# Identification of Shark Species Based on Their Dry Dorsal Fins through Image Processing
## 3 Results
### Neural Network
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Neural Network** & **Layer** & **Epochs** & **Time** & **\% Efficiency** & **Neurons** \\ \hline
38 & 1 & 1 & 128 & 44 min & 88.6 & 10 \\ \hline
38 & 2 & 1 & 127 & 43 min & 88.4 & 10 \\ \hline
38 & 3 & 1 & 127 & 20 min & 86.1 & 10 \\ \hline
38 & 4 & 1 & 122 & 49 min & 85.8 & 10 \\ \hline
38 & 5 & 1 & 138 & 44 min & 87.4 & 10 \\ \hline
38 & 6 & 1 & 125 & 43 min & 83.8 & 10 \\ \hline
38 & 7 & 1 & 128 & 77 min & 86.3 & 10 \\ \hline
38 & 8 & 1 & 128 & 46 min & 88.9 & 10 \\ \hline
38 & 9 & 1 & 128 & 47 min & 88.6 & 10 \\ \hline
38 & 10 & 1 & 127 & 22 min & 86.1 & 10 \\ \hline
38 & 11 & 1 & 127 & 43 min & 88.4 & 10 \\ \hline
38 & 12 & 1 & 122 & 44 min & 85.8 & 10 \\ \hline
38 & 13 & 1 & 138 & 37 min & 87.4 & 10 \\ \hline
38 & 14 & 1 & 125 & 40 min & 83.8 & 10 \\ \hline
38 & 15 & 1 & 128 & 60 min & 86.3 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: First experiment. Fifteen runs of the neural network with 37 species, 1 control group (38 “species”), and 10 neurons in the hidden layer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Neural Network** & **Layer** & **Epochs** & **Time** & **\% Efficiency** & **Neurons** \\ \hline
38 & 1 & 1 & 179 & 90 min & 87.8 & 20 \\ \hline
38 & 2 & 1 & 259 & 120 min & 87.6 & 20 \\ \hline
38 & 3 & 1 & 166 & 82 min & 87.8 & 20 \\ \hline
38 & 4 & 1 & 128 & 76 min & 86.3 & 20 \\ \hline
38 & 5 & 1 & 151 & 89 min  
The rest had between 65.34% and 99.04% sensitivity. The specificity was between 98.05% and 100%. We performed a third experiment based on the first two experiments. Nine shark species had only five images, which is why they were not considered in this experiment. There were 27 dry shark fin species with a white background and one control group. Table 5 shows the results of the third experiment, with 26 species and one control group. Here, we have three neural networks with an 89% efficiency. \begin{table}
\begin{tabular}{c c c c} \hline
**Scientific Name** &
\begin{tabular}{c} **Dry Dorsal Fins** \\ **without Noise** \\ \end{tabular} & **\% Sensitivity** & **\% Specificity** \\ \hline _Sphyrna leviuni_ & 262 & 92.4\% & 98.05\% \\ _Sphyrna zygaena_ & 92 & 66.96\% & 99.72\% \\ _Sphyrna mokarran_ & 16 & 78.21\% & 99.46\% \\ _Lamnu nasus_ & 9 & 91.34\% & 99.72\% \\ _Carcharodon carcharias_ & 16 & 88.10\% & 99.4\% \\ _Carcharthinus longimanus_ & 22 & 87.25\% & 99.86\% \\ _Carcharthinus falciformis_ & 101 & 65.34\% & 99.24\% \\ _Alopias pulpinus_ & 24 & 88.46\% & 99.72\% \\ _Alopias pelagicus_ & 75 & 83\% & 93.37\% \\ _Alopias superciliosus_ & 98 & 90.26\% & 99.70\% \\ _Isurus oxyrinchus_ & 49 & 71.15\% & 99.16\% \\ _Isurus paucus_ & 3 & 98.05\% & 99.89\% \\ _Rhincodon typus_ & 4 & 98.07\% & 100\% \\ _Carcharthinus acronotus_ & 4 & 96.15\% & 99.94\% \\ _Carcharthinus brachyus_ & 2 & 97.05\% & 99.91\% \\ _Carcharthinus brevijum_ & 22 & 92.85\% & 99.86\% \\ _Carchinus isodon_ & 5 & 98\% & 100\% \\ _Carcharthinus leucas_ & 19 & 85.57\% & 99.67\% \\ _Carcharthinus limbatus_ & 51 & 56.43\% & 99.91\% \\ _Carcharthinus obscurus_ & 27 & 89.74\% & 99.45\% \\ _Carcharthinus perezi_ & 3 & 95.14\% & 99.59\% \\ _Carcharthinus plumbeus_ & 1 & 100\% & 99.83\% \\ _Carcharthinus sealei_ & 6 & 95.28\% & 99.91\% \\ _Carchinus signatus_ & 2 & 97.05\% & 99.80\% \\ _Carcharthinus taurus_ & 8 & 95.37\% & 99.61\% \\ _Galexceduc cavier_ & 33 & 82.23\% & 99.89\% \\ _Ginglymostoma cirratum_ & 2 & 100\% & 99.78\% \\ _Ginglymostoma unami_ & 4 & 97.11\% & 100\% \\ _Mustelus lunulatus_ & 3 & 98.05\% & 99.89\% \\ _Mustelus mustelus_ & 5 & 99.04\% & 99.89\% \\ _Negaprioriu actiders_ & 2 & 100\% & 99.80\% \\ _Negaprion breviovstris_ & 2 & 99.01\% & 99.91\% \\ _Prinose glauca_ & 39 & 91.59\% & 99.64\% \\ _Rhizzoriondon acutus_ & 1 & 97.02\% & 99.94\% \\ _Rhizzoriondon longurio_ & 10 & 94.54\% & 99.91\% \\ _Sphyrna tiburo_ & 5 & 97.14\% & 99.86\% \\ _Triandon obesus_ & 2 & 99.01\% & 99.89\% \\ Random group & 80 & 93.91\% & 99.91\% \\ \hline \end{tabular}
\end{table}