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Dong, X., Potter, M., Kumar, G., Tsai, Y., Saripalli, V.R. and Trafalis, T. (2022) Optimizing Data Augmentation Policy through Random Unidimensional Search. In: Simos, D.E., Rasskazova, V.A., Archetti, F., Kotsireas, I.S., Pardalos, P.M., Eds., Lecture Notes in Computer Science, Springer International Publishing, 306-318.
https://doi.org/10.1007/978-3-031-24866-5_23

has been cited by the following article:

• TITLE: Data Evaluation in Artificial Intelligence

  AUTHORS: Philip de Melo

  KEYWORDS: Artificial Intelligence, Accuracy, PM GenAI Algorithm

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.13 No.3, August 7, 2025

  ABSTRACT: High-quality data is essential for hospitals, public health agencies, and governments to improve services, train AI models, and boost efficiency. However, real data comes with challenges: strict privacy laws, high storage costs, legal constraints, and issues like bias or incompleteness. These can reduce the reliability of AI systems. As a result, artificial datasets are gaining importance. Synthetic and augmented data offer alternatives, yet their differences and potential are not fully understood. Data quality refers to how well a dataset is suited for its intended purpose in an AI pipeline. Key attributes include Accuracy—How correct and error-free the data is; completeness—Whether all required data fields are present; Consistency—Uniformity across datasets (e.g., same format or scale); Timeliness—Relevance of the data in time (significant in real-time systems), Validity—Whether the data follows defined formats or constraints; Uniqueness—Absence of duplicate records. This paper examines how both types of data are generated and used, showcasing their characteristics through practical examples.