The process of manual species identification is a daunting task, so much so that the number of taxonomists is seen to be declining. In order to assist taxonomists, many methods and algorithms have been proposed to develop semi-automated and fully automated systems for species identification. While semi-automated tools would require manual intervention by a domain expert, fully automated tools are assumed to be not as reliable as manual or semi-automated identification tools. Hence, in this study we investigate the accuracy of fully automated and semi-automated models for species identification. We have built fully automated and semi-automated species classification models using the monogenean species image dataset. With respect to monogeneans’ morphology, they are differentiated based on the morphological characteristics of haptoral bars, an-chors, marginal hooks and reproductive organs (male and female copulatory organs). Landmarks (in the semi-automated model) and shape morphometric features (in the fully automated model) were extracted from four monogenean species images, which were then classified using k-nearest neighbour and artificial neural network. In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. As for the cross-validation, semi-automated models performed at 91.2 %, whereas fully automated models performed slightly higher at 93.75 %. and Corresponding author: Sarinder Kaur A/p Kashmir Singh
Breast cancer survival prediction can have
an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict
the survival prospects of patients, but newer algorithms such as deep learning can be tested with the
aim of improving the models and prediction accuracy. In this study, we used machine learning and deep
learning approaches to predict breast cancer survival in 4,902 patient records from the University of
Malaya Medical Centre Breast Cancer Registry. The
results indicated that the multilayer perceptron (MLP),
random forest (RF) and decision tree (DT) classifiers
could predict survivorship, respectively, with 88.2 %,
83.3 % and 82.5 % accuracy in the tested samples.
Support vector machine (SVM) came out to be lower
with 80.5 %. In this study, tumour size turned out to
be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction
accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.