Emotion recognition represents the position and motion of facial muscles. It contributes
significantly in many fields. Current approaches have not obtained good results. This paper …

A Random Forest (RF) classifier was applied to spectral as well as mono-and multi-seasonal
textural features extracted from Landsat TM imagery to increase the accuracy of land cover …

Land-cover map** in complex farming area is a difficult task because of the complex
pattern of vegetation and rugged mountains with fast-flowing rivers, and it requires a method …

Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide
range of symptoms. Hence, the early diagnosis and treatment is quite important. Method …

Automated and accurate classification of MR brain images is of importance for the analysis
and interpretation of these images and many methods have been proposed. In this paper …

Image segmentation is an important application of polarimetric synthetic aperture radar. This
study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli …

Accurate fruit classification is difficult to accomplish because of the similarities among the
various categories. In this paper, we proposed a novel fruit‐classification system, with the …

Remote sensing image classification (RSIC) is a classical and fundamental task in the
intelligent interpretation of remote sensing imagery, which can provide unique labeling …

In order to detect multiple sclerosis (MS) subjects from healthy controls (HCs) in magnetic
resonance imaging, we developed a new system based on machine learning. The MS …

Crop plant detection is vital for map** crop planting area and extracting pure crop canopy
information. In this study, three cameras (RGB, color infrared (NIR-GB) and multispectral …