Artificial neural network (ANN) is a simplified model of the biological nervous system
consisting of nerve cells or neurons. The application of ANN to food process engineering is …

Hyperspectral unmixing is an important problem for remotely sensed data interpretation. It
amounts at estimating the spectral signatures of the pure spectral constituents in the scene …

In the recent past years, deep learning based machine learning systems have demonstrated
remarkable success for a wide range of learning tasks in multiple domains such as computer …

The resurgence of applied informatics (AI) has revolutionised the whole of the computing
industry, which in turn has revolutionised almost all the possible sectors of industry. AI is the …

A self-supervised voltage sag source identification method based on a convolution neural
network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural …

Currently, educational data mining act as a major part of student performance prediction
approaches and their applications. However, more ensemble methods are needed to …

For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal
communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based …

As a scheme of non-orthogonal multiple access in the code-domain, sparse code multiple
access (SCMA) is one of the promoting candidate for the upcoming generations of wireless …

Multi-conditioned training is a state-of-the-art approach to achieve robustness in Automatic
Speech Recognition (ASR) systems. This approach works well in practice for seen …

Speech enhancement driven robust Automatic Speech Recognition (ASR) systems typically
require a parallel corpus with noisy and clean speech utterances for training. Moreover …