SLIIT Conference and Symposium Proceedings

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/295

All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

Browse

Filters

2019 - 201912020 - 20252

Advanced Search

Filter by

Settings

Has files: YesSubject: search.filters.subject.Neural Networks

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationOpen Access
    Bat.CG: Development of a Customizable Cricket Character Generator Web Application for Enhanced Broadcasting Experience
    (SLIIT City UNI, 2025-07-08) Kiriyalagammana, P. P; Niranga, G.D.H
    Cricket broadcasting has evolved significantly with technological advancements, yet traditional systems remain fragmented and technically complex for broadcasters. This research presents Bat.CG (Cricket Character Generator), an innovative web-based application that integrates customizable broadcasting graphics with realtime scoring, neural network-driven predictions, and enhanced audience interaction. The system addresses critical gaps in existing broadcasting infrastructure by providing a unified platform that eliminates the dependency on separate Character Generator (CG) and ball-by-ball scoring systems. Through comprehensive market research involving 81 industry professionals, this study identified key requirements including customizable graphics (99% demand), emergency score updating capabilities (87.7% essential), and integrated CG-scoring systems (49.4% extremely valuable). The proposed solution utilizes the MERN stack (MongoDB, Express.js, React, Node.js) architecture with hybrid neural network and regression models for match predictions. Key innovations include drag-and-drop graphic customization without programming knowledge, real-time data synchronization with sub-second latency, role-based access control, and interactive viewer engagement features. The system's modular design ensures scalability, security, and accessibility while maintaining professional broadcast quality. This research contributes to democratizing cricket broadcasting technology and establishing a foundation for future sports media innovations in developing regions.
  • Thumbnail Image
    PublicationEmbargo
    Domain Specific Conversational Intelligence: Voice Based E-Channeling System
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerathunga, W.A.H.; Lokugamage, G.N.; Hariharan, V.; Yahampath, A.D.N.H.; Kasthurirathna, D.
    In this research the application of Automatic Speech Recognition, Natural Language Understanding, Neural Networks and Text To Speech Conversion is investigated to create a domain specific end to end voice based E-Channeling system. The novel idea in this research can be extended to any other domain(e.g.: Taxi Application) and build a conversational intelligence system. This system enables the user to avoid the shortcomings in the traditional doctor appointment channeling procedures. The system also have the ability to predict the doctor specialization according to the symptoms of the patient and can give emergency health tips by using the powerful Neural Network module. Domain-specific speech recognition model is created according to Sri Lankan accents and handles the context-specific to this domain(94% accuracy). Extracting the entities, handling e-channeling functions and selecting the most suitable API is done by the RASA backend. Neural Network will give the first aid and doctor specialization recommendations according to user input with a validation accuracy of 90%. Speech synthesis model will output the response in user preferred language(Sinhala, English or Tamil).
  • Thumbnail Image
    PublicationEmbargo
    Stock Market Prediction Using Machine Learning Techniques
    (IEEE, 2019-12-05) Sirimevan, N; Mamalgaha, I. G. U. H; Jayasekara, C; Mayuran, Y. S; Jayawardena, C
    Predicting stock market prices is crucial subject at the present economy. Hence, the tendency of researchers towards new opportunities to predict the stock market has been increased. Researchers have found that, historical stock data and Search Engine Queries, social mood from user generated content in sources like Twitter, Web News has a predictive relationship to the future stock prices. Lack of information such as social mood was there in past studies and in this research, we discuss an effective method to analyze multiple information sources to fill the information gap and predict an accurate future value. For this, LSTM - RNN models were employed to analyze sperate sources and Ensembled method with Weighted Average and Differential Evolution technique were used for more accurate prediction of the stock prices. And highly accurate predictions were made to one-day, seven-days, 15-days and 30 days for the future. So that investors could gain an insight into what they are inventing for and the companies to track how well they will perform in the stock market.