Browsing by Author "Rathnayaka, RMKT"

Now showing 1 - 5 of 5

item: Conference-Full-text
Arima and ann approach for forecasting daily stock price fluctuations of industries in Colombo stock exchange, Sri Lanka
(Faculty of Information Technology, University of Moratuwa., 2020-12) Wijesinghe, GWRI; Rathnayaka, RMKT; Karunananda, AS; Talagala, PD
Time series forecasting is regarded as the most successful criterion among several factors involved in the decision-making process to pick a correct prediction model. Improving predictability has become crucial for decision-makers and managers, especially time series forecasts, in various fields of science. Using K-mean clustering and Principle Component Analysis, the dataset is clustered based upon a central point selection and the Euclidian distance measurement. The results define the main contribution sector for CSE, and the business in the selected sector in the 2008-2017 period in accordance with the clustering results. In particular, ARIMA has demonstrated its performance in predicting the next lags in precision and accuracy. With regard to Colombo Stock Exchange (CSE), there are very few studies in the literature that have focused on new approaches to forecasts of high volatility stock price indexes. Different statistical methods and economic data techniques have been widely applied in the last decade in order to classify CSE's stock price, patterns and trade volumes. This article looks at the best sector and organization to invest in and discusses whether and how the deep-learning algorithms for time series data projection, such as the Back Propagation Neural Network, are better than traditional algorithms. The results show that Deep learning algorithms like BPNN outperform traditionally based algorithms like the model ARIMA. For ARIMA and ANN, MAPE values are 0.472206 and 0.1783333 respectively. MAE values are 29.6975 and 4.708423 respectively results for ARIMA and ANN. The MAE and MAPE values relative to ARIMA and BPNN, which suggests BPNN `s superiority to ARIMA.
item: Conference-Full-text
Artificial neural network to estimate the paddy yield prediction using remote sensing, weather and non weather variable in Ampara district, Sri Lanka
(Faculty of Information Technology, University of Moratuwa., 2020-12) Wanninayaka, WMRK; Rathnayaka, RMKT; Udayakumara, EPN; Karunananda, AS; Talagala, PD
In Sri Lanka, seasonal paddy area mapping and rice prediction is based on the traditional methods with poor technologies. Ampara district has been chosen as the study area because its contribution is considered as the second highest paddy yield to the Sri Lankan rice harvest. This study focuses on developing models for precise mapping paddy and predicting the harvest of rice in the Ampara district. It helps the government and persons of authority to take decisions about how to manage the economy based on the rice quantity. Research includes the imageries of satellites sentinel-1 and sentinel-2 the period from April to September 2019. The two classification methods, Divisional Secretory Division (DSD) and maximum likelihood classification were used to identify the real paddy area. The accuracy rates of these classifications were 0.92 and 0.86 respectively. Artificial Neural Network (ANN) model was used to predict paddy rice harvest using sentinel 2 features extracts and round truth data. Mean square error of the model is 0.106 and mean absolute error is 0.245. Increasing the remote sensing imagery directly affects to enhance accuracy. Increasing the number of sample classes and number of classes in various types will raise-up higher accuracy than in here.
item: Conference-Full-text
An elephant detection system to prevent human-elephant conflict and tracking of elephant using deep learning
(Faculty of Information Technology, University of Moratuwa., 2020-12) Premarathna, KSP; Rathnayaka, RMKT; Charles, J; Karunananda, AS; Talagala, PD
Human settlement is spreading to forest boundary areas because of the population growth, it triggers disputes between elephants and humans, leading to the loss of property and life. Continuous monitoring and tracking of elephants are difficult due to their large size and movement. Therefore, large-scale for real-time detection and alert of elephant intrusion into human settlements, monitoring is needed. Many methods had been implemented for the elephant’s intrusion detection and warning systems. Wildlife conservation and the management of human-elephant conflict require a cost-effective method of monitoring elephant behavior. In this paper, a method for the identification of the elephant as an object using image processing is proposed. The major aim of the study is to minimize the human-elephant conflict in the forest border areas and the conservation of elephants from human activities as well as protect human lives from elephant attacks. We used a data set containing elephants and we developed an approach to distinguish elephants and other animals. We used the Convolutional Neural Network and achieved a maximum accuracy of 94 percent. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants.
item: Conference-Full-text
The public sentiment analysis within big data distributed system for stock market prediction– a case study on colombo stock exchange
(Faculty of Information Technology, University of Moratuwa., 2020-12) Malawana, MADHP; Rathnayaka, RMKT; Karunananda, AS; Talagala, PD
Stock price prediction plays an important role on the journey of investors on the stock market. The prices of the company stocks on the market are performed by different deliverables. Social media data sets, news sites, feedback and reviews are some kind of online tools that can affect the stock market. It is often worth using this context to predict the performance of market shares. We take the advantage of Sentiment analysis on Market related announcement and respective public opinions for stock market trend predictions for more accurate recommendations. Sentiment Analysis is a machine learning program for extracting opinions from a text section that is designed to support any product, company, individual or other entity (positive, negatively, neutral). In this research calculations and data processing were performed within machine learning approach with use of Spark model on Google cloud platform. Among most of the stock prediction researches, only few researchers have done their researches on sentiment analysis within big data distributed environment. Logistic Regression and Naïve Bayes perform well in sentiment classification. Main finding of this research is that public opinion significantly influences the fluctuations of market forces and economic factors such as monetarism, government reforms, unforeseen pandemics, interest rates, public trust, and faith in bond market trust. The detection of the feelings pattern will enhance the market prediction as it ensures the consistency of decision.
item: Conference-Full-text
A real-time density-based traffic signal control system
(Faculty of Information Technology, University of Moratuwa., 2020-12) Chandrasekara, WACJK; Rathnayaka, RMKT; Chathuranga, LLG; Karunananda, AS; Talagala, PD
Traffic congestion and accidents have become two major issues in Sri Lanka today. These issues cause to create many social, economic and environmental problems. Lack of effective Traffic Light Control System is one of the reasons for it happen. This research proposed an approach to develop an effective real-time density-based traffic light control system. This research consists of two major parts; Image processing model for capture real-time data and ANN model for predict the results considering real-time data. Identify the best features from gathered data and minimize dimensionality between the features, by principal component analysis (PCA) to train a Neural Network model. Using cameras, lanes are monitored and capture image of its. Detection and counting of number of vehicles in each lane and length of queue is done by using image processing. The data from each lane is sent to the ANN unit. According to the count of vehicles, the trained model will be decided the lane and time limit that will need to allow green phase. The NN model has achieved 0.9274 accuracy in the training phase. Thus, the traffic lights at the intersections will have changed isolated and dynamically according to the conditions of real-time traffic when using this traffic light control system than the existing fixed time traffic light control system or traditional computation algorithms. This system reduces the average waiting time and increases the efficiency of traffic clearance. New adaptive traffic management also reduces the pollution due CO2 emission and also social and economic problems.