Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

The research seeks to achieve its goal of demonstrating the impact of applying banking governance variables on the financial performance of Islamic banks, and the independent research variables are represented by (X) by (the number of independent members in the board (X1), the number of directors in the board (X2), the number of committees emanating from the board ( X3), the percentage of shares owned by major shareholders in the board (X4), the number of members of the Sharia supervisory board (X5)), and the dependent variable (Y) is represented by (rate of return on assets (Y1), rate of return on equity (Y2)).

The research sample included (4) Islamic banks, namely (Iraqi Islamic Bank, National Islamic Bank, Jihan Islamic Bank,

The Iranian nuclear program was one of the most prominent issues in the international arena, in light of Iran's insistence on using it in various ways, and according to international changes, it came under pressure from the American side to impose packages of sanctions, which continued to our time, as it went to Russia and China to gain support in developing its nuclear activities. And, by virtue of the common interests that are linked between them, Iran has received great support from the last two countries in all fields to reach the final agreement in 2015, regarding asserting the peace of these activities, in addition to lifting all restrictions represented in the previous sanctions according to the Security Council resolutions, Aldo

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

 The problem of the research came through the following question (what is the effectiveness of active design in contemporary industrial products), and it emphasized the importance of research to make society with its members in a positive state of activity, and what can help the industrial product design to achieve this, but the goal of the study is (determining the characteristics and principles of design Active and its effectiveness in the industrial product), and the study was determined by industrial products related to daily, recreational and sports activity, The researcher relied on a form to determine the axes of analysis for the sample models, in order to reach the results of the research, the most important of which are: Realiz

This paper deals with constructing a model of fuzzy linear programming with application on fuels product of Dura- refinery , which consist of seven products that have direct effect ondaily consumption . After Building the model which consist of objective function represents the selling prices ofthe products and fuzzy productions constraints and fuzzy demand constraints addition to production requirements constraints , we used program of ( WIN QSB )  to find the optimal solution

One of the most popular causes for implant infection is dental plaque bacteria. Previous studies have shown the bactericidal effect of CO2 laser irradiation on bacteria associated with soft tissue surrounding the implant materials. No published studies have examined the effect of irradiation by CO2 laser on Streptococcus oralis and Staphylococcus aureus.The aim of this study was to evaluate the bactericidal effect of CO2 laser on bacteria that are causing dental implant infections. This study was carried out on two isolates of bacterial species out of 25 samples, isolated from patients having soft tissue infections around the dental implant. These two pure isolates including Streptococcus oralis and Staphylococcus aureus were identified

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt