International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Enriched Neutrosophic Clustering With Knowledge Of Chaotic Crow Search Algorithm For Alzheimer Detection In Diverse Multidomain Environment

[Full Text]



Dhanusha C, Dr. A V Senthil Kumar



Alzheimer, neutrosophic clustering, chaos map, crow search algorithm, smart home, unsupervised learning, indeterminacy



The persons with alzheimer disease are severely impaired with their daily activities. Elderly peoples with mild cognitive impairment also have such issue but in a mild range. Alzheimer is a kind of dementia which results in chronic illness related with progressive memory loss, abstract thinking and lessen the intellectual abilities and cognitive loss. But detection of Alzheimer’s at earlier stages may stop further progression and automated detection and classification models are pretty challenging task. This paper focuses on developing an nature inspired unsupervised learning model whose objective is to handle the indeterminacy and ambiguity while clustering the dataset for alzheimer disease detection. This work uses two different heterogenous Multidomain datasets for prediction of alzheimer, CASAS dataset comprised of Activities of Daily living of the smart home residents and OASIS dataset consist of Clinical Dataset. This work proposed a neutrosophic clustering enabled with the intelligence of chaotic crow search algorithm. The neutrosophic clustering handles the indeterminacy by introducing three degrees of membership truthiness, falsity and indeterminacy. The centroids are optimized by applying chaotic crow search algorithm based on the inspiration of crow’s memory of food stored location and its searching strategy. The issue of local optima in conventional crow search algorithm is overwhelmed by applying chaos mapping which offers the ability of global optima to produce best solution in selecting most prominent cluster centroids. The performance results proved the efficacy of the proposed work in treating presence of noisy data and indeterminacy for better alzheimer disease detection.



[1] Zhu, C. Sheng, W.; and Liu, M. 2015. Wearable sensor based behavioral anomaly detection in smart assisted living systems. IEEE Transactions on Automation Science and Engineering 12(4):1225–1234.
[2] Hu C, Ju R, Shen Y, Zhou P, Li Q. Clinical decision support for Alzheimer’s disease based on deep learning and brain network. In: Communications (ICC), 2016 IEEE international conference on, IEEE. 2016. pp. 1–6.
[3] Arvesen, E.: Automatic Classification of Alzheimers Disease from Structural MRI. Master's thesis (2015)
[4] Brosch, T., Tam, R., Initiative, A.D.N.: Manifold learning of brain mrisby deep learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 633{640. Springer (2013)
[5] Gray, K.R.: Machine learning for image-based classification of Alzheimer’s disease, Ph.D. thesis, Imperial College London (2012)
[6] Gupta, A., Ayhan, M., Maida, A.: Natural image bases to represent neuroimaging data. In: ICML (3). pp. 987{994 (2013)
[7] Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer's disease diagnostics by adaptation of 3d convolutional network. In: Image Processing (ICIP), 2016 IEEE International Conference on. pp. 126{130. IEEE (2016)
[8] Koppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Ashburner, J., Frackowiak, R.S.: Automatic classification of mr scans in alzheimer's disease. Brain 131(3), 681{689 (2008)
[9] Liu, F., Shen, C.: Learning deep convolutional features for mri based alzheimer's disease classification. arXiv preprint arXiv:1404.3366 (2014)
[10] Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M.J, Multimodal neuroimaging feature learning for multiclass diagnosis of alzheimer's disease. IEEE Transactions on Biomedical Engineering 62(4), 1132{1140 (2015)
[11] Magnin, B., Mesrob, L., Kinkingn_ehun, S., P_el_egrini-Issac, M., Colliot, O., Sarazin, M., Dubois, B., Leh_ericy, S., Benali, H.: Support vector machine-based classification of alzheimers disease from whole-brain anatomical mri. Neuroradiology 51(2), 73{83 (2009)
[12] Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Toga, A.W., Thompson, P.M.: Comparison of adaboost and support vector machines for detecting alzheimer’s disease through automated hippocampal segmentation. IEEE transactions on medical imaging 29(1), 30 (2010)
[13] Tao S, Zhang T, Yang J, Wang X, Lu W. Bearing fault diagnosis method based on stacked autoencoder and softmax regression. In: Control conference (CCC), 2015 34th Chinese, IEEE. 2015. pp. 6331–5.
[14] FirouzehRazavi, Mohammad JafarTarokh, Mahmood Alborzi1, An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning, Journal of Big Data , (2019) 6:32
[15] R. Sivaranjani and A.V.Senthil Kumar “Efficient Health Monitoring Networks using Secured Energy Aware (SEA) Scheme” International Journal of Innovative Technology and Exploring Engineering, Volume 8, Issue 10, August 2019, pp: 3643- 3649
[16] M. Rathi and A.V.Senthil Kumar “Euler Movement Firefly Algorithm and Fuzzy Kernel Vector Machine Classifier for Keystroke Authentication” International Journal of Innovative Technology and Exploring Engineering, Volume 8, Issue 11, September 2019, pp: 2267- 2274
[17] M.Ilango and A.V.Senthil Kumar “Non Linear Differential Optimization for Quality Aware Resource Efficient Routing in Mobile Ad Hoc Networks” International Journal of Engineering and Advanced Technology, Volume 9, Issue 1, October 2019, pp: 1661- 1668
[18] A. Baraldi and P. Blonda, “A survey of fuzzy clustering algorithms for pattern recognition—Part II,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.29, no.6, pp.786–801, 1999
[19] F. Smarandache, A unifying field in logics: neutrosophic logic, Multiple-Valued Logic/An International Journal 8 (3), 385–438, 2002
[20] F. Smarandache, Neutrosophy, A new branch of philosophy, in multiple-valued logic, An International Journal 8(3), 297–384.2002
[21] F. Smarandache, Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Neutrosophic Set, Neutrosophic Probability and Statistics, University of New Mexico, Gallup Campus, Xiquan, Phoenix, 2002, p. 147
[22] Askarzadeh A (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct, 169, 1-12
[23] M. Saleh, and M. Haeri, “Comparision of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms, ”Applied Mathematics and Computation, vol. 187,pp. 1076-1085,2007.
[24] D. Cook. Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems, 2011.
[25] https://www.oasis-brains.org/
[26] Dhanusha C, .A.V Senthil Kumar. “Intelligent Intuitionistic Fuzzy with Elephant Swarm Behaviour Based Rule Pruning for Early Detection of Alzheimer in Heterogeneous Multidomain Datasets” ‘International Journal of Recent Technology and Engineering (IJRTE)’, ISSN: 2277-3878, Volume-8 Issue-4, November 2019. Page No.: 9291-9298.Scopous Indexed.