Marine animals play an important role in the ecosystem. ‘Aquatic animals play an important role in nutrient cycles because they store a large proportion of ecosystem nutrients in their tissues, transport nutrients farther than other aquatic animals and excrete nutrients in dissolved forms that are readily available to primary producers’ (Vanni MJ 1) Fish images are captured by scuba divers, tourist, or underwater submarines. different angles of fishes image can be very difficult to get because of the constant movement of the fish. In addition to getting the right angles, the images of marine animals are usually low-quality because of the water. Underwater cameras that is required for a good quality image can be expensive. Using AI could potentially increase the marine population by the help of classification by testing the usage of machine learning using the images obtained from the aquarium combined with advanced technology. We collect 164 fish images data from Georgia acquarium to look at the different movements.
The goal of this project is to predict the family of a protein based on the amino acid sequence of the protein. The structure and function of a protein are determined by the amino acid sequence that composes it. In the protein structure data set, each protein is classified according to its function. Categories include: HYDROLASE, OXYGEN TRANSPORT, VIRUS, SIGNALING PROTEIN, etc. dozens of kinds. In this project, we will use nucleic acid sequences to predict the type of protein. Although there are already protein search engines such as BLAST that can directly query the known protein families. But for unknown proteins, it is still important to use deep learning algorithms to predict their functions. Protein classification is a simpler problem than protein structure prediction. The latter requires the complete spatial structure of the protein, and the required deep learning model is extremely complex.