Data analytics and machine learning with Java

Zhelev, Svetoslav; Rozeva, Anna

doi:10.1063/1.5082135

Cited by 2 publications

(1 citation statement)

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“…It's also a catchphrase for any kind of large-scale database or data handling (collecting, retrieval, storage, assessment, and analytics), as well as any implementation of technology choice support systems, such as natural intellect (e.g., deep learning) and business analysis. The journal "Data analytics and machine learning with Java" (which contains largely computer learning content) was initially going to be called merely Practical learning, with the word "DM" considered for marketing purposes [1]. When referring to specific methodologies, the more broad phrases (large scale) research methodology and analytics-or, when relating to real techniques, natural learning and computer learning-are usually more suitable.…”

Section: Introductionmentioning

confidence: 99%

The Scope, Methods and Applications of Biomedical Data Mining

Steyn

Martins

2022

JBSHA

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Most literature assumptions have been drawn from public databases e.g. NHANES (National Health and Nutrition Examination Survey). Nonetheless, the sets of data are typically featured by high-dimensional timeliness, heterogeneity, characteristics and irregularity, hence amounting to valuation of these databases not being applied completely. Data Mining (DM) technologies have been the frontiers domains in biomedical studies, as it shows smart routine in assessing patients’ risks and aiding in the process of biomedical research and decision-making in developing disease-forecasting frameworks. In that case, DM has novel merits in biomedical Big Data (BD) studies, mostly in large-scale biomedical datasets. In this paper, a description of DM techniques alongside their fundamental practical applications will be provided. The objectives of this study are to help biomedical researchers to attain intuitive and clear appreciative of the applications of data-mining technologies on biomedical BD to enhance to creation of biomedical results, which are relevant in a biomedical setting.

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Section: Introductionmentioning

confidence: 99%