TELKOMNIKA Telecommunication, Computing, Electronics and Control
Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus Scylla

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus Scylla

Classification
Machine learning
Mud crab
Multi-stage classification
S. olivacea
Scylla

Recently, the mud-crab farming can help the rural population economically.
However, the existing parasite in the mud-crabs could interfere the long live
of the mud-crabs. Unfortunately, the parasite has been identified to live in
hundreds of mud-crabs, particularly it happened in Terengganu Coastal Water,
Malaysia. This study investigates the initial identification of the parasite
features based on their classes by using machine learning techniques. In this
case, we employed five classifiers i.e logistic regression (LR), k-nearest
neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine
(SVM), and linear discriminant analysis (LDA). We compared these five
classfiers to best performance of classification of the parasites. The
classification process involving three stages. First, classify the parasites into
two classes (normal and abnormal) regardless of their ventral types. Second,
classified sexuality (female or male) and maturity (mature or immature).
Finally, we compared the five classifiers to identify the species of the parasite.
The experimental results showed that GNB and LDA are the most effective
classifiers for carrying out the initial classification of the rhizocephalan
parasite within the mud crab genus Scylla.

Rozniza Ali, Muhamad Munawar Yusro, Muhammad Suzuri Hitam, Mhd Ikhwanuddin

http://journal.uad.ac.id/index.php/TELKOMNIKA

Sep 16, 2020

peri irawan

pdf

english

text