دانلود کتاب علوم داده، طبقه‌بندی و هوش مصنوعی برای مدل‌سازی تصمیم‌گیری

The present volume contains revised, double-blind refereed versions of selected papers presented at the conference of the International Federation of Classification Societies, “Data Science, Classification and Artificial Intelligence for Modeling Decision Making”, which was held in San Jos´e, Costa Rica, from July 15–19, 2024. The conference was organized by Central American and Caribbean Society for Classification and Data Analysis (SoCCCAD) and the School of Mathematics and the Research Centre for Pure and Applied Mathematics of the University of Costa Rica. Javier Trejos (University of Costa Rica) chaired the Scientific Programm Committee with Angela Montanari (IFCS Past-president) and Rebecca Nugent (IFCS President). Javier Trejos, Mario Villalobos and Adriana S´anchez (University of Costa Rica) chaired the Local Organizing Committee. We are grateful to the members of the Scientific Program Committee: Adalbert Wilhelm (Constructor U, Germany, GfKl), Atsuho Nakayama (Doshisha U, Japan, JCS), Bal´azs Horv´ath (U E¨otv¨os Lor´and, Hungary, MST), Berthold Lausen (Essex U, United Kingdom, BDSS), Carlos Cuevas Covarrubias (U An´ahuac, Mexico, SOCCAD), Hyunjoong Kim (Yonsei U, Korea), Jean Diatta (U La R´eunion, France, SFC), Johan´e Nienkemper-Swanepoel (Stellenbosch University, South Africa, SASAMDAG), Krzysztof Jajuga (Wroclaw U of Economics and Business, Poland, SKAD), Mark de Rooij (Leiden U, The Netherlands, VOC), Maurizio Vichi (U La Sapienza Roma, CLADAG), Michael Gallaugher (Baylor U, USA, TCS), Paula Brito (U Porto, Portugal, CLAD), Simona Korenjak-Cˇ erne (Ljubljana U, Slovenia, STAT), and Sonya Coleman (Ulster U, North Ireland, IPRCS). Aurea Gran´e and Theodore Chadjipadelis served also as representatives of SEIO-AMyC and GSDA, respectively. The IFCS 2024 Conference was organized simultaneously with the Latin American Conference on Statistical Computing (LACSC), an annual meeting of the Latin American Regional Section of the International Association for Statistical Computing (IASC). IFCS and IASC have an agreement of cooperation since 2014. Over 120 scholars from 25 countries across the globe attended the conference. More than 94 contributions were organized into special sessions, thematic tracks, contributed paper sessions, one poster session, four tutorials, and eight keynote lectures. Also, the IFCS Medal was granted in a special ceremony. The keynote lectures were addressed by Patrick J.F. Groenen (Rotterdam University, The Netherlands), Sugnet Lubbe (Stellenbosch University, South Africa), Arnoldo M¨uller-Molina (Chicago University, USA), Beatriz Cobo (Granada University, Spain), Marcela Alfaro (LACSC – The University of California in Santa Cruz, USA), and Marcos Matabuena (LACSC – Harvard University, USA). The conference program includes four tutorials: “Logistic Multidimensional Data Analysis” by Mark De Rooij (Leiden University, The Netherlands), “Methods on Artificial Intelligence” by Theodore Chadjupadelis (Aristotle University of Thessaloniki, Greece), “Reproducible Data Analysis” by Marcela Alfaro, and “ Statistical Science Meets Digital Health. Distributional Data Analysis in Digital Health” by Marcos Matabuena. This book gathers modern methods and real-world applications in data science, classification, and artificial intelligence related to modeling decision making and covers a wide range of research topics and application areas. The book is intended for researchers and practitioners who seek the latest developments and applications in the field of data science and classification. The topics span a wide range of areas within statistics and data science. They present novel methods and innovative applications in fields such as anomaly detection in public procurement processes, multivariate functional data clustering, air pollution prediction, benchmark generation for probabilistic planning, recommendation systems based on symbolic data analysis, and methods for clustering mixed-type data. Furthermore, advanced statistical concepts are explored, including Vapnik- Chervonenkis dimensionality, Riemannian statistics, hypothesis testing for intervalvalued data, and mixed models. Machine learning techniques are applied to predict soil bacterial and fungal communities, classify electoral behavior and political competition, and assess corrosion degradation in mining pipelines. The diversity of topics reflects the ongoing advancement and interdisciplinary nature of statistical and data science research, as well as its application across various fields and sectors. These studies contribute to the development of robust methodologies and efficient computational tools to address complex challenges in the era of big data.