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This book constitutes the refereed proceedings of the 9th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2021, held in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 62 full papers included in this book were carefully reviewed and selected from 215 submissions. They were organized in topical sections as follows: image processing; text, voice and video processing; bioinformatics; deep learning; medical image processing; theory and models; feature selection; biometrics and dermoscopy; facial image analysis; object and event detection; pattern recognition; machine learning applications.

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that'artificial intelligence is included.'We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools?Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.