Loans especially mortgage loans play a vital role in the banking industry of many countries. Loans to individuals are considered as more risky than business loans in many situations. Due to these two reasons, the efficiency of prediction of loan repayment credibility is important for the welfare of both households and banking system and hence it is also important for the entire society. Data mining can solve the problem by analysing historical data and predict the behaviour of a customer. For prediction purpose various tools are used. Classification is the popular modeling technique to predict the loan repayment capability of a customer. Classification can be performed using different algorithms and accuracy also varies with the algorithms. Fuzzy logic system maps the nonlinear an input data set to a scalar output data. Fuzzy logic system consists of four main parts such as Fuzzification, Rules, Inference engine and Defuzzification. In this research, a fuzzy based loan repayment capability prediction model is introduced. The main objective of this paper is to implement fuzzy model that incorporates artificial intelligence approach for the prediction. The experiments were being done using python programming and the data was selected from a premier banking institution.

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In today's world data mining is becoming an important area in terms of all business applications especially in the banking sector. In developing countries like India, bankers should be vigilant to fraudsters because they will create more problems to the banking organization. Application of data mining techniques helps the banks to look for hidden patterns in a group and discover unknown relationship in the data. Feature selection is a method used in data mining to select the most appropriate attributes for defining a relationship in a data set. It is very effective to build models based on these data mining techniques. There are several types of classifiers in data mining that helps to classify the records into two major groups based on the list of attributes. The proposed work is a comparative study of different types classifiers and evaluating the accuracy of the classifiers before and after applying the feature selection. After evaluating the results of experiment, it is easy to predict that feature selection is an important and necessary step during the process of data mining. From the results we can see that the performance metrics we obtained in different classifiers after applying feature selection is equal or better than that of before applying feature selection.

Implant level impressions are usually made with custom trays. This article describes the simultaneous use of open and closed tray transfer copings using stock tray.

Root canal treatment is said to be completely successful when the tooth is restored and comes back to normal function. A more complex restoration is required after endodontic treatment when compared to normal tooth restoration, because of factors such as extensive caries, post-treatment root canal dentin and even the economics condition of the patient. With the availability of newer high strength materials like lithium disilicate dentists are now able to offer better esthetics and high strength restoration which withstands heavy occlusal forces with even thin layers. Thus, the aim of this case series is to summarize the recent advances of post endodontic restorations, various new materials their indications depending on the remaining tooth structure and the teeth that needs to be restored.