The presence of the missing value in the data set has always been a major problem for
precise prediction. The method for imputing missing value needs to minimize the effect of …

Missing values are highly undesirable in real-world datasets. The missing values should be
estimated and treated during the preprocessing stage. With the expansion of nature-inspired …

Artificial intelligence is finding its ways into the mainstream of day-to-day operations. Novel
AI application techniques such as the artificial neural network (ANN), fuzzy logic, genetic …

In this paper, two novel hybrid imputation methods involving particle swarm optimization
(PSO), evolving clustering method (ECM) and autoassociative extreme learning machine …

The presence of missing values is a pervasive and unavoidable phenomenon in sensor
data. Despite numerous efforts from researchers to address this issue through imputation …

In collaborative filtering, one of the main challenges that researchers face is sparseness in
the data, which is caused by users rating fewer items as the number of items increase in the …

Military applications are producing massive amounts of data due to the use of multiple types
of sensors on the battlefield. The aim of this paper is to investigate the weapon system …

In financial decision making models, parameters are usually obtained based on historical
data, which involve strong uncertainties. In some cases, the fluctuation caused by …

In collaborative filtering, the stochastic gradient descent (SGD) method is used to determine
the latent features used in producing a non-negative N x M matrix of user-item ratings. The …

Partial discharge (PD) measurement and recognition is of great importance to assess the
health condition of power transformers. However, the variation of defect size, applied voltage …