”Varför tar man för givet att feminismen måste vara sekulär?” : En studie om muslimska feminister i svensk kontext

The purpose of this study is to examine how two Muslim feminists perceive themselves to betreated by the Swedish majority society and within the secular feminist movement. Thesurvey was conducted using qualitative method with a total of two interviews. For the study'stheoretical perspectives, I have used postcolonialism and postcolonial feminism. The result ofthe survey and the analysis show that the informants say that they face an image of Muslimwomen as considered being under oppression. The informants believe that this stereotypicalimage has its origin from the colonial period. The question that is most important for themwithin feminism is to be treated as a feminist and as a Muslim without being questioned. Theyfeel like it's hard to identify with the Swedish secular feminism, but they also feel that thegroup of Swedish secular feminists have a difficulty identifying themselves with Muslimwomen too. Consider this, one of the informants does not feel welcome among Swedishsecular feminism while the other one never had an interest in becoming a member of itbecause she did not consider them to strive for the same goal as herself. The informantsclaims that there are opportunities for them to speak in the public debate, but as Muslimfeminists they are facing a bigger struggle.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.