By Jyotsna Kumar Mandal, Suresh Chandra Satapathy, Manas Kumar Sanyal, Vikrant Bhateja
The booklet covers a variety of issues in computing device technology and knowledge know-how together with swarm intelligence, man made intelligence, evolutionary algorithms, and bio-inspired algorithms. it's a choice of papers offered on the First overseas convention on clever Computing and conversation (ICIC2) 2016. The top parts of the convention are clever Computing, clever communique, Bio-informatics, Geo-informatics, set of rules, images and snapshot Processing, Graph Labeling, internet protection, privateness and e-Commerce, Computational Geometry, provider Orient structure, and information Engineering.
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The above segmentation is veriﬁed on 35 MRI cases. 2 Material To evaluate the segmentation process output result, the data set used which consists of atlases of 35 pre-contrast T1-weighted MR breast, brain, chest, heart and liver MRI scans obtained from different patients. For screening test, the ages of the women are between 25 and 68 years. 5T), Magnetom Vision). The clinical imaging parameters is  used for whole segmentation process (Table 1). 76 ms A Comparative Framework of Probabilistic Atlas Segmentation … 11 3 Probabilistic Atlas Based Segmentation in Breast, Heart, Brain, Liver and Chest The authors of  presented a probabilistic atlas based method by using Bayesian framework.
Standard metrics: mean squared error (MSE), mean absolute error (MAE), signal to noise ratio (SNR), peak signal to noise ratio (PSNR), cross correlation (xcorr) and CPU consumption time have been computed to assess the performance the aforementioned techniques. From our study it is concluded that wavelet thresholding yields the best denoising and is hence the most suitable method for enhancement of real world SCG signals. K. K. Mandal et al. 1007/978-981-10-2035-3_5 37 38 Aditya Sundar and Vivek Pahwa 1 Introduction Seismocardiography involves the non invasive measurement of vibrations in the chest due myocardial movement.
Deng, J. Zhang, X. Yu, W. Zhang, and X. Zhang, ‘Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase microextraction with on-ﬁber derivatization,’ J. Chromatogr. B, vol. 810, no. 2, pp. 269–275, 2004. 14. -C. -J. Lin,–LIBSVM: A library for support vector machines,’ ACM Trans. Intell. Syst. ’, vol. 2, no. 3, 27, pp. 1–27, 2011. 15. C. J. Burges,—A tutorial on support vector machines for pattern recognition,’ Data Mining Knowl. Discovery’, vol. 2, no. 2, pp.
Proceedings of the First International Conference on Intelligent Computing and Communication by Jyotsna Kumar Mandal, Suresh Chandra Satapathy, Manas Kumar Sanyal, Vikrant Bhateja