Data Analysis Techniques and Applications
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.
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Persons
Luis Carlos Méndez-González is a research professor in the Department of Industrial Engineering and Manufacturing at the Institute of Engineering and Technology, Autonomous University of Ciudad Juárez (UACJ). He is recognized as a member of Mexico's National System of Researchers (SNII) by the SECIHTI, and has extensive experience in the automotive, medical, and manufacturing industries. His research interests and journal publications cover a wide range of topics, including reliability analysis, statistical modeling, automation, machine learning, and data science.
Isidro Jesús González-Hernández received his Ph.D. in Strategic Planning and Technology Management from the Popular Autonomous University of the State of Puebla (UPAEP). He is a professor and researcher with Bachelor's and Postgraduate degrees in Industrial Engineering from the Autonomous University of the State of Hidalgo (UAEH). He is a National Researcher within the National System of Researchers (SNII) of SECIHTI in Mexico. His research interests include the design, modeling, optimization, and simulation of supply chains and logistics systems. He also focuses on statistical modeling for reliability analysis.
Manuel Iván Rodríguez Borbón is a researcher and consultant with expertise in industrial engineering, statistics, and data analysis. He holds a Ph.D. in Industrial Engineering from New Mexico State University (NMSU), where his doctoral thesis focused on a multi-factor reliability model with a Bayesian application to accelerated life testing. He also holds a Master of Science in Statistics from the University of Texas at El Paso and a Bachelor of Science degree in Industrial Engineering from the Instituto Tecnológico de Ciudad Juarez, Mexico.
Content
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.
