Research Publications
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Publication Open Access Queer Identities in The Seven Moons of Maali Almeida by Karunatilaka ( 2022) and The Song of Achilles by Miller (2019): A Comparative Analysis from a South Asian Perspective(Faculty of Humanities and Sciences, SLIIT, 2024-12-31) Rodrigo, D; Jayamanna, HThis research study attempts to present a comparative analysis of the queer identities portrayed in The Seven Moons of Maali Almeida by Karunatilaka (2022) and The Song of Achilles by Miller (2019) from a South Asian perspective to examine how cultural, historical, and socio-political aspects of their respective geographical settings influence such identities in both novels and to identify the narrative techniques and characterizations used in them to portray their queer relationships. Moreover, the central questions that guide this study are: 1) how queer do the identities depicted through their main characters appear to be; and 2) and what similarities and differences emerge in the portrayal of their queer relationships. To achieve these objectives, a qualitative research approach is adopted where the data is collected by subjecting the respective novels to a thorough critical reading and extracting the relevant evidence from their narratives using critical discourse analysis. The data is analysed using techniques developed from queer theory, semiotics, and critical discourse analysis. Finally, this research attempts to reveal from a South Asian perspective the author’s expectation for the queer identities portrayed in the two relevant novels while illuminating on the fact that they both appeal for an inclusive society.Publication Embargo Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction(IEEE, 2019-12-05) Aryal, S; Nadarajah, D; Kasthurirathna, D; Rupasinghe, L; Jayawardena, CForecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.Publication Embargo Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction(2019-12-05) Nadarajah, D; Aryal, S; Kasthurirathna, D; Rupasinghe, L; Jayawardena, CForecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.