This paper provides an overview and analysis of the basic theory of deep learning (DL), and
specifically, a number of important algorithms were compared and analyzed. The article …

Large datasets are necessary for deep learning as the performance of the algorithms used
increases as the size of the dataset increases. Poor data management practices and the low …

Image translation with convolutional autoencoders has recently been used as an approach
to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the …

Efficient representation learning of data distribution is part and parcel of successful
execution of any machine learning based model. Autoencoders are good at learning the …

Food adulteration detection requires quick and simple methods. Spectral detection can
significantly reduce the analysis time, but it needs to construct a detection model. In this …

Detecting land cover change is an essential task in very-high-spatial-resolution (VHR)
remote sensing applications. However, because VHR images can capture the details of …

The concentration of nitrogen oxide (NOx) emissions is an important environmental index in
the cement production process. The purpose of predicting NOx emission concentration …

Learning useful representations from original features is a key issue in classification tasks.
Stacked autoencoders (SAEs) are easy to understand and realize, and they are powerful …

Electronic health records (EHRs) are a valuable data source that, in conjunction with deep
learning (DL) methods, have provided important outcomes in different domains, contributing …

Privacy-utility tradeoff remains as one of the fundamental issues of differentially private
machine learning. This paper introduces a geometrically inspired kernel-based approach to …