AI-centric modeling and analysis techniques that facilitate data analytics and learning in various applications. It offers fundamental concepts of advanced techniques, technologies, and tools along with the concept of real-time analysis systems. It also includes AI-centric approaches for the overall innovation, development, and implementation of business development and management systems along with a discussion of AI-centric robotic process automation systems that are useful in many government and private industries. This reference book targets a mixed audience of engineers and business analysts, researchers, professionals, and students from various fields.