At the beginning of the COVID-19 pandemic, there was significant hype about the potential
impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or …

Photocatalysis is a unique technology that harnesses solar energy through in-situ
processes, operating without the need for external energy inputs. It is integral to advancing …

Given a dataset with incomplete data (eg, missing values), training a machine learning
model over the incomplete data requires two steps. First, it requires a data-effective step that …

This paper proposed a network framework, namely DR-DTPN, which integrates data repair
and degradation trend prediction to address the serious deviation of equipment degradation …

Data integration is the problem of consolidating information provided by multiple sources.
After schema map** and duplicate detection have been dealt with, the problem consists in …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Data cleaning, a prerequisite to subsequent data analysis, has always been the focus of
data science research. Datasets with errors can severely detract from the quality of …

Most data analytical pipelines often encounter the problem of querying inconsistent data that
violate pre-determined integrity constraints. Data cleaning is an extensively studied …

Data quality is paramount in today's data-driven world, especially in the era of generative AI.
Dirty data with errors and inconsistencies usually leads to flawed insights, unreliable …

Imputation is a critical method for enhancing dataset quality, essential for ensuring accurate
analysis and insights. This research proposes an advanced imputation algorithm utilizing a …