A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition

In recent decades computer technology has considerable developed in use of intelligent systems for classification. The development of HCI systems is highly depended on accurate understanding of emotions. However, facial expressions are difficult to classify by a mathematical models because of natural quality. In this paper, quantitative analysis is used in order to find the most effective features movements between the selected facial feature points. Therefore, the features are extracted not only based on the psychological studies, but also based on the quantitative methods to arise the accuracy of recognitions. Also in this model, fuzzy logic and genetic algorithm are used to classify facial expressions. Genetic algorithm is an exclusive attribute of proposed model which is used for tuning membership functions and increasing the accuracy

Avaliação do Padrão de Senha Utilizado pelos Alunos de Tecnologia da Informação em Saúde

Today, technology is used as an important and foremost tool to facilitate human life. Computers, tablets, smart phones and massive social networks have affected the health and medical professions. Keeping security in such environments is one of the important and critical issue. Choose of a proper password for user accounts is a challenging matter for security in the digital environment. This study was conducted to consider the authentication level for the academic users. The sample size was found using the Gregis Morgan formula. Therefore, the patterns of passwords used in cyber environments by 200 students of IT Sciences were investigated and considered. Descriptive statistics have been used to analyze the gathered data in this study. The results showed that the passwords selected by the students were poor and imaginable for breaking.Hoy en día, la tecnología se utiliza como una herramienta importante y principal para facilitar la vida humana. Las computadoras, tabletas, teléfonos inteligentes y redes sociales masivas han afectado a la salud y las profesiones médicas. Mantener la seguridad en tales entornos es uno de los temas importantes y críticos. La elección de una contraseña adecuada para las cuentas de usuario es un tema difícil para la seguridad en el entorno digital. Este estudio se realizó para considerar el nivel de autenticación para los usuarios académicos. El tamaño de la muestra se encontró utilizando la fórmula de Gregis Morgan. Por lo tanto, se investigaron y consideraron los patrones de contraseñas utilizadas en los entornos cibernéticos por 200 estudiantes de ciencias de TI. Se han utilizado estadísticas descriptivas para analizar los datos recopilados en este estudio. Los resultados mostraron que las contraseñas seleccionadas por los estudiantes eran deficientes e imaginables para romperlas.Hoje, a tecnologia é usada como uma ferramenta importante e importante para facilitar a vida humana. Computadores, tablets, smartphones e grandes redes sociais afetaram as profissões médicas e de saúde. Manter a segurança nesses ambientes é uma questão importante e crítica. A escolha de uma senha adequada para contas de usuário é um assunto desafiador para a segurança no ambiente digital. Este estudo foi realizado para considerar o nível de autenticação para os usuários acadêmicos. O tamanho da amostra foi encontrado usando a fórmula de Gregis Morgan. Portanto, os padrões de senhas utilizados em ambientes cibernéticos por 200 estudantes de Ciências da TI foram investigados e considerados. Estatísticas descritivas foram utilizadas para analisar os dados coletados neste estudo. Os resultados mostraram que as senhas selecionadas pelos alunos eram ruins e imagináveis para quebrar

Effect of vitamin D supplementation on inflammatory markers and total antioxidant capacity in breast cancer women using a machine learning technique

Aim: This study aimed to establish a learning system using an artificial neural network (ANN) to predict the effects of vitamin D supplementation on the serum levels of vitamin D, inflammatory factors, and total antioxidant capacity (TAC) in women with breast cancer. Methods: The data set of the current project was created from women with breast cancer who were referred to the Shafa State Hospital of Patients with Cancers in Ahvaz city, Iran. Modeling was implemented using the data set at the serum levels of vitamin D, tumor necrosis factor-α (TNF-α), transforming growth factor β (TGF-β), and TAC, before and after vitamin D3 supplement therapy. A prediction ANN model was designed to detect the effects of vitamin D3 supplementation on the serum level changes of vitamin D, inflammatory factors and TAC. Results: The results showed that the ANN model could predict the effect of vitamin D3 supplementation on the serum level changes of vitamin D, TNF-α, TGF-β1, and TAC with an accuracy average of 85%, 40%, 89.5%, and 88.1%, respectively. Conclusions: According to the findings of the study, the ANN method could accurately predict the effect of vitamin D3 supplementation on the serum levels of vitamin D, TNF-α, TGF-β1, and TAC. The results showed that the proposed ANN method can help specialists to improve the treatment process more confidently in terms of time and accuracy of predicting the influence of vitamin D supplementation on the factors affecting the progression of breast cancer (https://www.irct.ir/ identifier: IRCT2015090623924N1)

The Effect of Noise in Educational Institutions on Learning and Academic Achievement of Elementary Students in Ahvaz, South-West of Iran

Background The learning environment dramatically affects the learning outcomes of students. Noise, inappropriate temperature, insufficient light, overcrowded classes, misplaced boards and inappropriate classroom layout all make up factors that could be confounding variables distracting students in class. This study was conducted to examine the effect of noise in educational institutions on the academic achievement of elementary school students in the academic year 2015-2016 in Ahvaz. Materials and Methods This study is applied and it is survey in terms of the nature of study. The population of the study included all male elementary school students in Ahvaz, of whom 210 students were selected randomly as the sample of the study. Cluster sampling was done by appropriate allocation. Questionnaires were randomly distributed among students. Data collection tools included Hermance’s achievement motivation questionnaire and the researcher-constructed questionnaire (observation checklist to examine the physical parameters of noise in educational institutions) and interviews with students. Validity of questionnaires was confirmed by content and construct validity, and the reliability of study was confirmed by Cronbach's alpha. The data of the study were analyzed using descriptive statistics (frequency, percentage, mean, standard deviation) and inferential statistics (factor analysis, t-test, Kolmogorov - Smirnov test and one-way ANOVA analysis) in SPSS21. Results The results showed that noise in educational institutions has a negative impact on learning and academic achievement of elementary school students in Ahvaz (

A heuristic model for optimizing fuzzy knowledge base in a pattern recognition system

341-347This study presents a genetic algorithm (GA) to optimize performance of a fuzzy system for reconition of facial expression from images. In proposed model, a Mamdani-type fuzzy rule based system recognizes emotions, and a GA is used to improve accuracy and robustness of the system. To evaluate system performance, images from FG-Net (FEED) and Cohn-Kanade database were used to obtain the best functions parameters. Proposed model under training process not only increased accuracy rate of emotion recognition but also increased validity of the model in adverse conditions

EVALUATION OF AHVAZ JUNDISHAPUR UNIVERSITY OF MEDICAL SCIENCES RANKING IN IRAN, MIDDLE EAST AND NEIGHBORING COUNTRIES IN 2016, A WEBOMETRICS ANALYSIS

University websites are of great importance to show the scientific activities of academic institutions. Qualitative and quantitative promotion of these websites, enhances the chance of recovery and visibility of medical universities in the cyber world. Ranking of Webometrics represents the degree of annual scientific activities appeared in universities website. In this study, four indicators including Size, Visibility, Richfiles and Google scholars indexing as the main indicators of Webometrics were analyzed in the Ahvaz Jundishapur University of Medical Scinces website. Moreover, it was evaluated in comparison with websites of other top medical universities in Iran, middle east and Iran neighboring countries. The results showed that in terms of Webometrics ranking, Lomonosov Moscow State University of Russia, King of Saudi Arabia, Tehran University of Medical Sciences, and Shahid Beheshti of Tehran were ranked first to fifth respectively. Evaluation of Ahvaz University of Medical Sciences based on the indicators showed that this site has not been successful in most of these indicators. The information provided on the web pages was not attractive at the international level. Moreover, weakness of the site in terms of Search Engine Optimization (SEO), English language of the content as well as few number of English web pages and backlinks for the university website were identified. Keywords: Webometrics, Ranking of websites, Search engines, University visibility, Ahvaz Jundishapur University of Medical Science

EVALUATION OF AHVAZ JUNDISHAPUR UNIVERSITY OF MEDICAL SCIENCES RANKING IN IRAN, MIDDLE EAST AND NEIGHBORING COUNTRIES IN 2016, A WEBOMETRICS ANALYSIS

University websites are of great importance to show the scientific activities of academic institutions. Qualitative and quantitative promotion of these websites, enhances the chance of recovery and visibility of medical universities in the cyber world. Ranking of Webometrics represents the degree of annual scientific activities appeared in universities website. In this study, four indicators including Size, Visibility, Richfiles and Google scholars indexing as the main indicators of Webometrics were analyzed in the Ahvaz Jundishapur University of Medical Scinces website. Moreover, it was evaluated in comparison with websites of other top medical universities in Iran, middle east and Iran neighboring countries. The results showed that in terms of Webometrics ranking, Lomonosov Moscow State University of Russia, King of Saudi Arabia, Tehran University of Medical Sciences, and Shahid Beheshti of Tehran were ranked first to fifth respectively. Evaluation of Ahvaz University of Medical Sciences based on the indicators showed that this site has not been successful in most of these indicators. The information provided on the web pages was not attractive at the international level. Moreover, weakness of the site in terms of Search Engine Optimization (SEO), English language of the content as well as few number of English web pages and backlinks for the university website were identified. Keywords: Webometrics, Ranking of websites, Search engines, University visibility, Ahvaz Jundishapur University of Medical Science