Research Papers - Dept of Software Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1022

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Author: search.filters.author.Kasthurirathna, DSubject: search.filters.subject.Deep Learning

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Now showing 1 - 3 of 3
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    SMART Garbage Bin Kit Expandable and Intelligent Waste Management System using Deep Learning and IoT for Modern Organizations
    (IEEE, 2021-12-02) Hewagamage, P.; Perera, D; Thilakarathna, T; Kasthurirathna, D; Fernando, R; Mihiranga, A
    According to published statistics, Sri Lanka produces garbage around 7000MT per day, and every organization directly contributes this national amount depending on the waste management practices. 'Waste contamination' is a critical issue that affects waste management, and it should be addressed during the garbage collection process. This has led to environmental hazards resulting in health and other social issues. Hence, it is a responsibility of an organization to separate the garbage during the collection process using a suitable technique. In this paper, we are proposing a smart garbage bin kit that automates the separation of garbage collection, which minimizes human error using AI-based technologies. IoT-based devices connected to a smart garbage bin kit guide the user to the correct bin. At the same time, our proposed system can be easily expanded for new special waste categories as well. The other important issue of the current garbage management is improper time management of the garbage removal process in organizations. This happens due to the lack of real-time data on waste bins, and collection is based on the fixed time interval irrespective of the status and location of garbage bins. In the proposed system of SMART Garbage Bin Kit, the group of all interconnected garbage bins is monitored in real-time to identify the optimum collection path considering the location and the status of garbage bins using an optimized algorithm. Hence, the study presented in this paper integrates several intelligent approaches together with IoT based network to build a cutting-edge device, declared as SMART Garbage Bin kit. The prototype system has been built as a part of the research study to demonstrate its feasibility and sustainability.
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    Deep learning based apparel product development system
    (IEEE, 2019-11-22) Kularatne, S. D. M. W; Nelligahawatta, A. N. I; Kasthurirathna, D; Wickramage, S. A
    The apparel industry is one of the biggest, yet growing areas of business in the world. The objective of this research was to implement a solution that reduces the difficulties faced by the apparel industry when producing garment items in an efficient and timely manner. With the use of Generative Adversarial Networks (GANs) and Regional Convolutional Neural Networks (RCNNs), the expectation is to generate brand new, unprecedented garment items using existing garment items and to identify the basic pattern blocks of generated garment images with high accuracy. Through the experimentation and analysis, we were able to generate new garment images by employing the GAN with an acceptable level of accuracy and was able to identify the basic blocks of the garments with high accuracy Instance Segmentation. Hence, this provides a unique solution that combines both fashion designer's and pattern maker's expertise areas at once, which could serve as a perfect platform in optimizing the product development process in the apparel industry.
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    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, C
    Forecasting 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.