ICITR - 2020International Conference on Information Technology Research (ICITR)http://dl.lib.uom.lk/handle/123/163162024-03-28T08:07:49Z2024-03-28T08:07:49ZBuilding social resilience during disasters: an investigation into the role of online social media networksFirdhous, MFMhttp://dl.lib.uom.lk/handle/123/195242023-10-13T02:29:59Z2020-12-01T00:00:00ZBuilding social resilience during disasters: an investigation into the role of online social media networks
Firdhous, MFM
Karunananda, AS; Talagala, PD
Within the last ten years, the world witnessed four serious epidemics. COVID-19 has been the most serious of these ones in terms of the number of people affected and the lives lost. In order to contain the spread of the disease many countries including Sri Lanka enforced 24 hour curfews. The social isolation created by lockdowns creates many problems in people including anxiety and depression. Many studies have been carried out on effect of lockdowns on mental well being of people. But, so far nobody has studied whether online social me can help people overcome the negative effects of lockdowns. This research was carried out to fill this gap. An online survey was carried out to understand how people used social media during the continuous curfew enforced by the Sri Lankan government. The research found that the average time spent using social media has increased compared to normal days. Also, majority of the users agreed that the social media helped them overcome the boredom created by the lockdown. This fact was confirmed using statistical tests in this study.
2020-12-01T00:00:00ZArima and ann approach for forecasting daily stock price fluctuations of industries in Colombo stock exchange, Sri LankaWijesinghe, GWRIRathnayaka, RMKThttp://dl.lib.uom.lk/handle/123/195232023-10-13T02:30:17Z2020-12-01T00:00:00ZArima and ann approach for forecasting daily stock price fluctuations of industries in Colombo stock exchange, Sri Lanka
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.
2020-12-01T00:00:00ZThe public sentiment analysis within big data distributed system for stock market prediction– a case study on colombo stock exchangeMalawana, MADHPRathnayaka, RMKThttp://dl.lib.uom.lk/handle/123/195222023-10-13T02:30:16Z2020-12-01T00:00:00ZThe public sentiment analysis within big data distributed system for stock market prediction– a case study on colombo stock exchange
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.
2020-12-01T00:00:00ZAn automated decision-making framework for precipitation-related workflowsAdikari, AMHBandara, HMNDHerath, SChitraranjan, Chttp://dl.lib.uom.lk/handle/123/195212023-10-13T02:30:16Z2020-12-01T00:00:00ZAn automated decision-making framework for precipitation-related workflows
Adikari, AMH; Bandara, HMND; Herath, S; Chitraranjan, C
Karunananda, AS; Talagala, PD
Due to weather’s chaotic nature, static workflow managers are ineffective in integrating multiple Numerical Weather Models (NWMs) with cascading relationships. Unexpected events like flash floods and breakdown in canal water control systems or reservoirs make decision-making in workflow management further complicated. To enable dynamic decision-making, we need to update part or entire workflow, terminate unfitting NWM executions, and trigger parallel NWM workflows based on recent results from NWMs and observed conditions. Most of the existing weather-related decision support systems cannot trigger or create workflows dynamically. They are also designed for specific geography or functionality, making it challenging to customize for regions with different weather patterns. In this paper, we present an automated decision-making framework for precipitation-related workflows. The proposed framework can manage complex weather-related workflows dynamically in response to varying weather conditions, automatically control and monitor those workflows, and update workflow paths in response to unexpected weather events. Using significant flood-related datasets from the Colombo catchment area, we demonstrate that the proposed framework can achieve 100% accuracy in dynamic workflow generation and path updates compared to manual workflow controlling. Also, we demonstrate that unexpected event identification and pumping station controlling workflow triggers could be improved with advance rule sets.
2020-12-01T00:00:00Z