Browsing by Author "Diyasena, Deshanjali"

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Review on Decision Support Systems used for Resource Allocation in Health Crises
(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Diyasena, Deshanjali; Arambepola, Nimasha; Munasinghe, Lankeshwara
A disaster or crisis can be stated as a serious disruption occurring for a certain period of time, which could cause loss of human lives, properties, and disrupt the day-to-day life of people. Managing such situations is always a challenge due to various reasons. Especially, allocating and providing resources to manage disaster situations to restore the normal life of people is the main challenge in a disaster situation. Having a proper mechanism for resource allocation could save thousands of human lives as well as properties. Modern smart technologies play a vital role in designing and developing solutions for efficient and effective resource allocation mechanisms. For example, the COVID-19 pandemic has forced people to work from home using digital platforms. Those digital platforms have been able to support people to do their routine work while maintaining social distancing which minimizes the spread of Covid-19. On the other hand, those digital platforms provide an easy and fast way for healthcare officials to reach infected patients to provide necessary treatments and care. Present research critically reviews the past research on managing resources in health crises particularly falls under pandemics and epidemics.
A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Hewapathirana, Isuru; Kekayan, Nanthakumar; Diyasena, Deshanjali
As a result of rapid digitisation, online transactions using credit cards have become popular. With this, fraudulent activities have also increased considerably. Although many supervised and unsupervised machine learning techniques were proposed in past research for identifying fraudulent transactions, they do not fully utilize the tabular and hierarchical structure present in transaction datasets. Recently, the TabBERT neural network model was proposed to calculate row-wise embeddings that capture both inter and intra dependencies between transactions in tabular time series data. In this research, we present a systematic experimental framework to assess the effectiveness of applying the embeddings calculated using the TabBERT model for credit card fraud detection. We employ the calculated row embeddings for fraud detection using three unsupervised machine learning algorithms and two supervised machine learning algorithms. We perform our experiments on a synthetic dataset that has been generated using the TabGPT model. Overall, TabBERT-based embeddings increase the performance of the supervised learning models with the extreme gradient boosting model achieving a precision of 99% and an F1 score of 98%, and the multilayer neural network model achieving a precision of 97% and an F1 score of 95%. For unsupervised learning, the use of TabBERT embeddings increases the recall rate of K-means clustering algorithm by 0.19%.
A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection
(2022) Hewapathirana, Isuru Udayangani; Kekayan, Nanthakumar; Diyasena, Deshanjali
As a result of rapid digitisation, online transactions using credit cards have become popular. With this, fraudulent activities have also increased considerably. Although many supervised and unsupervised machine learning techniques were proposed in past research for identifying fraudulent transactions, they do not fully utilize the tabular and hierarchical structure present in transaction datasets. Recently, the TabBERT neural network model was proposed to calculate row-wise embeddings that capture both inter and intra dependencies between transactions in tabular time series data. In this research, we present a systematic experimental framework to assess the effectiveness of applying the embeddings calculated using the TabBERT model for credit card fraud detection. We employ the calculated row embeddings for fraud detection using three unsupervised machine learning algorithms and two supervised machine learning algorithms. We perform our experiments on a synthetic dataset that has been generated using the TabGPT model. Overall, TabBERT-based embeddings increase the performance of the supervised learning models with the extreme gradient boosting model achieving a precision of 99% and an F1 score of 98%, and the multilayer neural network model achieving a precision of 97% and an F1 score of 95%. For unsupervised learning, the use of TabBERT embeddings increases the recall rate of K-means clustering algorithm by 0.19%.