GeMSyD: Generic Framework for Synthetic Data Generation

In the era of data-driven technologies, the need for diverse and high-quality datasets for training and testing machine learning models has become increasingly critical. In this article, we present a versatile methodology, the Generic Methodology for Constructing Synthetic Data Generation (GeMSyD),...

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主要な著者:	Ramona Tolas, Raluca Portase, Rodica Potolea
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2024-01-01
シリーズ:	Data
主題:	synthetic data generation framework smart home-appliance dataset user interaction data event-based data processing open-source framework usage forecasting
オンライン･アクセス:	https://www.mdpi.com/2306-5729/9/1/14
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