The need for tailored data for machine learning models is often unsatisfied, as it is considered too much of a risk in the real-world context. Synthetic data, an algorithmically birthed counterpart to operational data, is the linchpin for overcoming constraints associated with sensitive or regulated information. In high-dimensional data, where the dimensions of features and variables often surpass the number of available observations, the emergence of synthetic data heralds a transformation. Applications of Synthetic High Dimensional Data delves into the algorithms and applications underpinning the creation of synthetic data, which surpass the capabilities of authentic datasets in many cases. Beyond mere mimicry, synthetic data takes center stage in prioritizing the mathematical domain, becoming the crucible for training robust machine learning models. It serves not only as a simulation but also as a theoretical entity, permitting the consideration of unforeseen variables and facilitating fundamental problem-solving. This book navigates the multifaceted advantages of synthetic data, illuminating its role in protecting the privacy and confidentiality of authentic data. It also underscores the controlled generation of synthetic data as a mechanism to safeguard private information while maintaining a controlled resemblance to real-world datasets. This controlled generation ensures the preservation of privacy and facilitates learning across datasets, which is crucial when dealing with incomplete, scarce, or biased data. Ideal for researchers, professors, practitioners, faculty members, students, and online readers, this book transcends theoretical discourse.

The field of statistical machine learning has seen a surge in popularity for feature selection methods for ultra-high dimensional datasets due to their huge applicability in various scientific domains ranging from genetics to astronomy. These applications typically involve a vast number of potential features, and a quantitative response or outcome variable. Also, often it is observed/hypothesized that only a small subset of these features are truly associated with the response. Any traditional feature selection algorithm is motivated by the need to uncover the true sparsity pattern, buried in the ultra-high dimensional data setting. However, these methods may lead to high false discoveries providing poor scientific insights into the underlying relationship. The error-controlled methods are designed to address this issue by controlling the expected proportion of falsely identified features among the selected ones. In this thesis, we develop and study two novel feature selection methods for ultrahigh dimensional data with False Discovery Rate (FDR) control with a real-world application in the context of diffusion magnetic resonance imaging (DMRI) tractography data.In the first chapter, we propose a p-value-free FDR controlling method for feature selection. Most of the state-of-the-art methods in the literature for controlling FDR rely on p-value, which depends on specific assumptions on the data distribution and may be questionable in some high-dimensional settings. To surpass this problem, we propose a 'screening \\& cleaning' strategy consisting of assigning importance scores to the predictors, followed by constructing an estimate of the FDR. We study the theoretical properties of the method and demonstrate its superior performance compared to existing methods in an extensive simulation study. Finally, we apply the method to a gene expression dataset and identify important genes associated with drug sensitivity.In the second chapter, We extend the feature selection method from a linear model to a non-linear and non-parametric setting by utilizing the Deep Learning (DL) framework. The DL has been at the center of analytics in recent years due to its impressive empirical success in analyzing complex data objects. Despite this success, most existing tools behave like black-box machines, thus the increasing interest in interpretable, reliable, and robust deep learning models applicable to a broad class of applications. Feature-selected deep learning has emerged as a promising tool in this realm. However, the recent developments do not accommodate ultra-high dimensional and highly correlated features or high noise levels. In this article, we propose a novel screening and cleaning method with the aid of deep learning for a data-adaptive multi-resolutional discovery of highly correlated predictors with a controlled FDR. Extensive empirical evaluations over a wide range of simulated scenarios and several real datasets demonstrate the effectiveness of the proposed method in achieving high power while keeping the false discovery rate at a minimum.In the third and final chapter, we apply the proposed feature selection methods to the brain imaging tractography dataset. Our motivation comes from the evidence from studies of dementia which shows that some older adults continue to maintain their cognitive abilities despite signs of ongoing neuropathological diseases. Commonly referred to as cognitive reserve, this phenomenon has unclear neurobiological substrates and a current understanding of corresponding markers is lacking. This study aims at investigating the immense system of structural connections between brain regions constituting subcortical white matter (WM) as potential markers of cognitive reserve. Diffusion MRI tractography is an established computational neuroimaging method to model WM fiber organization throughout the brain. Standard statistical analyses capable of leveraging the high dimensionality of tractography data face additional methodological complications beyond those encountered in typical feature selection problems. Our proposed methodology is specifically tailored for addressing these concerns. Extensive simulation studies on synthetic datasets mimicking the real tractography dataset demonstrate a substantial gain in power with minimal false discoveries, compared with state-of-the-art methods for feature selection. Our application to predicting cognitive reserve in a clinical aging neuroimaging tractography dataset produces anatomically meaningful discoveries in brain regions associated with risk and resilience to neurodegeneration.Overall, this thesis presents novel and effective methods for feature selection in ultrahigh dimensional settings. Our proposed framework would benefit the researchers and professionals who encounter the difficulty of choosing pertinent variables from correlated and vast datasets in diverse fields, ranging from finance and social sciences to biology.

The book concentrates on the impact of digitalization and digital transformation technologies on the Industry 4.0 and smart factories, how the factory of tomorrow can be designed, built, and run virtually as a digital twin likeness of its real-world counterpart, before the physical structure is actually erected. It highlights the main digitalization technologies that have stimulated the Industry 4.0, how these technologies work and integrate with each other, and how they are shaping the industry of the future. It examines how multimedia data and digital images in particular are being leveraged to create fully virtualized worlds in the form of digital twin factories and fully virtualized industrial assets. It uses BMW Group’s latest SORDI dataset (Synthetic Object Recognition Dataset for Industry), i.e., the largest industrial images dataset to-date and its applications at BMW Group and Idealworks, as one of the main explanatory scenarios throughout the book. It discusses the need of synthetic data to train advanced deep learning computer vision models, and how such datasets will help create the “robot gym” of the future: training robots on synthetic images to prepare them to function in the real world.

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure

The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.

​This three-volume set, LNAI 11670, LNAI 11671, and LNAI 11672 constitutes the thoroughly refereed proceedings of the 16th Pacific Rim Conference on Artificial Intelligence, PRICAI 2019, held in Cuvu, Yanuca Island, Fiji, in August 2019. The 111 full papers and 13 short papers presented in these volumes were carefully reviewed and selected from 265 submissions. PRICAI covers a wide range of topics such as AI theories, technologies and their applications in the areas of social and economic importance for countries in the Pacific Rim.

This volume constitutes the refereed proceedings of the 18th International Conference on Database and Expert Systems Applications held in September 2007. Papers are organized into topical sections covering XML, data and information, datamining and data warehouses, database applications, WWW, bioinformatics, process automation and workflow, knowledge management and expert systems, database theory, query processing, and privacy and security.