TY - GEN
T1 - Classification of Bio-optical signals using soft computing tools
AU - Nayak, G. Subramanya
AU - Puttamadappa, C.
AU - Kamath, Akshata
AU - Sudeep, B. Raja
AU - Kavitha, K.
PY - 2008
Y1 - 2008
N2 - The identification of the state of human skin tissues is discussed here. The Bio-optical signals recorded in vitro have been analyzed by extracting various statistical features. Using LAB VIEW 7.1 programs/tools, different statistical features are extracted from both normal and pathology spectra. Each spectrum is flittered and normalized. Then different features like Skewness, summation, median residuals, power spectral density, etc were extracted. The values of the feature vector reveal information regarding tissue state. The values of the feature vector reveal information regarding tissue state. These parameters have been analyzed for discrimination between normal and pathology conditions. For analysis, a specific data set has been considered. Further discrimination between normal and pathology spectra is also be achieved by using MATLAB @6.1 tool based classical multilayer feed forward neural network with back propagation algorithm
AB - The identification of the state of human skin tissues is discussed here. The Bio-optical signals recorded in vitro have been analyzed by extracting various statistical features. Using LAB VIEW 7.1 programs/tools, different statistical features are extracted from both normal and pathology spectra. Each spectrum is flittered and normalized. Then different features like Skewness, summation, median residuals, power spectral density, etc were extracted. The values of the feature vector reveal information regarding tissue state. The values of the feature vector reveal information regarding tissue state. These parameters have been analyzed for discrimination between normal and pathology conditions. For analysis, a specific data set has been considered. Further discrimination between normal and pathology spectra is also be achieved by using MATLAB @6.1 tool based classical multilayer feed forward neural network with back propagation algorithm
UR - https://www.scopus.com/pages/publications/57749179310
UR - https://www.scopus.com/pages/publications/57749179310#tab=citedBy
U2 - 10.1109/SNPD.2008.9
DO - 10.1109/SNPD.2008.9
M3 - Conference contribution
AN - SCOPUS:57749179310
SN - 9780769532639
T3 - Proc. 9th ACIS Int. Conf. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2008 and 2nd Int. Workshop on Advanced Internet Technology and Applications
SP - 661
EP - 663
BT - Proc. 9th ACIS Int. Conf. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2008 and 2nd Int. Workshop on Advanced Internet Technology and Applications
T2 - 9th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2008 in conjunction with 2nd International Workshop on Advanced Internet Technology and Applications, AITA 2008
Y2 - 6 August 2008 through 8 August 2008
ER -