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To celebrate the 270th anniversary of the De Gruyter publishing house, the company is providing permanent open access to 270 selected treasures from the De Gruyter Book Archive. Titles will be made available to anyone, anywhere at any time that might be interested. The DGBA project seeks to digitize the entire backlist of titles published since 1749 to ensure that future generations have digital access to the high-quality primary sources that De Gruyter has published over the centuries.

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The 18th IMT-GT International Conference on Mathematics, Statistics, and its Applications (ICMSA 2023) aimed to underscore the significance of data science in achieving the Sustainable Development Goals (SDGs), emphasizing the pivotal role of academics and practitioners in this endeavor. The conference sought to increase the publication output of Syiah Kuala University's faculty, enhance the research environment and foster scientific dialogue between the university's lecturers and their international counterparts, elevate the institution's global prestige, and serve as a platform for disseminating research findings to support government efforts in addressing health, environmental, economic, and other challenges. ICMSA 2023 established a high benchmark for future iterations of the conference, with the organizers committed to enhancing the event's format and extending its scope. The two-day conference drew significant interest, evidenced by the submission of 117 papers from diverse institutions and regions. This enthusiastic participation demonstrated the dynamism of the mathematics and statistics community and underscored the pertinence and critical nature of the conference themes. Following a stringent peer-review process, 35 papers were selected for publication in Sciendo, a premier academic publishing platform. Additionally, recognizing the value of a detailed conference record, the organizers opted to publish the details of 22 papers in the Applied Statistics book and 13 papers in the Applied Mathematics book, ensuring a comprehensive documentation of the conference proceedings. Conference date, place10 - 11 April 2023, Universitas Syiah Kuala, Banda Aceh, Aceh, Indonesia Conference OrganizerICMSA 2023, Statistics Department, Universitas Syiah Kuala Instructions for Authors Scientific-Committee Authors Organizing Committee

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"Boolean functions are perhaps the most basic objects of study in theoretical computer science. They also arise in other areas of mathematics, including combinatorics, statistical physics, and mathematical social choice. The field of analysis of Boolean functions seeks to understand them via their Fourier transform and other analytic methods. This text gives a thorough overview of the field, beginning with the most basic definitions and proceeding to advanced topics such as hypercontractivity and isoperimetry. Each chapter includes a "highlight application" such as Arrow's theorem from economics, the Goldreich-Levin algorithm from cryptography/learning theory, Hastad's NP-hardness of approximation results, and "sharp threshold" theorems for random graph properties. The book includes roughly 450 exercises and can be used as the basis of a one-semester graduate course. It should appeal to advanced undergraduates, graduate students, and researchers in computer science theory and related mathematical fields" --

Computer science --- Algebra, Boolean --- Informatique --- Algèbre de Boole --- Mathematics. --- Mathématiques --- Algebra, Boolean. --- COMPUTERS / General.

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"The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data mining with R: learning with case studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies: predicting algae blooms, predicting stock market returns, detecting fraudulent transactions, classifying microarray samples. With these case studies, the author supplies all necessary steps, code, and data. Resource: A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions"-- "This hands-on book uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, it covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. The main data mining processes and techniques are presented through detailed, real-world case studies. With these case studies, the author supplies all necessary steps, code, and data. Mirroring the do-it-yourself approach of the text, the supporting website provides data sets and R code"--

Programming --- Data mining --- R (Computer program language) --- R (Langage de programmation) --- Méthode statistique --- Statistical methods --- Technique de prévision --- Forecasting --- Traitement des données --- Data processing --- Application des ordinateurs --- computer applications --- Étude de cas --- case studies --- Analyse de données --- Data analysis --- Logiciel --- Computer software --- BUSINESS & ECONOMICS / Statistics --- COMPUTERS / General --- COMPUTERS / Database Management / Data Mining --- Data mining - Case studies

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The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms. .

Engineering. --- Big data. --- Data mining. --- Computational intelligence. --- Computational Intelligence. --- Data Mining and Knowledge Discovery. --- Big Data. --- Big Data/Analytics. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data sets, Large --- Large data sets --- Construction --- Industrial arts --- Technology --- Machine learning --- COMPUTERS / General. --- Data sets --- Intelligence, Computational --- Artificial intelligence --- Soft computing