Aim and Background: The main purpose of this study was to review the effectiveness of applied behavioral analysis (ABA) approach on symptoms of autistic children during a five-year period.Methods and Materials: In this quasi-experimental study, 20 autistic children (3-7 years of age) were intervened by ABA approach at Isfahan Center of Autism for a period of five years (2007-12). Gilliam Autism Rating Scale (GARS) was filled out at baseline and at the end of the five-year period. Besides, the subjects were assessed by a checklist including cognitive comprehension, mathematical and verbal skills, and self-help at the beginning of the study and at the end of each year. After five years, all collected data was analyzed with SPSS19.Findings: Pretest and the posttest values of stereotypic behaviors, communication problems, social interactions, and GARS total scores were significantly different (P<0.001). Furthermore, significant improvements in cognitive comprehension, mathematical and verbal skills, and self-help were observed after each year and at the end of the five-year period (P<0.001).Conclusions: On the whole, it can be concluded that ABA-based interventions can effectively reduce symptoms of autism and increase skills.

The purpose of this study was to investigate the effect of bankers’ behavioral biases on credit risk management. We examined whether and how individual emotional biases (i. e., loss aversion, optimism, overconfidence, and cognitive dissonance) affect the banks’ credit risk policy, including the risk assessment methods (i. e., quantitative vs. qualitative methods). Based on a sample of Tunisian banks, the results showed that the bankers’ emotional biases affect their preferences regarding the choice of credit risk assessment methods. The findings revealed that optimist, loss-averse, and overconfident bankers are more likely to adopt quantitative methods such as scoring when they assess the credit risk. The bankers with high cognitive dissonance, however, were found to have a high preference for a qualitative approach.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Background and Aim: In addition to medical care, psychological support is needed to treat depression in rehabilitated cardiac patients.In this study we have compared the effectiveness of two different methods i.e. behavior-intervention and cognitive management in decreasing intensity of depression in rehabilitated cardiac patients. Materials and methods: All cardiac patients who had undergone surgery Coronary Artery Bypass Graft (CABG) during 2005-2006 in Khatam-al-Anbia general hospital of Zahedan were included in the study. Sixty seven patients fulfilling the criteria for inclusion were divided randomly into three groups, each comprising of 15 patients, two groups as experimental and one group as control. We used dual therapeutic methods of behavioral management and cognitive management in the two experimental groups, no intervention was done in the control group and multi group dependent variables were defined as the degree of depression in rehabilitated cardiac patients which was assessed by scale of light depression.Beck depression test was utilized for data collection for pre and post-tests; unilateral variance analysis (ANOVA) was used for data analysis. Results: The average score of depression in both test groups showed remarkable difference before and after intervention, whilst subjects in the control groups did not show any intervention. Cognitive method proved more efficient in lessening the intensity of depression.Conclusion: We can apply psychotherapeutic intervening procedures in order to enhance cure and improve to deal with psychological issues in such patients.

Attention to education in the realm of economics is a topic that has consistently been at the forefront of economic policymakers in developed countries. Their approach is typically based on a model of ideal individual and social life, and to implement this, they have established purposeful policies in the field of educational systems. The Qur'an and the Hadith of the Ahl al-Bayt (peace be upon them), with their comprehensive teachings, have also provided specific and organized recommendations in this regard, which can be utilized and implemented as behavioral methods of education. Since attention to economic education, along with the necessary knowledge and insight in this area, is fundamental in every society based on its educational doctrines and principles, it is essential for Islamic communities to refer to their authoritative sources, namely the Qur'an and Hadith. This study, titled "Behavioral Methods of Economic Education in the Qur'an and Hadith, " aims to extract and explain the methods of economic education by employing an analytical-descriptive approach and referring to the valuable texts of the Qur'an and the Hadith of the Ahl al-Bayt (peace be upon them). The research findings show that the Qur'an and the Hadith introduce various behavioral methods, including: encouraging work and effort, providing role models, preaching, urging contentment, proper planning, avoiding waste and extravagance, controlling consumption, practicing moderation, promoting self-sufficiency, enjoining good and forbidding evil, admonition and reminders, firm action, analysis, and prohibition

All human beings need to communicate with individuals in society to avoid being inactive and passive. They are able to communicate with others when they can transmit their thoughts, interests, and mentalities in the form of behavior. So, behavior as one of the most important aspects of human life is the only way for human beings to communicate with their surroundings. On the other hand, behavior is the most important context for incorrect or correct education in humans and one of the ways to obtain correct education is to learn about traumatic behaviors in education. Therefore, this study through a descriptive-analytical method seeks to explain the most important traumatic behaviors methods in education by relying on Alawite theology and using religious texts and data from contemporary sciences. The findings of the study show that the most important of these methods are punishment, improper encouragement and controversy, and strategies such as adherence to the principles of punishment and encouragement and its application in their own time and place and avoidance of hostile behavior toward the child.

Four important ways of religious education in the field of beliefs are inferred in this article through the nature of religion in the Holy Quran. We have examined two categories of “nature of religion” and “methods of religious education” for this purpose. All verses including the root of “د ـ ی ـ ن” (religion) have been investigated in order to properly understand the nature of religion in the Holy Quran. Then, we have benefited from the inference method to find the ways of religious education in the Holy Quran. In the inferential method, we have reached the “conclusion”, which is the method of religious education by using two premises, “normative” and “realistic”. The solutions of the Holy Quran to achieve the methods of religious education are explained in the following. For example, using the Scriptures directly to know the right actions is one of the solutions of the Holy Quran to reach the way of believing in the punishment of deeds. The achievement of religious education methods through discovering the nature of religion and explaining the purpose of education on the one hand and using the inferential method as a research method on the other hand is one of the innovations of this research. The achievements of this paper can be used to provide an educational theoretical model for formal and public education system.

Malware is emerging every day in a new form with new capabilities. But in the meantime, covert malware is trying to keep itself out of the sight of intrusion detection systems. This type of malware can continue to operate for years without being detected, stealing information from individuals, companies and even countries, causing irreparable damage. Therefore, timely detection of this type of malware is even more important. The aim of this study is to investigate the performance of the proposed method on standard malware datasets. First, the results of each processing step on the data are reviewed, and finally, the results of the proposed algorithm will be tested on the data and compared with other works. In this research, the effective features in detecting malware are determined using their behavioral analysis. The accuracy of malware detection has also been increased by using the cumulative random forest classifier. The evaluation criteria of the proposed algorithm are the accuracy, precision, sensitivity and F-criteria in classifying the classes in the data. The evaluation criteria in the proposed algorithms are compared with other methods and the results of these comparisons are presented in tables. The results show that malware detection using the proposed method has high accuracy, precision, sensitivity and F-criteria compared to other methods.