HRFSVM: identification of fish disease using hybrid Random Forest and Support Vector Machine

Aquaculture fish diseases pose a serious threat to the security of food supplies. Fish species vary widely, and because they resemble one another so much, it is challenging to distinguish between them based solely on appearance. To stop the spread of disease, it is important to identify sick fish as soon as possible. Due to a lack of necessary infrastructure, it is still difficult to identify infected fish in aquaculture at an early stage. It is essential to promptly identify sick fish to stop the spread of disease. The purpose of this work is to suggest a machine learning technique based on the DCNN method for identifying and categorizing fish diseases. To solve problems involving global optimization, this paper suggests a brand-new hybrid algorithm called the Whale Optimization Algorithm with Genetic Algorithm (WOA-GA) and Ant Colony Optimization. In this work, for classification, the hybrid Random Forest algorithm is used. To facilitate the increased quality, distinctions between both the proposed WOA-GA-based DCNN architecture and the presently used methods for machine learning have been made. The effectiveness of the proposed detection technique is done with MATLAB. Performance metrics like sensitivity, specificity, accuracy, precision, recall, F-measure, NPV, FPR, FNR, and MCC are compared to the performance of the proposed technique.