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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Subject: search.filters.subject.Neural Network

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Now showing 1 - 5 of 5
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    PublicationOpen Access
    Cryptocurrency Price Prediction: A Comparative Study using LSTM, GRU and Stacking Ensemble Algorithm for Time Series Forecasting
    (SLIIT, 2022-02-11) Ashikul Islam, M. D
    Technology has significantly reshaped how humans interact with their tangible and intangible surroundings. Cryptocurrency is considered to be one of the most recent technological inventions which revolutionized how we perceive currencies and their functionality. It has become popular because of its safety, security and anonymity. However, volatility remains one of the major issues with cryptocurrencies to this day. Therefore, the primary aim of this paper is to develop LSTM (Long ShortTerm Memory), GRU (Gated Recurrent Units) and a Stacking Ensemble Learning algorithm that efficiently predicts the price of a cryptocurrency for a given period of time. The predictions are then observed and analysed to determine the comparative performance of the said algorithms.
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    PublicationOpen Access
    Special Event Item Prediction System for Retails – Using Neural Network Approach.
    (SLIIT, 2022-02-11) Alwis, T; Pemarathna, P
    Selling and buying is the general process marketing field follows. Nowadays marketing field bonded with the modern technology, and it highly effected to field expandability. Marketing become fruitful when it achieves its key points which are called sales and profit. Mostly people are move to the retails because all the essentials and other things can buy from one place. There are many technological concepts involve with marketing field as an enhancement. Prediction processes, data analysis, item designing and profit calculation are some representatives for those concepts. This study is a prediction process, developed for retails using machine learning approaches. Item sales data analyzed and generated prediction results on set of items which are given maximum or expected profit margins and which items satisfy the customer most. Item suppliers are key stakeholder type a retail can have, there is a recommender system in this approach for suppliers and the recommendation is based on past sales data. There are certain types of machine learning approaches used in sales item prediction, sales item feature prediction, sales price prediction and etc. Novelty of this research is, it focused only special event items such as items in Christmas season, items specialized for Mother’s Day, Valentine Day, Sinhala, and Tamil new year and etc. This research process had completely followed the machine learning neural network concept. Recurrent Neural Network is subpart of neural networks and this research study followed up through this RNN method. Neural network had applied using a form of machine learning called deep learning. This model had worked on sequential data therefor LSTM (Long Short-Term Memory) layers were used and to avoid overfitting issue several dropout layers were used. The results prove neural network method has highest accuracy.
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    PublicationEmbargo
    Traffic Density Estimation and Traffic Control using Convolutional Neural Network
    (IEEE, 2019-12-05) Ikiriwatte, A. K; Perera, D. D. R; Samarakoon, S. M. M. C; Dissanayake, D. M. W. C. B; Rupasignhe, P. L
    The existing traffic light control systems are inefficient due to the usage of predefined algorithms on offline data. This causes in numerous problems such as long delays and a wastage of energy. Estimation of traffic density indirectly affects in decreasing the high traffic congestion which will occur due to the less planning of transportation infrastructure and the policies. The goal of this research is to introduce an applicable method to improve the existing static traffic signal system into a dynamic system. As an approach we analyze the use of machine learning algorithms to measure the traffic density to tackle this research problem of high traffic congestion. The main target is to implement this system for the four-way junctions since it is a place where the possibility of having a traffic congestion seems to be high. With use of these traffic density estimation algorithm, crowd density estimation and signal handling we conduct experiments on minimizing the congestion at four-way junctions. We decided on using convolutional neural networks as an advanced machine learning method to increase the accuracy of the learning algorithm.
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    PublicationEmbargo
    Ayurvedic Knowledge Sharing Platform with Sinhala Virtual Assistant
    (2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Jayalath, A.D.A.D.S.; Nadeeshan, P.V.D.; Amarawansh, T.G.A.G.D.; Jayasuriya, H.P.; Nawinna, D. P.
    Apart from western medicine methods Ayurveda medicinal system is a very huge and better resulting medicinal technique. In these Ayurveda methods identification of indigenous plants to predict the medicines is very important and must do very carefully. Generally main components that we use to identify a plant are leaf, flower, trunk and root etc. Among these features, we use images of leaves and flowers. To do this we are using deep learning based CNN approaches and machine learning and technologies. Those are OpenCV, and Tensorflow classification algorithm. According to the evidences that we gathered from surveys and interviews that we conducted with the responsible parties we could find out that lots of people don’t have much knowledge about indigenous medicinal plants and their Ayurveda treatment methods. To overcome this problem we implemented Ayurveda information centralized chatbot which is able to answer user’s questions relevant to the Ayurveda and indigenous medicinal plants. Chatbot will analyze the question that user asks and will provide answers according to that. Another useful feature of this system is it provides relevant information of Ayurveda doctors. So users can find doctors according to their needs and they are able to rate and give recommendations for the doctors. That will be help others to find doctors more easily and efficiently without any doubt.
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    PublicationEmbargo
    A Geophone Based Surveillance System Using Neural Networks and IoT
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Supun Hettigoda, Chamath Jayaminda; Amarathunga, U.; Wijesundara, M.; Wijekoon, J.; Thaha, S.
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.