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 …

Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor
faults, and transmission failures. The incomplete data obstruct the effective use of data and …

Nowadays, the issue of MV imputation has become one of the research hotspots in the field
of data quality, since the missing values (MVs) are prevalent in real-world datasets and bring …

Missing data is a common problem in a wide range of fields that can arise as a result of
different reasons: lack of analysis, mishandling samples, measurement error, etc. The area …

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 …

Dimension reduction is one of the key data transformation techniques in machine learning
and knowledge discovery. It can be realized by using linear and nonlinear transformation …

Abstract In Smart Energy Grids, the information flow used to make decisions is the result of
fusion of different sources. Communication latency, possible sensor faults and inaccuracies …

Missing value imputation (MVI) is a key task in data science, in which learning models are
built from incomplete data. In contrast to externally driven MVI algorithms, this study …

Educational Institutions data constitute the basis for several important analyses on the
educational systems; however they often contain not negligible shares of missing values, for …

Deep reinforcement learning (DRL) is widely applied in motion planning for multi-robot
systems as DRL leverages the offline training process to improve the real-time computation …