Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Applications Of Synthetic High Dimensional Data
Download Applications Of Synthetic High Dimensional Data full books in PDF, epub, and Kindle. Read online Applications Of Synthetic High Dimensional Data ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Applications of Synthetic High Dimensional Data by : Sobczak-Michalowska, Marzena
Download or read book Applications of Synthetic High Dimensional Data written by Sobczak-Michalowska, Marzena and published by IGI Global. This book was released on 2024-03-25 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning Aided Feature Selection for Ultrahigh Dimensional Data by : Arkaprabha Ganguli
Download or read book Machine Learning Aided Feature Selection for Ultrahigh Dimensional Data written by Arkaprabha Ganguli and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Synthetic Data written by Jimmy Nassif and published by Springer Nature. This book was released on 2024-01-03 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Practical Synthetic Data Generation by : Khaled El Emam
Download or read book Practical Synthetic Data Generation written by Khaled El Emam and published by "O'Reilly Media, Inc.". This book was released on 2020-05-19 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis BIG DATA ANALYTICS by : Parag Kulkarni
Download or read book BIG DATA ANALYTICS written by Parag Kulkarni and published by PHI Learning Pvt. Ltd.. This book was released on 2016-07-07 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis PRICAI 2019: Trends in Artificial Intelligence by : Abhaya C. Nayak
Download or read book PRICAI 2019: Trends in Artificial Intelligence written by Abhaya C. Nayak and published by Springer Nature. This book was released on 2019-08-23 with total page 729 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Database and Expert Systems Applications by : Roland Wagner
Download or read book Database and Expert Systems Applications written by Roland Wagner and published by Springer. This book was released on 2007-08-23 with total page 927 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Database and Expert Systems Applications by : Sourav S. Bhowmick
Download or read book Database and Expert Systems Applications written by Sourav S. Bhowmick and published by Springer. This book was released on 2009-08-25 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 20th International Conference on Database and Expert Systems Applications, DEXA 2009, held in Linz, Austria, in August/September 2009. The 35 revised full papers and 35 short papers presented were carefully reviewed and selected from 202 submissions. The papers are organized in topical sections on XML and databases; Web, semantics and ontologies; temporal, spatial, and high dimensional databases; database and information system architecture, performance and security; query processing and optimisation; data and information integration and quality; data and information streams; data mining algorithms; data and information modelling; information retrieval and database systems; and database and information system architecture and performance.
Book Synopsis Rough Sets and Intelligent Systems Paradigms by : Marzena Kryszkiewicz
Download or read book Rough Sets and Intelligent Systems Paradigms written by Marzena Kryszkiewicz and published by Springer. This book was released on 2014-06-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 23rd Australasian Joint Conference on Rough Sets and Intelligent Systems Paradigms, RSEISP 2014, held in Granada and Madrid, Spain, in July 2014. RSEISP 2014 was held along with the 9th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2014, as a major part of the 2014 Joint Rough Set Symposium, JRS 2014. JRS 2014 received 40 revised full papers and 37 revised short papers which were carefully reviewed and selected from 120 submissions and presented in two volumes. This volume contains the papers accepted for the conference RSEISP 2014, as well as the three invited papers presented at the conference. The papers are organized in topical sections on plenary lecture and tutorial papers; foundations of rough set theory; granular computing and covering-based rough sets; applications of rough sets; induction of decision rules - theory and practice; knowledge discovery; spatial data analysis and spatial databases; information extraction from images.
Book Synopsis Nature-Inspired Algorithms for Big Data Frameworks by : Banati, Hema
Download or read book Nature-Inspired Algorithms for Big Data Frameworks written by Banati, Hema and published by IGI Global. This book was released on 2018-09-28 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.
Book Synopsis Understanding and Interpreting Machine Learning in Medical Image Computing Applications by : Danail Stoyanov
Download or read book Understanding and Interpreting Machine Learning in Medical Image Computing Applications written by Danail Stoyanov and published by Springer. This book was released on 2018-10-23 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
Book Synopsis Privacy in Statistical Databases by : Josep Domingo-Ferrer
Download or read book Privacy in Statistical Databases written by Josep Domingo-Ferrer and published by Springer Nature. This book was released on with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Big Data Analytics written by and published by Elsevier. This book was released on 2015-08-04 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions. Review of big data research challenges from diverse areas of scientific endeavor Rich perspective on a range of data science issues from leading researchers Insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domains
Book Synopsis Computational Intelligence Applications in Business Intelligence and Big Data Analytics by : Vijayan Sugumaran
Download or read book Computational Intelligence Applications in Business Intelligence and Big Data Analytics written by Vijayan Sugumaran and published by CRC Press. This book was released on 2017-06-26 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.
Book Synopsis High-dimensional Data Indexing with Applications by : Michael Arthur Schuh
Download or read book High-dimensional Data Indexing with Applications written by Michael Arthur Schuh and published by . This book was released on 2015 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: The indexing of high-dimensional data remains a challenging task amidst an active and storied area of computer science research that impacts many far-reaching applications. At the crossroads of databases and machine learning, modern data indexing enables information retrieval capabilities that would otherwise be impractical or near impossible to attain and apply. One such useful retrieval task in our increasingly data-driven world is the k-nearest neighbor (k-NN) search, which returns the k most similar items in a dataset to the search query provided. While the k-NN concept was popularized in every-day use through the sorted (ranked) results of online text-based search engines like Google, multimedia applications are rapidly becoming the new frontier of research. This dissertation advances the current state of high-dimensional data indexing with the creation of a novel index named ID* (\ID Star"). Based on extensive theoretical and empirical analyses, we discuss important challenges associated with high dimensional data and identify several shortcomings of existing indexing approaches and methodologies. By further mitigating against the negative effects of the curse of dimensionality, we are able to push the boundary of effective k-NN retrieval to a higher number of dimensions over much larger volumes of data. As the foundations of the ID* index, we developed an open-source and extensible distance-based indexing framework predicated on the basic concepts of the popular iDistance index, which utilizes an internal B+-tree for efficient one-dimensional data indexing. Through the addition of several new heuristic-guided algorithmic improvements and hybrid indexing extensions, we show that our new ID* index can perform significantly better than several other popular alternative indexing techniques over a wide variety of synthetic and real-world data. In addition, we present applications of our ID* index through the use of k-NN queries in Content-Based Image Retrieval (CBIR) systems and machine learning classification. An emphasis is placed on the NASA sponsored interdisciplinary research goal of developing a CBIR system for large-scale solar image repositories. Since such applications rely on fast and effective k-NN queries over increasingly large-scale and high-dimensional datasets, it is imperative to utilize an efficient data indexing strategy such as the ID* index.
Book Synopsis Deep Learning and Medical Applications by : Jin Keun Seo
Download or read book Deep Learning and Medical Applications written by Jin Keun Seo and published by Springer Nature. This book was released on 2023-06-15 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 40 years, diagnostic medical imaging has undergone remarkable advancements in CT, MRI, and ultrasound technology. Today, the field is experiencing a major paradigm shift, thanks to significant and rapid progress in deep learning techniques. As a result, numerous innovative AI-based programs have been developed to improve image quality and enhance clinical workflows, leading to more efficient and accurate diagnoses. AI advancements of medical imaging not only address existing unsolved problems but also present new and complex challenges. Solutions to these challenges can improve image quality and reveal new information currently obscured by noise, artifacts, or other signals. Holistic insight is the key to solving these challenges. Such insight may lead to a creative solution only when it is based on a thorough understanding of existing methods and unmet demands. This book focuses on advanced topics in medical imaging modalities, including CT and ultrasound, with the aim of providing practical applications in the healthcare industry. It strikes a balance between mathematical theory, numerical practice, and clinical applications, offering comprehensive coverage from basic to advanced levels of mathematical theories, deep learning techniques, and algorithm implementation details. Moreover, it provides in-depth insights into the latest advancements in dental cone-beam CT, fetal ultrasound, and bioimpedance, making it an essential resource for professionals seeking to stay up-to-date with the latest developments in the field of medical imaging.
Book Synopsis Translational Applications of Neuroimaging by : Stavros Skouras
Download or read book Translational Applications of Neuroimaging written by Stavros Skouras and published by Frontiers Media SA. This book was released on 2024-04-04 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite substantial progress in the development of neuroimaging methodologies, translational applications of neuroimaging remain scarce. This Research Topic invites article submissions that present promising neuroimaging applications and methods addressing critical needs for improving health outcomes. These may include Original Research, Clinical Trial, Systematic Review or Methods articles that investigate neuroimaging metrics as outcome measures or in combination with neural perturbation techniques (e.g., neurofeedback, neurostimulation), other translational applications (e.g., guiding neurosurgery). To foster debate, we also welcome critical Review, Opinion, and Perspective articles that survey the field and its progress towards clinical utility.
