A novel approach to resource allocation mechanism in artificial immune recognition system: Fuzzy resource allocation mechanism and application to diagnosis of atherosclerosis disease

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4163 LNCS, Issue , 2006, Pages 244-255

  Polat, Kemal ^a   Kara, Sadik ^b   Latifoǧlu, Fatma ^b   Güneş, Salih ^a  

^a Dept of Electrical and Electronics Engineering   (Turkey)

^b ERCIYES UNIVERSITY   (Turkey)

Author keywords

Artificial Immune Recognition System (AIRS); Atherosclerosis disease; Carotid artery; Fast fourier transformation; Fuzzy resource allocation mechanism

Indexed keywords

BENCHMARKING; DIAGNOSIS; DISEASES; FAST FOURIER TRANSFORMS; FUZZY SETS; LEARNING SYSTEMS; PATTERN RECOGNITION SYSTEMS; RESOURCE ALLOCATION; TUMORS;

ARTIFICIAL IMMUNE RECOGNITION SYSTEM (AIRS); ATHEROSCLEROSIS DISEASE; CAROTID ARTERY; FUZZY RESOURCE ALLOCATION MECHANISM;

IMMUNOLOGY;

EID: 33751010761     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11823940_19     Document Type: Conference Paper

References (14)

1. T. Hirai, S. Sasayama, T. Kawasaki and S. Yagi,: Stiffness of systemic arteries in patients with myocardial infarction. A noninvasive method to predict severity of coronary atherosclerosis, Circulation, (1989) 78-86.
2. C. Stefanadis, C. Stratos, H. Boudoulas, C. Kourouklis and P. Toutouzas,: Distensibility of the ascending aorta, comparison of invasive and non-invasive techniques in healthy men and in men with coronary artery disease. Eur. Heart J., (1990) 990-996.
3. A.M. Dart, F. Lacombe, J.K. Yeoh, J.D. Cameron, G.L. Jennings, E. Laufer and D.S. Esmore,: Aortic distensibility in patients with isolated hyper-cholesterolaemia, coronary artery disease, or cardiac transplant. Lancet, (1991) 270-273.
4. D. Evans,: Doppier Signal Analysis. Ultrasound in Med. & Biol., 26 (2000)13-15.
5. V. D. Saini, N. C. Nanda & D. Maulik,: Basic principles of ultrasound and Doppler effect. Doppler echocardiography, 1993.
6. B. Sigel,: A brief history of Doppler ultrasound in the diagnosis of peripheral vascular disease. Ultrasound Med. Biol., 24 (1998)169-176.
7. M. M,uller, P. Ciccotti, W. Reiche, T. Hagen,: Comparison of color flow Doppler scanning, power Doppler scanning, and frequency shift for assessment of carotid artery stenosis. J. Vase. Surg., 34(2001) 1090-1095.
8. D.H. Evans, W.N. McDicken, R. Skidmore, J.P. Woodcock,: Doppler Ultrasound: Physics. Instrumentation and Clinical Applications, 1989.
9. P.J. Vaitkus, R.S.C. Cobbold, K.W. Johnston,: A comparative study and assessment of Doppler ultrasound spectral estimation techniques part II: methods and results. Ultrasound Med. Biol., 14(1988) 673-688.
10. Van Asten WN, Beijneveld WJ, Pieters BR et al.,: Assessment of aortoiliac obstructive disease by Doppler spectrum analysis of blood flow velocities in the common femoral artery at rest and during reactive hyperemia. Surgery, 109(1991) 633-639.
11. Watkins, A.: AIRS: A Resource Limited Artificial Immune Classifier, A Thesis Submitted to the Faculty of Mississippi State University (2001).
12. Watkins, A., and Timmis, J.: Artificial Immune Recognition System (AIRS): Revisions and Refinements, Proc. Int. Conf. on Artificial Immune Systems (ICARIS), (2002) 99-106.
13. Goodman DE, Boggess L, Watkins A.: An Investigation into the source of power for AIRS, an artificial immune classification system. In Proceedings of the International Joint Conference on Neural Networks (IJCNN'03), Portland, Oregon July (2003) 1678-1683.
14. Kohavi, R, and Provost, F.,: Glossary of Terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, 30 (1998) 2/3.