SLIIT Conference and Symposium Proceedings
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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.
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Publication Embargo Computational Model for Rating Mobile Applications based on Feature Extraction(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunaratnam, I.; Wickramarachchi, D.N.Google Play Store and App Store allow users to share their opinions and helps to measure users satisfaction level about the app through user comments. However, it's highly time-consuming to process all reviews manually. The usefulness of star ratings is limited for development teams since a rating represents an average of both positive and negative evaluations. Therefore, an automated solution is needed to systematically analyze reviews and other textual forms of data. The main objective of this research is to build a platform that rate apps by feature extraction and sentiment analysis to calculate the functionality index of apps based on metrics obtained by surveying 204 mobile phone users. The 5 topmost metrics obtained from them among the 16 metrics obtained from the literature review are usability, price, and frequency of updates, ad-freeness and battery consuming level. This research focuses on selected apps in music and audio category. To perform app rating indexes calculation of the overall app's reviews; data extraction, data cleaning, POS tagging, feature extraction, feature/feature values pairing, weighted feature rating, overall apps' rating and feature-wise app rating is done on textual data. The accuracy of the created model is measured by the level of satisfaction from users.Publication Embargo DNN Based Currency Recognition System for Visually Impaired in Sinhala(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gamage, C.Y.; Bogahawatte, J.R.M.; Prasadika, U.K.T.; Sumathipala, S.Recently researches have been conducted in the domain of currency recognition. The task of recognizing the currency notes has become challenging due to the distortion of the notes over time. Currency recognition systems in Sinhala for visually impaired people are rarely developed. To address this problem a research has been done and a relevant application has been implemented comprising three modules as Speech Recognition module, Currency Recognition module and Text to Speech Module. The major challenge in all three modules is to achieve a better accuracy using deep learning concepts. TensorFlow platform and Keras library were used to build the speech recognition neural network model for Sinhala spoken words. Deep learning neural networks were utilized for the development of currency recognition module and text to speech module.