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Relevant bibliographies by topics / Synthetic Database Generation

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Journal articles

Academic literature on the topic 'Synthetic Database Generation'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Synthetic Database Generation"

1

Priyadarshini, Pallavi, Fengqiong Qin, Ee-Peng Lim, and Wee-Keong Ng. "Parameter driven synthetic web database generation." Journal of Systems and Software 69, no. 1-2 (2004): 29–42. http://dx.doi.org/10.1016/s0164-1212(03)00002-5.

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Sanghi, Anupam, Shadab Ahmed, and Jayant R. Haritsa. "Projection-compliant database generation." Proceedings of the VLDB Endowment 15, no. 5 (2022): 998–1010. http://dx.doi.org/10.14778/3510397.3510398.

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Synthesizing data using declarative formalisms has been persuasively advocated in contemporary data generation frameworks. In particular, they specify operator output volumes through row-cardinality constraints. However, thus far, adherence to these volumetric constraints has been limited to the Filter and Join operators. A critical deficiency is the lack of support for the Projection operator, which is at the core of basic SQL constructs such as Distinct, Union and Group By. The technical challenge here is that cardinality unions in multi-dimensional space, and not mere summations, need to be

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Pujol, David, Amir Gilad, and Ashwin Machanavajjhala. "PreFair: Privately Generating Justifiably Fair Synthetic Data." Proceedings of the VLDB Endowment 16, no. 6 (2023): 1573–86. http://dx.doi.org/10.14778/3583140.3583168.

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When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the synthetic data, while maintaining the privacy of the original data. Therefore, multiple works have been devoted to devising systems for DP synthetic data generation. However, such systems may preserve or even magnify properties of the data that make it unfair, rendering the synthetic data unfit for use. In this work, we present PreFair, a system that

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Pavez, Vicente, Gabriel Hermosilla, Francisco Pizarro, Sebastián Fingerhuth, and Daniel Yunge. "Thermal Image Generation for Robust Face Recognition." Applied Sciences 12, no. 1 (2022): 497. http://dx.doi.org/10.3390/app12010497.

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This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate therma

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Dinges, Laslo, Ayoub Al-Hamadi, Moftah Elzobi, Sherif El-etriby, and Ahmed Ghoneim. "ASM Based Synthesis of Handwritten Arabic Text Pages." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/323575.

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Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system th

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Sazonova, Kateryna, Olena Nosovets, Vitalii Babenko, and Olga Averianova. "GENERATION OF SYNTHETICAL MEDICAL DATA BY MDR-ANALYSIS." Proceedings of the National Aviation University 87, no. 2 (2021): 31–36. http://dx.doi.org/10.18372/2306-1472.87.15719.

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Purpose: The purpose of this article is to outline an algorithm for generating synthetic medical data in order to augment small samples of data. Methods: To achieve the research goal, methods such as: correlation analysis (to identify significant variables and the relationships between them), MDR analysis (to build logical chains of relationships between medical data), and regression analysis (to model medical data variables to use this to generate synthetic data) were used. Results: A database of heart failure patients that is publicly available was used to test the developed algorithm for ge

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Burman, Nitin, Claudia Manetti, Paulo Tostes, Joost Lumens, and Jan D'hooge. "A pipeline to enable large-scale generation of diverse 2D cardiac synthetic ultrasound recordings corresponding to healthy and heart failure virtual patients." Journal of the Acoustical Society of America 152, no. 4 (2022): A279. http://dx.doi.org/10.1121/10.0016267.

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Simulated ultrasound (US) data are widely used in echocardiography to develop and validate rapidly growing convolutional neural networks (CNNs) based learning algorithms for image processing and analysis. In this context, a large and diverse database of synthetic US scans is considered vital for CNN training purposes, as clinical US data are scarce and difficult to access. Major hurdles in creating an extensive database are the long US simulation time and unstable heart models for extreme parameter settings. Here, we developed and implemented a cardiac US simulation pipeline that kinematically

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Kuriki, Mikaele Silva, Francisco Lledo Santos, and Cristiano Poleto. "Small-Scale Wetland Model for Synthetic Sewage Treatment." Ciência e Natura 44 (April 21, 2022): e25. http://dx.doi.org/10.5902/2179460x68834.

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With the demand for electricity growing, the migration to renewable sources is a reality. In distributed generation, photovoltaic systems are a renewable and sustainable alternative to the main energy sources to generate electricity. Monitoring a photovoltaic system over its operating time guarantees its good performance. This requires solar radiation and temperature data measured at the installation site or the use of solarimetric stations databases. However, the differences between the results simulated with databases and with data measured at the installation site are not widely known, whic

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9

Baowaly, Mrinal Kanti, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen. "Synthesizing electronic health records using improved generative adversarial networks." Journal of the American Medical Informatics Association 26, no. 3 (2018): 228–41. http://dx.doi.org/10.1093/jamia/ocy142.

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AbstractObjectiveThe aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.Materials and MethodsWe modified medGAN to obtain two synthetic data generation models—designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)—and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First,

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Loisel, Hubert, Daniel Schaffer Ferreira Jorge, Rick A. Reynolds, and Dariusz Stramski. "A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean." Earth System Science Data 15, no. 8 (2023): 3711–31. http://dx.doi.org/10.5194/essd-15-3711-2023.

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Abstract. Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrar

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