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Go to Editorial ManagerFacial expressions are a form of non-verbal communication, they appear as changes on the surface of the facial skin according to one's inner emotional states, aims, or social communications. Classification of these expressions is a normal process for humans, but it is a challenging task for machines.Lately, interest in facial expression recognition has grown, and many systems have been developed to classify expressions from facial images. Any expression recognition system is comprised of three steps. The first one is face acquisition, then feature extraction, and finally classification. The classification accuracy depends primarily on the feature extraction step. Therefore, in this research we study many texture feature extraction descriptors and compare their results under the same preprocessing circumstances; moreover, we propose two improvements for one of these descriptors, which give better results than the original one. We validate the results on two commonly used databases for expression recognition using Matlab programming language, wishing all of that to be an interesting point for researchers in this field.
In this research, we successfully obtained Ni/SiC micro-composite coatings with various contents of SiC particles of particle size(10 ?m), by using electrodeposition method from nickel watts bath in which the SiC particles suspend. The effects of the current density, temperature, and particle loading(PL) of SiC particles in the electrolyte on the morphology, texture, and vol% of SiC in deposit were investigated. The morphological and structural analysis show uniform distribution of SiC particles within the composite coatings. It was found that the depositing conditions affect the microstructure of deposited nickel and the SiC vol% in deposit. Furthermore, the vol% of SiC increases in deposit by increasing the particle loading(PL) in the bath, while decreased by increasing the current density. Also the higher values of SiC vol% were obtained at temperature (50°C).
Technically, medical imaging modalities are quantitative, qualitative, and semi-quantitative. Such modalities can generate meaningful and valuable quantitative and qualitative data. Correlating predictive outcomes with quantitative and qualitative data is a difficult process. Thanks to modern computational hardware and advanced machine learning algorithms, it is not a demanding job to perform predictive analysis by cultivating quantitative and qualitative data. Radiomics is a popular topic that studies quantitative data from medical images in order to obtain biologically meaningful information for diagnosis, prognosis, theragnosis, and decision support. Handcrafted radiomics is a process including features based on shape, pixel, and texture-related knowledge from medical scans. In the pursuit of advancing the field of radiomics, we have developed a cutting-edge radiomics training simulator, powered by MATLAB. This tool has been designed for those familiar with MATLAB, making it easy for them to transition into the fascinating world of radiomics. MATLAB's user-friendly interface and strong support in the engineering community provide an ideal platform for this simulator, ensuring aspiring radiomics learners have access to the resources they need for success. Throughout the paper, purpose, design details and methodology of the simulator are described.