Background and Aim: Breast cancer is the abnormal cell growth in the breast. In both benign and malignant masses, there is rapid and high cell growth. Nowadays, due to the development of technologies, the diagnosis of diseases has become non-invasive and physicians attempts to diagnose the disease without surgery and based on internal organ images.
Methods & Materials: In this study, by using images prepared from the Digital Database for Screening Mammography (DDSM), a new method is proposed for detecting cancerous masses in the mammographic images using geometric features extraction and optimization of Support Vector Machine (SVM) parameters to classify breast cancer masses automatically. First, images were pre-processed and then boundaries were determined using threshold method. Next, morphological operators were used to improve these boundaries and the segmentation of images was carried out to classify cancerous masses. Finally, by using the SVM parameter optimization method, Grasshopper Optimization Algorithm (GOA), and 4-fold crossvalidation method, data were classified into two groups of benign and malignant (cancer) masses.
Ethical Considerations Images from DDSM database were used in this research, all images are open access in this database.
Results: The accuracy, sensitivity and specificity values for applying the Radial Basis Function (RBF) kernel in SVM classifier (before optimization process) were obtained 97%, 100% and 96, respectively. After optimization of SVM parameters by the GOA, it was reported 100% for all accuracy, sensitivity and specificity indices for applying linear kernel function, indicating the high accuracy of the proposed method. The average values of accuracy, sensitivity and specificity indices for applying all three SVM kernel functions after optimization were 95.83, 100 and 94.81%, respectively.
Conclusion: The extracted geometrical features from breast cancer masses are highly efficient for model training and the diagnosis of breast cancer. The GOA could improve the overall accuracy of the proposed method by optimizing the SVM parameters. The results showed the higher performance of the proposed method compared to other methods.

Introduction: The outbreak of covid-19 caused an impact on the process of education in schools and universities. Therefore, the current research was conducted with the aim of investigating the effectiveness of virtual education based on interactive multimedia, video and educational factor on the learning rate and cognitive load of students in the conditions of covid-19 postgraduate students.
Methods: The method of the present research was quasi-experimental. The statistical population of the research included all master's students in the field of educational sciences in the course of research methods in the academic year 2019-1400, in the number of 36 people who were present in three different classes.
Results: The statistical sample also included a census of the research population, 36 people who were randomly assigned to three groups of interactive multi-media based education (9 people), film-based education (16 people) and training agent (11 people) And they responded to PASS (1994) cognitive questionnaire. To analyze the data, univariate covariance analysis was used through SPSS-23 statistical software. This research was reviewed in Islamic Azad University - Arak Unit and approved with the ethics code IR.IAU.ARAK.REC.1401.096. Informed consent was obtained from the participants and they were assured that their information would be confidential
Conclusions: The results showed that there is a significant difference in learning variables and cognitive load between each of the interactive multimedia groups and the video with the educational factor compared to the traditional group, with an error level of 0.05, and these groups performed better in learning variables and cognitive load. It can be said that virtual education based on interactive multimedia and video can be used to increase learning and cognitive load and cause students' academic progress.