A note on belief structures and s-approximation spaces

We study relations between evidence theory and S-approximation spaces. Both theories have their roots in the analysis of Dempsterchr('39')s multivalued mappings and lower and upper probabilities, and have close relations to rough sets. We show that an S-approximation space, satisfying a monotonicity condition, can induce a natural belief structure which is a fundamental block in evidence theory. We also demonstrate that one can induce a natural belief structure on one set, given a belief structure on another set, if the two sets are related by a partial monotone S-approximation space

A note on belief structures and s-approximation spaces

We study relations between evidence theory and S-approximation spaces. Both theories have their roots in the analysis of Dempsterchr('39')s multivalued mappings and lower and upper probabilities, and have close relations to rough sets. We show that an S-approximation space, satisfying a monotonicity condition, can induce a natural belief structure which is a fundamental block in evidence theory. We also demonstrate that one can induce a natural belief structure on one set, given a belief structure on another set, if the two sets are related by a partial monotone S-approximation space

Modeling a teacher in a tutorial-like system using Learning Automata

The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students

Social�capital determinants of the women with diabetes: a population�based study

Introduction: Social-capital level contributes to clinical factors and health outcomes of patients suffering from diabetes. Considering the social determinants of type 2 diabetes patients could benefit to prevention of diabetes complications especially in women population. This study aims to determine social capital determinants in women with diabetes. Methods: Four hundred and thirty-five women with diabetes take-part in this cross-sectional, multi-centric study. The data was completed by a demographic questionnaire and the Social Capital instrument (SC-IQ). This study is investigating demographic (age, gender, BMI, marital, educational and social-economic status), and lifestyle factors (physical activity, nutrition), Diabetes status (HbA1c Level, medications, complications, duration of diabetes), general health status (life satisfaction, self-rated health, physical activity, and depression) and Social capital items (Value of life, Tolerance of Diversity, Neighborhood network, Family and Friends Connections, Work connections, Community participation, Feeling of trust and Safety and Proactivity). The descriptive statistics and linear regression models were used to assess the associations between social capital and determinants. Results: The mean age of participants was 50 (SD: 7.7), range 28�71 year. The mean social capital score was 77.8 (SD: 15.8). In linear regression analysis, results showed that women who had the greater score in total social-capital (�: 3.7, SE: 1.5) and Feeling of trust and Safety (�: 0.87, SE: 0.42) had vigorous physical activity and also women who had greater score in Neighborhood Connections had moderate physical activity in comparison with patients who had low physical activity. (�: 0.67, SE: 0.26 and �: 0.61, SE: 0.26).Also, the findings showed that women who had had a lower score in total social-capital (�: 6, SE: 1.47), Community participation (�: 1.44, SE: 0.37), Value of life (�: 1.71, SE: 0.24), Family and Friends Connections (�: 0.88, SE: 0.25) and proactivity (�: 0.71, SE: 0.25) had depression in comparison with patients who had no depression. The findings revealed that instead of each year increase in the duration of diabetes, the total social-capital score had decreased about the half score (�: 0.48, SE: 0.21). Conclusions: Important social factors that make diabetes control are alterable to health interventions. The results of the current study suggest that social capital status may determine how effectively the women with diabetes have been managed. This initial finding permits subsequent experimental investigations to identify social strategies that can be valuable to improve diabetes control. © 2021, Springer Nature Switzerland AG

LACAIS: Learning Automata based Cooperative Artificial Immune System for Function Optimization

Abstract. Artificial Immune System (AIS) is taken into account from evolutionary algorithms that have been inspired from defensive mechanism of complex natural immune system. For using this algorithm like other evolutionary algorithms, it should be regulated many parameters, which usually they confront researchers with difficulties. Also another weakness of AIS especially in multimodal problems is trapping in local minima. In basic method, mutation rate changes as only and most important factor results in convergence rate changes and falling in local optima. This paper presented two hybrid algorithm using learning automata to improve the performance of AIS. In the first algorithm entitled LA-AIS has been used one learning automata for tuning the hypermutation rate of AIS and also creating a balance between the process of global and local search. In the second algorithm entitled LA-CAIS has been used two learning automata for cooperative antibodies in the evolution process. Experimental results on several standard functions have shown that the two proposed method are superior to some AIS versions