Titlebook: AIxIA 2020 – Advances in Artificial Intelligence; XIXth International Matteo Baldoni,Stefania Bandini Conference proceedings 2021 Springer

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algebra which is not included here) and, at the same time, as an introduction to the use of Maple to explore the algorithms discussed. Even for readers not familiar with these subjects we hope that sufficient information is included here to allow them to profitably read the rest of the book. We incl

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Introducing General Argumentation Frameworks and Their Usedes a gentle introduction to but a few of the many external resources that may interest those who work in biostatistics and use R to engage in data science activities. With experience, data scientists develop a personal collection of data resources, typically those resources associated with specific

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Towards an Implementation of a Concurrent Language for Argumentationat can be applied in computer science, data science/data analytics,?and information technology programs as well as for internships and research experiences. This book is?accessible to a wide variety of students. By drawing together content normally spread across upper level?computer science courses,

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Solving Operating Room Scheduling Problems with Surgical Teams via?Answer Set Programmingng clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’increasing use.? The authors? present deep learning case studies on all data described..Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep lear

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Optimal Control of Point-to-Point Navigation in Turbulent Time Dependent Flows Using Reinforcement Lknowledge. Every chapter includes a set of exercises based on what it covered to further cement your learning. No specialized knowledge of mathematics is expected beyond the basics, so it is perfect for novices..What You Will Learn.UnderstandLambda calculus and dependent types.Gaininsight into funct

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