@article{Malik_Kumar_Sahoo_2017, title={A Novel Approach to Fish Disease Diagnostic System based on Machine Learning}, volume={5}, url={https://journals.scholarpublishing.org/index.php/AIVP/article/view/2809}, DOI={10.14738/aivp.51.2809}, abstractNote={<p>Real-Time identification automated system diagnoses fish disease i.e. Epizootic Ulcerative syndrome (EUS) which is caused by <em>Aphanomyces invadans</em>, a fungal pathogen. In this paper we propose a Real-Time fish disease diagnose system with better accuracy. In order to improve the accuracy we propose a combination (PCA-FAST-NN) which combine the  Principle component analysis (PCA) with Features from Accelerated Segment Test (FAST)feature detector using Machine Learning Algorithm(Neural Network) i.e. (PCA-FAST-NN) .The Experimentation has been done on the real images of  Epizootic Ulcerative syndrome (EUS) infected fish database and implemented in MATLAB environment.</p>}, number={1}, journal={European Journal of Applied Sciences}, author={Malik, Shaveta and Kumar, Tapas and Sahoo, A.K}, year={2017}, month={Mar.}, pages={49} }