conversation is an oral skill in which two persons can exchange their ideas, thoughts and opinions to communicate with each other. Learners can work in pairs or groups to practice dialogues chosen by the teacher from their textbook materials or from additional resources. Nowadays, because of Corona virus infection, our college students are taught all the subjects including conversation course either via google classroom platform or any other platforms. Google meet is one of online meetings between the teacher and students, i.e., a video meet in which students and the teacher join the online meetings in a specified time to communicate, express their ideas, thoughts and feelings about what is mentioned above. In this study, the researcher has chosen (third stage level/college students during the academic year 2020/2021) and the data of the study was two dialogues chosen from “Chris Redston and Gillie Cunningham Face2face Intermediate Student's Book. Cambridge University Press (2013)” to study by google meet. Another two dialogues were chosen from their required textbook Real Listening and Speaking 4 with Answers. Miles Craven (2008). Cambridge University Press to study by face to face. One group of thirty-two students was chosen randomly to be the experimental group for the sample of the study to practice the dialogues via face- to- face and via google meet. This group post-tested by an observation checklist; results are calculated. At the end, the researcher concludes google meet has a great influence on students’ learning in the post-test scores, the t-test value is found to be 20.320 which is more than the tabulated value 2.042. Then, recommendations and suggestions for further studies are put forward.
Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.
Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, bu
... Show MoreThis study aims to investigate the effect of changing skins material on the strength of sandwich plates with circular hole when subjected to mechanical loads. Theoretical, numerical and experimental analyses are done for sandwich plates with hole and with two face sheet materials. Theoretical analysis is performed by using sandwich plate theory which depends on the first order shear deformation theory for plates subjected to tension and bending separately. Finite element method was used to analyse numerically all cases by ANSYS program.
The sandwich plates were investigated experimentally under bending and buckling load separately. The relationship between stresses and the ratio of hole diameter to plate width (d/b) are built, by
... Show MoreIn light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen
... Show MoreThere are many techniques for face recognition which compare the desired face image with a set of faces images stored in a database. Most of these techniques fail if faces images are exposed to high-density noise. Therefore, it is necessary to find a robust method to recognize the corrupted face image with a high density noise. In this work, face recognition algorithm was suggested by using the combination of de-noising filter and PCA. Many studies have shown that PCA has ability to solve the problem of noisy images and dimensionality reduction. However, in cases where faces images are exposed to high noise, the work of PCA in removing noise is useless, therefore adding a strong filter will help to im
... Show MoreThe triggering effect for the face pumping of Nd:YVO4 disc medium of 4×5×0.5 mm was investigated using bulk diode laser at different resonator cavity length in pulse mode and at repetition rate of 1.3kHz. The maximum emitted peak power was found to be 100, 82, and 66 mW for resonator lengths of 10, 13.5, and 17.5 cm respectively, while the threshold pumping power was found to be 41mW. The maximum emitted peak power obtained was 300 mW when using external triggering and 10cm length, with repetition of 3Hz.
One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca
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