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Full Description
This book presents innovative techniques in data analysis and related branches for solving problems in different areas of science. The authors present multiple techniques in data science and its applications, such as multi-objective optimization, statistical analysis, statistical process, and design of experiments for industry, artificial intelligence and machine learning, big data analytics, and stochastic processes. The methodologies used in the case studies allow practitioners to replicate and adapt the proposed models and techniques to new areas of analysis. At the same time, the book allows students from different areas to see how the implementation of data analysis can help understand phenomena in the real world and how, through a structured methodology, it is possible to derive conclusions to different problems that arise in many areas of science.
Contents
Introduction.- Advancements in the Application of Stochastic Systems in Environmental Sciences.- Aggregate production planning in a reverse logistics context considering random demand and service level.- Day of the Dead Before and After the Covid -19 Pandemic in Mixquic, Mexico City.- Machine learning and deep learning-based models applied in fundamental electrochemical devices.- Bilevel Approach for Discovering Latent Variables in Bayesian Networks.- Design a novel algorithm for Fault Detection, Diagnosis, and Monitoring based on a Dedicated Observer Scheme and Multi-Objective Optimization of Bioethanol Production.- Risks in the Supply Chain of the Persian Lemon: Mexican Case.- Conclusion.
