Abstract: The data mining can be referred as discovery of relationships in large databases automatically and in some cases it is used for predicting relationships based on the results discovered. Data mining plays an important role in various applications such as business organizations, e-commerce, health care industry, scientific and engineering. In the health care industry, the data mining is mainly used for Disease Prediction.the objective our works to predict the diagnosis of heart disease with reduced number of attributes. Here fourteen attributes involved in predicting heart disease. But fourteen attributes are reduced to six attributes by using Genetic algorithm. Subsequently three classifiers like Naive Bayes, Classification by Clustering and Decision Tree are used to predict the diagnosis of heart disease after the reduction of number of attributes

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Abstract: With the development of various services through the Web and especially with the emergence of electronic commerce, all suppliers of products and services are providing considerable efforts to secure against all possible fraudulent intrusions. It appears that biometrics is the only method that can satisfy the requirements of remote identity in terms of relevance and reliability. In this paper, we propose a client-server network architecture for a remote multimodal biometric identification. As a matter of fact, we use two modalities, namely, the human iris and his fingerprint in order to strengthen the security, since the unimodal biometric systems cannot always be used reliably to perform recognition. However, the association of the information presented by the various modalities may allow a precise recognition of the identity. Concerning the fusion of these two modalities, we used a new approach at the scores level based on a classification method by the decision tree and a combination

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Abstract: The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with lowquality data. Consequently, a major challenge in quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’ mobility and reliability. Decouplingthesetwotypesofuncertaintiesthroughlocationtracki ngwill raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling.

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Abstract: —In image search re-ranking, besides the well-known semantic gap, intent gap, which is the gap between the representation of users’ query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image clickthrough data, which can be viewed as the implicit feedback from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis—visually similar images should be close in a ranking list—and the strategy—images with higher relevance should be ranked higher than others—are widely accepted. To obtain satisfying search results, thus, image similarity and the level of relevance typicality are determinate factors correspondingly.

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