Data quality analysis is a crucial step in ensuring the reliability and accuracy of data used in
machine learning models. The proposed approach utilized with machine learning algorithms …

Data analytics is the process of analyzing raw data to make predictions and derive
conclusions. This process involves collecting and organizing data to discover hidden …

Generally, the quality of artificial intelligence (AI) models depends on the training dataset.
Therefore, evaluating dataset quality is crucial. Datasets contain considerable attribute …

Advanced analytical techniques such as data mining, text mining or predictive analytics are
concepts that are increasingly important in the area of discovering large data sets. Various …

Good quality data is an essential part for the purpose of reaching an accurate and trusted
machine learning model, However the present gained datasets in the real world usually …

Data can be viewed as a product. The quality of data can be assessed as the quality of a
product which can be evaluated by multiple dimensions. Traditional data quality only refers …

There is profound confusion at the final stages of software development as to which defect
discovered belongs to which phase of software development lifecycle. Techniques like root …

Abstract Machine learning models represent a data framework that takes data from a
particular set and makes assumptions about the new observation through learning from the …

Software Quality is the very popular and a very important topic for researchers within the
software engineering communities, most probably everyone has an opinion about the …

With the rapid development of artificial intelligence, massive multi-modal data continues to
accumulate and precipitate, how to efficiently use rich data resources, improve data quality …