Synthesizing Tabular Data Using Conditional Gan
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Synthesizing Tabular Data Using Conditional Gan
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Author : Lei Xu (S.M.)
language : en
Publisher:
Release Date : 2020
Synthesizing Tabular Data Using Conditional Gan written by Lei Xu (S.M.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
In data science, the ability to model the distribution of rows in tabular data and generate realistic synthetic data enables various important applications including data compression, data disclosure, and privacy-preserving machine learning. However, because tabular data usually contains a mix of discrete and continuous columns, building such a model is a non-trivial task. Continuous columns may have multiple modes, while discrete columns are sometimes imbalanced, making modeling difficult. To address this problem, I took two major steps. (1) I designed SDGym, a thorough benchmark, to compare existing models, identify different properties of tabular data and analyze how these properties challenge different models. Our experimental results show that statistical models, such as Bayesian networks, that are constrained to a fixed family of available distributions cannot model tabular data effectively, especially when both continuous and discrete columns are included. Recently proposed deep generative models are capable of modeling more sophisticated distributions, but cannot outperform Bayesian network models in practice, because the network structure and learning procedure are not optimized for tabular data which may contain non-Gaussian continuous columns and imbalanced discrete columns. (2) To address these problems, I designed CTGAN, which uses a conditional generative adversarial network to address the challenges in modeling tabular data. Because CTGAN uses reversible data transformations and is trained by re-sampling the data, it can address common challenges in synthetic data generation. I evaluated CTGAN on the benchmark and showed that it consistently and significantly outperforms existing statistical and deep learning models.
Engineering Applications Of Neural Networks
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Author : Lazaros Iliadis
language : en
Publisher: Springer Nature
Release Date : 2022-06-14
Engineering Applications Of Neural Networks written by Lazaros Iliadis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-14 with Computers categories.
This book constitutes the refereed proceedings of the 23rd International Conference on Engineering Applications of Neural Networks, EANN 2022, held in Chersonisos, Crete, Greece, in June 2022. The 37 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on Bio inspired Modeling / Novel Neural Architectures; Classification / Clustering; Machine Learning; Convolutional / Deep Learning; Datamining / Learning / Autoencoders; Deep Learning / Blockchain; Machine Learning for Medical Images / Genome Classification; Reinforcement /Adversarial / Echo State Neural Networks; Robotics / Autonomous Vehicles, Photonic Neural Networks; Text Classification / Natural Language.
Data Envelopment Analysis Dea Methods For Maximizing Efficiency
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Author : Ajibesin, Adeyemi Abel
language : en
Publisher: IGI Global
Release Date : 2024-01-16
Data Envelopment Analysis Dea Methods For Maximizing Efficiency written by Ajibesin, Adeyemi Abel and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-16 with Computers categories.
In today's highly competitive and rapidly evolving global landscape, the quest for efficiency has become a crucial factor in determining the success of organizations across various industries. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency is a comprehensive guide that delves into the powerful mathematical tool of DEA, is designed to assess the relative efficiency of decision-making units (DMUs), and provides valuable insights for performance improvement. This book presents a systematic overview of DEA models and techniques, from fundamental concepts to advanced methods, showcasing their practical applications through real-world examples and case studies. Catering to a broad audience, this book is designed for students, researchers, consultants, decision-makers, and enthusiasts in the field of efficiency analysis and performance measurement. Consultants and practitioners will gain practical insights for applying DEA in various contexts, and decision-makers will be equipped to make informed decisions for maximizing efficiency. Additionally, individuals with a general interest in data analysis and performance measurement will find this book accessible and informative. This book covers a wide range of topics, including mathematical foundations of DEA, DEA models and variations, DEA efficiency and productivity measures, DEA applications in various industries such as healthcare, finance, supply chain management, environmental management, education management, and public sector management.
Artificial Intelligence In Medicine
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Author : Martin Michalowski
language : en
Publisher: Springer Nature
Release Date : 2020-09-25
Artificial Intelligence In Medicine written by Martin Michalowski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-25 with Computers categories.
The LNAI 12299 constitutes the papers of the 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, which will be held online in August 2020. The 42 full papers presented together with 1short papers in this volume were carefully reviewed and selected from a total of 103 submissions. The AIME 2020 goals were to present and consolidate the international state of the art of AI in biomedical research from the perspectives of theory, methodology, systems, and applications.
Hybrid Artificial Intelligent Systems
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Author : Pablo García Bringas
language : en
Publisher: Springer Nature
Release Date : 2023-08-28
Hybrid Artificial Intelligent Systems written by Pablo García Bringas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-28 with Computers categories.
This book constitutes the refereed proceedings of the 18th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2023, held in Salamanca, Spain, during September 5–7, 2023. The 65 full papers included in this book were carefully reviewed and selected from 120 submissions. They were organized in topical sections as follows: Anomaly and Fault Detection, Data Mining and Decision Support Systems, Deep Learning, Evolutionary Computation and Optimization, HAIS Applications, Image and Speech Signal Processing, Agents and Multiagents, Biomedical Applicatons.
Pattern Recognition
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Author : Ullrich Köthe
language : en
Publisher: Springer Nature
Release Date :
Pattern Recognition written by Ullrich Köthe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Discovery Science
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Author : Carlos Soares
language : en
Publisher: Springer Nature
Release Date : 2021-10-08
Discovery Science written by Carlos Soares and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-08 with Computers categories.
This book constitutes the proceedings of the 24th International Conference on Discovery Science, DS 2021, which took place virtually during October 11-13, 2021. The 36 papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: applications; classification; data streams; graph and network mining; machine learning for COVID-19; neural networks and deep learning; preferences and recommender systems; representation learning and feature selection; responsible artificial intelligence; and spatial, temporal and spatiotemporal data.
Artificial Intelligence In Mechatronics And Civil Engineering
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Author : Ehsan Momeni
language : en
Publisher: Springer Nature
Release Date : 2023-02-15
Artificial Intelligence In Mechatronics And Civil Engineering written by Ehsan Momeni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-15 with Science categories.
Recent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.
Generative Adversarial Networks With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2019-07-11
Generative Adversarial Networks With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-11 with Computers categories.
Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.
Multimedia Modeling
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Author : Duc-Tien Dang-Nguyen
language : en
Publisher: Springer Nature
Release Date : 2023-03-28
Multimedia Modeling written by Duc-Tien Dang-Nguyen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-28 with Computers categories.
The two-volume set LNCS 13833 and LNCS 13834 constitutes the proceedings of the 29th International Conference on MultiMedia Modeling, MMM 2023, which took place in Bergen, Norway, during January 9-12, 2023. The 86 papers presented in these proceedings were carefully reviewed and selected from a total of 267 submissions. They focus on topics related to multimedia content analysis; multimedia signal processing and communications; and multimedia applications and services.