A Survey of Synthetic Data Generation for Machine Learning

Abufadda, Mohammad; Mansour, Khalid

doi:10.1109/acit53391.2021.9677302

Cited by 20 publications

(9 citation statements)

References 29 publications

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“…This data set can be used with any machine‐learning module, thus allowing other researchers to utilize it in their research. Synthetic data generation for machine‐learning is an active area of research, especially in the domain of medicine and health care, where the privacy of patient data is a big concern [3, 14, 22, 29, 40, 46]. To the best of our knowledge, no existing open‐source contribution automate the diagnosis of the three hearing loss attributes: type, degree, and configuration.…”

Section: Related Workmentioning

confidence: 99%

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Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Taylor,

Sheikh

2024

Comp Applic In Engineering

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Approximately 700 million people will have disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with disabling hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time, resulting in negative societal and economic impacts. In this article, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of an open‐source automated hearing impairment diagnosis software, which consists of two modules: a hearing test data‐generation module and a machine‐learning model. The data‐generation module produces a diverse and exhaustive data set for training and evaluating the machine‐learning model. By employing multiclass and ultilabel classification techniques to learn from the hearing test data, the model can swiftly predict the type, degree, and configuration of hearing loss with high reliability. Our proposed machine‐learning model demonstrates promising results with a prediction time of 634 ms, a log‐loss reduction rate of 0.9848 and accuracy, precision, recall, and f1‐score of 1.0000—showing the model's applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss. In addition to the technical contributions, this article also highlights the importance of involving undergraduate students in open‐source software development projects which have a direct impact on improving the quality of human life.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Taylor,

Sheikh

2024

Comp Applic In Engineering

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“…However, improving data sources to feed artificial intelligence (AI) algorithms is not limited to DA exclusively. Therefore, some studies have decided to take the path of building synthetic traffic generators to build their datasets almost from scratch; some examples focusing on this aspect are [17][18][19]. In this way, they are able to abstract from the dataset itself, which is only necessary to understand the distribution of the data.…”

Section: Related Workmentioning

confidence: 99%

Data Augmentation techniques in time series domain: a survey and taxonomy

Iglesias

Talavera

González-Prieto

et al. 2023

Neural Comput & Applic

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With the latest advances in deep learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using data augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state of the art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.

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“…There are four main techniques that allow deep models to be trained on small datasets. First is synthetic data generation [35][36][37]. If meaningful data can be artificially computer generated, this can reduce the need for timely manual collection and labeling.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning using infrared and optical full motion video data for gender classification

Glandon

Zalameda

Iftekharuddin

2023

Infrared Technology and Applications XLIX

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This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

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A Survey of Synthetic Data Generation for Machine Learning

Cited by 20 publications

References 29 publications

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Data Augmentation techniques in time series domain: a survey and taxonomy

Transfer learning using infrared and optical full motion video data for gender classification

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