Browsing by Author "Wickramaarachchi, Dilani"

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A community-based hybrid blockchain architecture for the organic food supply chain
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Thanujan, Thanushya; Rajapakse, Chathura; Wickramaarachchi, Dilani
This paper presents a novel blockchain architecture to incorporate community-level trust into the organic food supply chain by hybridizing Proof of Authority (PoA) and Federated Byzantine Agreement (FBA) consensus protocols. Community-level trust is an important aspect in the organic agriculture industry. Organic farming, in most parts of the world, happens in small scale farms where the farmers represent rural and less-privileged communities. Even though third-party certification systems exist for quality assurance in organic farming, due to many socio-economic reasons, participatory guarantee systems (PGS) have become a popular alternative among organic farmers and consumers. However, such participatory guarantee systems are still prone to frauds and have limitations in scalability as well. With the recent rise of blockchain technology, there is an emerging trend to adopt blockchain technology to enhance the credibility of organic food supply chains and mitigate the risk of fraudulent transactions. However, despite the popularity of participatory guarantee systems among organic farmer communities, the blockchain researchers have paid little attention to develop blockchain architectures by adopting the community-level trust into their consensus protocols. The hybrid consensus mechanism presented in this paper addresses that gap in existing blockchain research. Apart from discussing the details of the proposed blockchain architecture and the underlying consensus protocol, this paper also presents a qualitative analysis on the proposed architecture based on expert opinions.
Estimation of the incubation period of COVID-19 using boosted random forest algorithm
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Rathnayake, P. P. P. M. T. D.; Senanayake, Janaka; Wickramaarachchi, Dilani
Coronavirus disease was first discovered in December 2019. As of July 2021, within nineteen months since this infectious disease started, more than one hundred and eighty million cases have been reported. The incubation period of the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can be defined as the period between exposure to the virus and symptom onset. Most of the affected cases are asymptomatic during this period, but they can transmit the virus to others. The incubation period is an important factor in deciding quarantine or isolation periods. According to current studies, the incubation period of SARS-CoV-2 ranges from2 to 14 days. Since there is a range, it is difficult to identify a specific incubation period for suspected cases. Therefore, all suspected cases should undergo an isolation period of 14 days, and it may lead to unnecessarily allocation of resources. The main objective of this research is to develop a classification model to classify the incubation period using machine learning techniques after identifying the factors affecting the incubation period. Patient records within the age group 5-80 years were used in this study. The dataset consists of 500 patient records from various countries such as China, Japan, South Korea and the USA. This study identified that the patients' age, immunocompetent state, gender, direct/indirect contact with the affected patients and the residing location affect the incubation period. Several supervised learning classification algorithms were compared in this study to find the best performing algorithm to classify the incubation classes. The weighted average of each incubation class was used to evaluate the overall model performance. The random forest algorithm outperformed other algorithms achieving 0.78 precision, 0.84 recall, and 0.80 F1-score in classifying the incubation classes. To fine-tune the model AdaBoost algorithm was used.
Performance Optimization of Microservice Applications under Resource Constrained Environments
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Fernando, Ravindu; Wickramaarachchi, Dilani
Performance of microservice applications deployed on cloud platforms have a non-linear relationship with resources allocated to each service of the application. Applications can be limited by fixed resource budgets or operation costs. This study presents an automated framework utilizing equality constrained Bayesian Optimization (BO) on CPU, and Memory limits of individual services in a benchmark single node microservice application with the objective of minimizing latency and maximizing throughput. The model found configurations that achieve over 3 times improvement on latency and over 2 times improvement on the throughput of the default configuration. Particle Swarm Optimization (PSO) achieved similar improvement in performance with a higher number of iterations compared to BO.