The evolving field of generative artificial intelligence (GenAI), particularly generative deep
learning, is revolutionizing a host of scientific and technological sectors. One of the pivotal …

Missing data imputation is a technique to deal with incomplete datasets. Since many models
and algorithms cannot be applied to data containing missing values, a pre-processing step …

Medical imaging has become integral to modern healthcare, enabling non-invasive
visualization and assessment of anatomical structures. However, medical imaging datasets …

In this study, we explore the impact of different masking strategies on time series imputation
models. We evaluate the effects of pre-masking versus in-mini-batch masking, normalization …

Data quality plays a crucial role in tasks, such as enhancing the accuracy of data analytics
and avoiding the accumulation of redundant data. One of the significant challenges in data …

We introduce a novel classification framework for time-series imputation using deep
learning, with a particular focus on clinical data. By identifying conceptual gaps in the …

Secondary research use of Electronic Health Records (EHRs) is often hampered by the
abundance of missing data in this valuable resource. Missingness in EHRs occurs naturally …

Merení genové exprese je nezbytné pro porozumení bunecným procesum a stavum v
rozlicných experimentálních podmínkách, což je potreba v ruzných oblastech …

Merení genové exprese je nezbytné pro porozumení bunecným procesum a stavum v
rozlicných experimentálních podmínkách, což je potreba v ruzných oblastech …

Due to the interruption of network transmission, the collected electronic medical records are
usually incomplete data. Therefore, the imputation of missing values is of great significance …