This paper addresses a fast white blood cell (WBC) image segmentation scheme
implemented by on-line trained neural network. A pre-selecting technique, based on mean …

The ability to detect and isolate process fault for product quality control in assembly
processes plays an essential role in the success of a manufacturing enterprise in today's …

In this paper, the development and evaluation of an entropy based hybrid data driven model
coupled with input selection approach and wavelet transformation is investigated for long …

This paper proposes a new stacked denoising autoencoders (SDAE), called manifold
regularized SDAE (MRSDAE) based on particle swarm optimization (PSO), where manifold …

At present, the energy consuming during the electrolytic copper foil preparation accounts for
more than 75% of the total energy consumption. In real-life production, the process …

The existing studies have shown that miRNAs are related to human diseases by regulating
gene expression. Identifying miRNA association with diseases will contribute to diagnosis …

In this paper, based on a sample selection rule and a Back Propagation (BP) neural
network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites …

An Artificial Neural Networks (ANNs) model is constructed to forecast SO2 concentrations in
Izmir air. The model uses meteorological variables (wind speed and temperature) and …

This study evaluates the application of an intelligent hybrid system for time-series
forecasting of atmospheric pollutant concentration levels. The proposed method consists of …

To reduce process variability in complex manufacturing processes, a tremendous need
exists to integrate monitoring process variability (PV) and identification of source of out-of …