Browsing by Author "Perera, Indika"

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An exploratory evaluation of replacing ESB with microservices in service-oriented architecture
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Weerasinghe, L. D. S. B.; Perera, Indika
With the continuous progress in technology during the past few decades, cloud computing has become a fast-growing technology in the world, making computerized systems widespread. The emergence of Cloud Computing has evolved towards microservice concepts, which are highly demanded by corporates for enterprise application level. Most enterprise applications have moved away from traditional unified models of software programs like monolithic architecture and traditional SOA architecture to microservice architecture to ensure better scalability, lesser investment in hardware, and high performance. The monolithic architecture is designed in a manner that all the components and the modules are packed together and deployed on a single binary. However, in the microservice architecture, components are developed as small services so that horizontally and vertically scaling is made easier in comparison to monolith or SOA architecture. SOA and monolithic architecture are at a disadvantage compared to Microservice architecture, as they require colossal hardware specifications to scale the software. In general terms, the system performance of these architectures can be measured considering different aspects such as system capacity, throughput, and latency. This research focuses on how scalability and performance software quality attributes behave when converting the SOA system to microservice architecture. Experimental results have shown that microservice architecture can bring more scalability with a minimum cost generation. Nevertheless, specific gaps in performance are identified in the perspective of the final user experiences due to the interservice communication in the microservice architecture in a distributed environment.
Impact of Feature Selection Towards Short Text Classification
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Jayakody, J.R.K.C.; Vidanagama, V.G.T.N.; Perera, Indika; Herath, H.M.L.K.
Feature selection technique is used in text classification pipeline to reduce the number of redundant or irrelevant features. Moreover, feature selection algorithms help to decrease the overfitting, reduce training time, and improve the accuracy of the build models. Similarly, feature reduction techniques based on frequencies support eliminating unwanted features. Most of the existing work related to feature selection was based on general text and the behavior of feature selection was not evaluated properly with short text type dataset. Therefore, this research was conducted to investigate how performance varied with selected features from feature selection algorithms with short text type datasets. Three publicly available datasets were selected for the experiment. Chi square, info gain and f measure were examined as those algorithms were identified as the best algorithms to select features for text classification. Moreover, we examined the impact of those algorithms when selecting different types of features such as 1-gram and 2-gram. Finally, we look at the impact of frequency-based feature reduction techniques with the selected dataset. Our results showed that info gain algorithm outperform other two algorithms. Moreover, selection of best 20% feature set with info gain algorithm provide the same performance level as with the entire feature set. Further we observed the higher number of dimensions was due to bigrams and the impact of n grams towards feature selection algorithms. Moreover, it is worth noting that removing the features which occur twice in a document would be ideal before moving to apply feature selection techniques with different algorithms.