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

ISBN-10: 8132222075

ISBN-13: 9788132222071

ISBN-10: 8132222083

ISBN-13: 9788132222088

The contributed quantity goals to explicate and deal with the problems and demanding situations that of seamless integration of the 2 center disciplines of desktop technological know-how, i.e., computational intelligence and information mining. info Mining goals on the computerized discovery of underlying non-trivial wisdom from datasets by way of making use of clever research options. The curiosity during this learn zone has skilled a substantial development within the final years because of key elements: (a) wisdom hidden in enterprises’ databases will be exploited to enhance strategic and managerial decision-making; (b) the big quantity of knowledge controlled via organisations makes it most unlikely to hold out a guide research. The publication addresses diversified tools and methods of integration for reinforcing the general target of information mining. The publication is helping to disseminate the information approximately a few cutting edge, energetic study instructions within the box of knowledge mining, desktop and computational intelligence, besides a few present concerns and functions 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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In: International Conference on Communication and Computing (ICC-2014), pp. 134–139 (2014) A Context Sensitive Thresholding Technique for Automatic Image Segmentation Anshu Singla and Swarnajyoti Patra Abstract Recently, energy curve of an image is defined for image analysis. The energy curve has similar characteristics as that of histogram but also incorporates the spatial contextual information of the image. In this work we proposed a thresholding technique based on energy curve of the image to find out the optimum number of thresholds for image segmentation.

This algorithm is not vulnerable to brute force attack, cipher text only attack and known plaintext attacks. Comparing to other encryption algorithms, it takes only less computation overhead and the time for encryption and decryption is very less. The proposed scheme can be applied for massive data with minimum overhead. This algorithm can also be used for encryption of audio and video files. In future, this can be used for the encryption of data that is dynamically changing. Encryption for Massive Data Storage in Cloud 37 References 1.

5 Pareto front at different cardinality 7 Conclusion In this paper, the multi-objective design optimization based on NSGA-II and size equations are applied for the three phase induction motors. In order to effectively obtain a set of Pareto optimal solutions, ranking method is applied. From the results, we can select the balanced optimal solution between the power density and efficiency. In case of optimized model, the efficiency increases at 80 % and the power density is also increased 12 kW/kg, compared to the SA, TS and GA result of the initial model.

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Computational Intelligence in Data Mining - Volume 2: Proceedings of the International Conference on CIDM, 20-21 December 2014 by Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal, Durga Prasad Mohapatra


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