# Identification of Shark Species Based on Their Dry Dorsal Fins through Image Processing
## 3 Results
### Neural Network  
Four experiments were conducted using a neural network that varied the number of neurons in the hidden layer, species, and noise in the images. The second experiment was the best neural network because we obtained a 90% efficiency with 20 neurons in the hidden layer. Efficiencies between 84% and 88% were obtained in the other runs. The experiments were conducted as follows. Table 2 shows the results from the first experiment with 37 shark species with a white background and one control group using ten neurons in the hidden layer. We repeated the neural network 15 times to determine the optimal neural network efficiency. The epochs are the number of cycles that the neural network performed to reach the global minimum value of the error function. Efficiency is a relative value that shows the ratio between the achieved result and the used resource. In this experiment, the best neural network achieved an efficiency of 88.9%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scientific Name** & **Dry Dorsal Fin** & **\% Sensitivity** & **\% Specificity** \\ \hline _Sphyrna leviini_ & 262 & 100 & 100 \\ _Sphyrna zygaena_ & 92 & 100 & 100 \\ _Sphyrna mokarran_ & 16 & 100 & 100 \\ _Lamna nasus_ & 9 & 100 & 100 \\ _Carchandon carchararias_ & 16 & 100 & 100 \\ _Carchahinus longinanus_ & 22 & 100 & 100 \\ _Carchharinus falciformis_ & 101 & 100 & 100 \\ _Alopias vulpinus_ & 24 & 100 & 100 \\ _Alopias pelagicus_ & 75 & 100 & 100 \\ _Alopias superceiliosus_ & 98 & 100 & 100 \\ _Isurus oxyrichus_ & 49 & 100 & 100 \\ _Isurus paucus_ & 3 & 100 & 100 \\ _Rhincodon typus_ & 4 & 100 & 100 \\ _Carchahinus acronotus_ & 4 & 100 & 100 \\ _Carchharinus brachyurus_ & 2 & 100 & 100 \\ _Carchharinus brevipinna_ & 22 & 100 & 100 \\ _Carchharinus isodon_ & 5 & 100 & 100 \\ _Carchharinus leucas_ & 19 & 100 & 100 \\ _Carchharinus limbatus_ & 51 & 100 & 100 \\ _Carchharinus obscurus_ & 27 & 100 & 100 \\ _Carchharinus perezi_ & 3 & 100 & 100 \\ _Carchharinus plumbeus_ & 1 & 100 & 100 \\ _Carchharinus sealei_ & 6 & 100 & 100 \\ _Carchharinus signatus_ & 2 & 100 & 100 \\ _Carchharinus taurus_ & 8 & 100 & 100 \\ _Galeocordo cavier_ & 33 & 100 & 100 \\ _Ginglymostoma cirratum_ & 2 & 100 & 100 \\ _Ginglymostoma unumi_ & 4 & 100 & 100 \\ _Mustelus lunulatus_ & 3 & 100 & 100 \\ _Mustelus mustelus_ & 5 & 100 & 100 \\ _Negarion acutildens_ & 2 & 100 & 100 \\ _Negarion breviostris_ & 2 & 100 & 100 \\ _Prionace glauca_ & 39 & 100 & 100 \\ _Rhizopriondon acutus_ & 1 & 100 & 100 \\ _Rhizopriondon longurio_ & 10 & 100 & 100 \\ _Sphyrna tiburo_ & 5 & 100 & 100 \\ _Triandon obesus_ & 2 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensitivity and specificity percentage of each of the dry dorsal fin species of sharks using the non-linear composite filter. Table 3 shows the second experiment with 37 shark species with a white background and one control group. We obtained an efficiency of 90% (shown in yellow) for the four neural networks. Table 4 shows the sensitivity and specificity percentage of each dry dorsal fin shark species using the neural network with 90% efficiency. In this table, we show the 100% sensitivity for _Carchanthus plumbus_, _Ginglymostoma ciratum_ and _Negaprion acutidens_. _Carharhinus limbatus_ had a sensitivity of 56.43%; this was the lowest percentage of all species.