Browsing by Author "Rathnayaka, R.M.K.T."

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Behavior Segmentation based Micro-Segmentation Approach for Health Insurance Industry
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Nandapala, E.Y.L.; Jayasena, K.P.N.; Rathnayaka, R.M.K.T.
To manage the company’s future growth, the relationship between companies and customers is important. This can be referred to as Customer Relationship Management (CRM). By applying the micro-segmentation process companies can succeed in this CRM process. Micro-segmentation is a breakdown into micro-segments of the entire data collection. The user can easily be deeply defined with this segmentation process. Demographic segmentation is a breakdown of the dataset based on the consumers’ age, gender, etc. Behavior segmentation is diving the whole dataset based on customers’ behaviors. RFM analysis is a behavioral segmentation process based on consumer’s behaviors. There is no exact way to precisely conduct micro-segmentation. Thus, this study proposed a new micro-segmentation process. That is applying demographic segmentation with the support of the RFM analysis. This method can easily determine the customers’ behaviors accurately and deeply. Insurance companies offer different types of insurance and health insurance is the most critical insurance type for humans. By applying the proposed method in this research, health insurance companies can determine the policyholder’s behaviors, claiming patterns, claiming chargers, and other information precisely. Furthermore, health insurance providers can effectively manage their claims using this knowledge.
Embargo
Experimental Determination of CNN Hyper- 􀀳arameters for Tomato Disease Detection 􀁘sing Leaf Images
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunarathna, M.M.; Rathnayaka, R.M.K.T.
Today, deep learning has become an emerging topic widely used in pattern recognition and classification problems. The design choice of the deep learning models entirely depends on who it's going to create. Still, it requires prior experience because identifying the best combination of parameters is a challenging task. So, the main objective of this study is to develop an accurate model for tomato disease classification while exploring the possible range of parameters that highly affects the performance of the Convolutional Neural Network (CNN). A simple CNN model was first built and train from scratch by using 22930 tomato leaf images collected from the Plant Village dataset in Kaggle. The model was tested for many cases by changing the values of a set of parameters while keeping other parameters constant. Finally, performance metrics were evaluated for every chosen parameter comparing with the base model. The highest prediction accuracy, training accuracy, and validation accuracy achieved from the study are 92%, 94%, and 92%, respectively. Rather than offering a guess, this study can, at most, give a definite answer that will assist new researchers in understanding how the accuracy and loss vary for every parameter within the area of tomato plant disease classification.