By Himansu Sekhar Behera, Durga Prasad Mohapatra
The publication is a set of fine quality peer-reviewed examine papers provided within the moment foreign convention on Computational Intelligence in facts Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India in the course of five – 6 December 2015. The two-volume complaints deal with the problems and demanding situations for the seamless integration of 2 center disciplines of laptop technological know-how, i.e., computational intelligence and knowledge mining. The ebook addresses assorted tools and methods of integration for boosting the final objective of knowledge mining. The publication is helping to disseminate the information approximately a few leading edge, energetic study instructions within the box of information mining, desktop and computational intelligence, besides a few present concerns and purposes of similar topics.
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Additional info for Computational Intelligence in Data Mining—Volume 2: Proceedings of the International Conference on CIDM, 5-6 December 2015
Eng. Tech. (IJET) 12 S. V. Divya 15. : Real-time EEG Analysis with Subject-Speciﬁc Spatial Patterns for a Brain-Computer Interface 16. : A self training semi-supervised SVM algorithm and its application in an EEG based brain computer interface speller system. Sci. Direct Pattern Recogn. Lett. 29 (2008) 17. : Classifying Human-robot interaction: an updated taxonomy 18. : A robotic wheelchair based on the integration of human and environmental observations. In: IEEE Robotics and Automation Magazine 26, March 2003 19.
HB-K means: an algorithm for high dimensional data clustering using bisecting k-means. In: Accepted for publication in International Journal of Applied Engineering Research, Scopus Indexed Journal 7. : A novel benchmark K-means clustering on continuous data. Int. J. Comput. Sci. Eng. (IJCSE) 3(8), 2974–2977 (2011) 8. : An improved hybridized K means clustering algorithm (IHKMCA) for high dimensional dataset and it’s performance analysis. Int. J. Comput. Sci. Eng. (IJCSE) 3(3), 1183–1190 (2011) 9.
And after that values of further blinks and attention will be recorded. This is plotted in the graph. The black curve in the graph represents the eye blink values and the red one indicates the attention values. 6 Conclusion Brain controlled mobile robots are now used in various applications like industries, medical ﬁelds etc. Now they are gradually entered into the daily life. The main thing that needed for controlling the mobile robots is the brain waves. A sensor based mechanism is used in this paper for controlling the mobile robots.