Published Paper Details:

A "red tide" is a typical term utilized for an unsafe algal bloom. A standout amongst the best known HABs in the country happens about each midyear along Florida's Gulf Coast. This bloom, in the same way as other HABs(Harmful Algae Algal blossom), is brought about by infinitesimal Harmful Algae that produce poisons that murder fish and make shellfish hazardous to eat. With the goal that Red tide, a danger to marine condition, has been a significant issue all around as of late. The species and the number of inhabitants in red tide algae are two key lists to anticipate red tide flare-up. Previously, the assignment to perceive and characterize red tide green growth was attempted by marine environmentalists and taxonomists with magnifying instruments physically, which is tedious and requires considerable pro learning. To beat the restrictions of conventional red tide algal testing instruments, a robotized Harmful Algae order framework is out of luck. The Efficient classifiers in light of utilizing WHT change with Generalized feed forward (GFF) Neural Network. Another Cross-Validation dataset is utilized for credible examination of the proposed assembling calculation concerning essential execution measures, for example, MSE and solicitation exactness. The Average Classification Accuracy of GFF Neural Network containing one hidden layers with 17 PE's managed in a standard topology supposedly is unrivaled (97.50%) for Training and cross-approval. At long last, flawless tally has been conveyed reliant on the best classifier execution.