Matlab For Data Science Programming Language Elements And Applications
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MATLAB FOR DATA SCIENCE. PROGRAMMING LANGUAGE: ELEMENTS AND APPLICATIONS
MATLAB is a programming language and development environment designed primarily for numerical computation, data analysis, and visualization. It is widely used in engineering, mathematics, physics, and other scientific disciplines. MATLAB has several fundamental elements that define it as a programming language and that are the main focus of this book. Throughout the chapters of this book, you will work with variables, operators, data types, control structures, functions, arrays, matrices, graphics, and visualization elements. From these elements, algorithms are built that allow you to work efficiently in numerical computation and solve problems related to algebra, mathematical analysis, geometry, engineering, and other scientific computing subjects.
Formalizing Natural Languages with NooJ 2019 and Its Natural Language Processing Applications
This book constitutes the refereed proceedings of the 13th International Conference, NooJ 2019, held in Hammamet, Tunisia, in June 2019. NooJ is a linguistic development environment that allows linguists to formalize several levels of linguistic phenomena. NooJ provides linguists with tools to develop dictionaries, regular grammars, context-free grammars, context-sensitive grammars and unrestricted grammars as well as their graphical equivalent to formalize each linguistic phenomenon. The 18 full papers presented were carefully reviewed and selected from 54 submissions. The papers are organized in the following tracks: Development of Linguistic Resources, Natural Language Processing Applications, NooJ for the Digital Humanities.
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery
This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.