iosr-JCE   Volume-18 ~ Issue-5 ~ Sep-Oct 2016

Abstract: Most frequently occurring recurrent chromosomal translocation allied with all subtype of leukemia are available in Mitel Mann Data base. We have retrieved about 55 such genome sequence from TIC dB data base with 100% similarity score and got noncoding sequence of chromosome 9 and 22 as positive example of fragile site. Another 55 housekeeping genome sequence is taken for classification purpose. For content based analysis we have extracted 20 features of frequency density of mono nucleotide and dinucleotide. The network is designed by determining hyper parameters like number of hidden layer...........

Keyword: Back propagation, cancer classification, leukemia, non-coding sequence

[1]. Albano, F., Anelli, L., Zagaria, A., Coccaro, N., D'Addabbo, P., Liso, V., Rocchi, M. & Specchia, G. (2010). Genomic segmental duplications on the basis of the t (9; 22) rearrangement in chronic myeloid leukemia. Oncogene, 29(17), 2509-2516.
[2]. Aminzadeh, F., Shadgar, B., &Osareh, A. (20SS, 3(1), 11-20.
[3]. Basu, S., &Plewczynski, D. (2010). AMS 3.0: prediction of post-translational modifications. BMC bioinformatics, 11(1), 1.
[4]. Berretta, R., &Moscato, P. (2010). Cancer biomarker discovery: the entropic hallmark. PLoS One, 5(8), e12262.
[5]. Cho, S. B., & Won, H. H. (2003, January). Machine learning in DNA microarray analysis for cancer classification. In Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003-Volume 19 (pp. 189-198). Australian Computer Society, Inc..

Abstract: We present a very computationally light and fast approximation algorithm and then verify it with genetic algorithm and simulated annealing. We show that our algorithm is on par with GA and SA in terms of output produced while having a tightly bounded time complexity. Our algorithm works best when there is a strong positive correlation between the reliability of a component and its cost. We present two algorithms with the same essence. One of them is system cost bounded and the other is target reliability bounded. Our proposed algorithm works on a subsystem level redundancy instead of component level redundancy

Keyword: Redundancy Allocation Problem, Genetic Algorithm, Simulated Annealing, Greedy Algorithm

[1] Barlow, R. & Proschan, R. (1981). Statistical theory of reliability and life testing, Silver Spring, MD: Madison.

[2] Boland, P. J. & El-Neweihi, E. (1995). Component redundancy versus system redundancy in the hazard rate ordering. IEEE Transactions on Reliability 44, 614–619.

[3] Marco Caserta and Stefan Voß (2015). A Discrete-Binary Transformation of the Reliability Redundancy Allocation Problem

[4] Seyed Mohsen Mousavi, Najmeh Alikar and Seyed Taghi Akhavan Niaki (2015) An improved fruit fly optimization algorithm to solve the homogeneous fuzzy series-parallel redundancy allocation problem under discount strategies.

[5] Misra KB & Sharma U. An efficient algorithm to solve integer programming problems arising in system-reliability design. IEEE Trans Reliab 1991 ; 40(1):81–91

Abstract: The detection of brain tumor is one of the most challenging tasks in the field of medical image processing, since brain images are very complicated and tumors can be analyzed efficiently only by the expert radiologists. Therefore, there is a significant need to automate this process. In this paper, a method for the automatic detection of the tumor from the brain magnetic resonance imaging (MRI) images has been proposed. For this, the region-based segmentation of the input MRI image is done............

Keyword: Brain tumor segmentation, FCM, Region growing, Wavelet decomposition

[1]. Aswathy, S. U., G. Glan Deva Dhas, and S. S. Kumar, "A survey on detection of brain tumor from MRI brain images", In Control, Instrumentation, Communication and Computational Technologies (ICCICCT), International Conference on, pp. 871-877, IEEE, 2014.
[2]. Gordillo, Nelly, Eduard Montseny, and Pilar Sobrevilla, "State of the art survey on MRI brain tumor segmentation", Magnetic resonance imaging, Elsevier, vol. 31, no. 8, pp. 1426-1438, 2013.
[3]. Viji, K.A. and JayaKumari, J., "Modified texture based region growing segmentation of MR brain images" In Information & Communication Technologies (ICT), IEEE Conference on, pp. 691-695, IEEE, April 2013. [4]. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A. and Moriarty, T. "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data", IEEE transactions on medical imaging, vol. 21, no. 3, pp.193-199, IEEE, 2002.
[5]. Aswathy, S. U., G. Glan Deva Dhas, and S. S. Kumar, "A survey on detection of brain tumor from MRI brain images", In Control, Instrumentation, Communication and Computational Technologies (ICCICCT), International Conference on, pp. 871-877, IEEE, 2014.