<p class="0abstract">Sign Language is considered the main communication tool for deaf or hearing impaired people. It is a visual language that uses hands and other parts of the body to provide people who are in need to full access of communication with the world. Accordingly, the automation of sign language recognition has become one of the important applications in the areas of Artificial Intelligence and Machine learning. Specifically speaking, Arabic sign language recognition has been studied and applied using various intelligent and traditional approaches, but with few attempts to improve the process using deep learning networks. This paper utilizes transfer learning and fine tuning deep convolutional neural networks (CNN) to improve the accuracy of recognizing 32 hand gestures from the Arabic sign language. The proposed methodology works by creating models matching the VGG16 and the ResNet152 structures, then, the pre-trained model weights are loaded into the layers of each network, and finally, our own soft-max classification layer is added as the final layer after the last fully connected layer. The networks were fed with normal 2D images of the different Arabic Sign Language data, and was able to provide accuracy of nearly 99%.</p>
Rock joints play an important role in the behavior of rock masses under normal and shear loading conditions. Numerical simulation of the behavior of jointed rock masses is not an easy task due to complexities involved in the problem such as joint roughness, joint shear strength, hardening and softening phenomenon and mesh dependency. In this study for modeling purposes, a visco-plastic multilaminate model considering hardening and softening effects has been employed. For providing the necessary data for numerical simulation, a series of laboratory experiments have been carried out on regular tooth-shape asperities made by gypsum, under constant normal load conditions. Shear stress-shear displacement and normal displacement-shear displacement of artificial joint specimens are simulated using the proposed numerical model at constant normal load condition (CNL). The results indicate the capability of the model for simulating rock joints behavior in both strength and deformation field. Although the numerical model has been developed for simulating the behavior of artificial joints, the concept of the method can also be used for natural rock joints.
The study provides further evidence for the activity of gemcitabine plus oxaliplatin combination as a first-line treatment for advanced biliary tract carcinomas. This combination can be given safely as a convenient biweekly outpatient regimen.
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