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 …

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 …

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 …

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 …

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 …

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 …

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 …

Traffic data imputation is a critical preprocessing step in intelligent transportation systems,
enabling advanced transportation services. Despite significant advancements in this field …

In the face of the huge amount of intelligent transportation data, it is necessary and important
to collect and statistically process it. Due to adverse weather conditions, sensor malfunctions …