Computational Intelligence in Data Mining - Volume 2: by Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal,

By Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal, Durga Prasad Mohapatra

The contributed quantity goals to explicate and tackle the problems and demanding situations that of seamless integration of the 2 middle disciplines of machine technological know-how, i.e., computational intelligence and knowledge mining. information Mining goals on the computerized discovery of underlying non-trivial wisdom from datasets through employing clever research suggestions. The curiosity during this learn region has skilled a substantial development within the final years because of key elements: (a) wisdom hidden in agencies’ databases may be exploited to enhance strategic and managerial decision-making; (b) the big quantity of knowledge controlled by means of businesses makes it very unlikely to hold out a guide research. The booklet addresses various tools and strategies of integration for boosting the general aim of information mining. The publication is helping to disseminate the data approximately a few leading edge, lively learn instructions within the box of knowledge mining, laptop and computational intelligence, besides a few present concerns and purposes of comparable topics.

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Extra info for Computational Intelligence in Data Mining - Volume 2: Proceedings of the International Conference on CIDM, 20-21 December 2014

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The population size is set to be 100. The algorithms stop after 20,000 function evaluations. Initial populations are generated by uniformly randomly sampling from the feasible search space. 8. e. n is 10, the number of decision variables. Table 1 shows the S metric and Δ metric obtained using all four algorithms. Table 1 shows that the S and Δ metric value for NSGA-II is less than other three algorithms and hence its performance is better among all. Table 2 shows the result obtained for Convergence (C) metrics.

The major security challenge in cloud is that the users cannot have direct control over the remotely stored data. To preserve the privacy of user’s data stored in the cloud, an efficient algorithm maintaining the confidentiality of data is proposed. We ensure that the data stored in the untrusted cloud server is confidential by developing a new data encryption algorithm. Unlike other encryption algorithms, our encryption algorithm needs lesser computation overhead. Encryption and decryption algorithms are developed in java and Remote Method Invocation (RMI) concepts are used for communication between client and server.

The values 0 in the first row, first column mean that no solution of the non-dominated population obtained by TS, GA and NSGA-II is dominated by solutions from final populations obtained by SA. From the result, it clear that the performance of NSGA-II significantly outperforms the competing algorithms in the considered optimal design of induction motor. The comparison time computed by the CPU is shown in Table 3. The mean time and the variance (var) of time for NSGA-II algorithm is less than other algorithms.

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