AI resources refer to tools, applications, and databases that utilize artificial intelligence to assist with various tasks like research, content creation, data analysis, and more. These resources can be accessed online, often through library catalogs or specific platforms, and can provide powerful support for academic and professional work by enhancing efficiency and creativity. For those who prefer physical materials, the library stacks also contain books and resources on AI. These books are organized by subject and call number, and you can browse them in person, check them out, and explore AI topics in a traditional format.
A.I. finance : an experimental exercise in human and A.I. collaboration by 
Artificial Intelligence Applications in Banking and Financial Services by 
Artificial Intelligence for Audit, Forensic Accounting, and Valuation by 
Machine learning for auditors : automating fraud investigations through artificial intelligence by 
Planning and Reporting in BI-Supported Controlling by
Planning and reporting solutions in many companies still suffer from poor data quality, are insufficiently integrated and are often time and cost intensive. This practice-oriented book shows step by step how things can be done differently. It systematically shows how modern planning and reporting systems in BI-supported controlling can be set up with the use of data warehouse and big data technology and usefully supplemented with AI-supported features. For the 4th edition, the book has been comprehensively updated. The extensive controlling cockpit example has been expanded. It now contains suggestions for the areas of corporate management (operational and strategic controlling), sales, production, purchasing and project management. In addition, the latest developments in BI-supported controlling with the support of traditional and explorative BI are highlighted, including data mining, predictive analytics, artificial intelligence, RPA, chatbots, data discovery, data visualization, app technology, self-service BI and cloud computing. Further innovations concern the topics of data quality and data modeling. The final chapter is "Mobile BI", which deals with the expansion of powerful mobile analysis and planning solutions with the help of tablets, mobile phones and other mobile devices.
Automated Secure Computing for Next-Generation Systems by
AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMS This book provides cutting-edge chapters on machine-empowered solutions for next-generation systems for today's society. Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user's privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. Audience Researchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students.
Convergence of Cloud with AI for Big Data Analytics by 
Data Quality by
Discover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, including practical design patterns for remediating data quality Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Designing Workforce Management Systems for Industry 4. 0 by 
Artificial Intelligence and Cyber Security in Industry 4. 0 by 
Building AI Driven Marketing Capabilities : Understand Customer Needs and Deliver Value Through AI by 
The Use of Artificial Intelligence in Digital Marketing by 
