KICACT 2017
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/17369
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Item V-Synch: Rendering Distance a No-issue with the New Feature of Video Synchronization in Existing Multimedia Platforms.(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Tiwari, R.; Shakya, S.Social media are computer mediated technologies that allow creating and sharing of information idea, career interests and other forms of expression via communities and networks. They introduce substantial and pervasive changes to communication between businesses, organizations, communities and individuals. Various features are being introduced in this field with the objective to make it more attractive to users. “V-Synch” is aimed at introducing features like video and sketch pad synchronization to develop a full- fledged app that also has the current popular features like internet call and chat. We intend to make an android application in which users can always stay connected through multiple platform synchronization (watch the video and use sketch pad in synchronized way in real time) although they are distance apart. All the devices connected to the group can take control of video playback. When any user of that group starts, pauses, or performs specific action on a video, the state of that video is synchronized to all other connected devices in real time. The elements drawn on sketch pad are also shown live in real time to everyone connected to the group. NTP algorithm is used to synchronize all participating devices to within a few milliseconds of Coordinated Universal Time (UTC). The synchronization is correct when both the incoming and outgoing routes between the client and the server have symmetrical nominal delay. V-Synch could be very much beneficial to students for group study, long distance friends to hang out together and Serve a great deal in case of tele-education.Item Stock Market Analysis and Prediction.(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Shakya, A.; Pokhrel, A.; Bhattarai, A.; Sitikhu, P.; Shakya, S.Stock price and stock index price forecasting system, used by investors and financial managers to describe the market and compare the return on specific investments, has been a topic of research for very long now. When in the stock market, there are more buyers than there are sellers, the price must adapt or no trades are made. This tends to drive the price upwards, increasing the market quotation at which investors can sell their shares, enticing investors who had previously not been interested in selling and vice versa. These demands and supplies are ever changing, resulting in highly-fluctuating, non-linear stock prices which poses a threat against the credibility of those prediction systems which only view the market from one perspective. For a reliable system, it is therefore important to explore the market on multiple grounds, basically through Technical, Fundamental and News Analysis. Under Technical Analysis, SMA (Simple Moving Average) is used as a preliminary data smoothing technique, which helps reduce the fluctuations substantially. Artificial Neural Networks (ANNs) is then employed to analyze the nonlinear relationships between the stock closed price and various technical indexes, and to capture the knowledge of trading signals that are hidden in historical data. Features like traded share, traded volume, opening price, closing price, high price and low price are fed as an input parameter in Neural Network. Backpropagation algorithm is then implemented to train the given Network model. The neural network layers and neuron numbers in hidden layers are then tuned by training and validating the data set iteratively. The fundamental analysis involves thorough study of financial statements of companies, also known as quantitative analysis. This involves looking at assets, liabilities, revenue, expenses and all other financial aspects of a company. It gives insight on the company's future performance. The results moreover reflect the company's success or failure over the long term than immediate future. Hence, unlike technical analysis, it helps predicting stock price on a long run. In news analysis, we focus on understanding the news sentiment and its affects which may cause the investors to either buy or sell the shares based on positivity or negativity of the news. The news analysis problem can be mapped into similarity based classification. A set of vectors are created from analysis of historical news, where each component of a vector represents the features in data set. The required labeling are done based on historical rise/fall of stock prices. The increase or decrease of the trend is then predicted based on similarity measures. In short, news analysis predicts the price of share of the following day by comparing the most recent news with past news using Knearest neighbor algorithm. Thus, through the circumstantial application of the above-mentioned analysis, the paper proposes to predict the stock market in a more generalized manner.Item Foreign Exchange Rate Prediction using Artificial Neural Network and Sentiment Analysis(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Shrestha, S.; Baral, S.; Subedi, S.; Ranjit, S.; Shakya, S.Foreign currency exchange plays an important role for currency trading in the financial market. Modern approach to the foreign currency exchange market requires support from the computer algorithms to manage huge volume of transactions. There occurs problems like trading without a plan, failing to adapt to the market, having unrealistic expectation and many more. Due to these problems, predictions are to be done. This paper investigates on prediction of foreign exchange market using neural network and sentiment analysis. There are many algorithms for performing prediction but different algorithms have different accuracy. One of the best method with high accuracy is given by Artificial Neural Networks (ANN). Neural network parameters include number of hidden layers, number of neurons, use of bias neurons, activation functions and training methods. Input nodes are price of gold, crude oil, Nasdaq index, yesterday’s price. Our model contains 4 input node, 1 hidden layer and 7 hidden nodes. At first, pre-processing is done and inputs are fed to the neural network. By using backpropagation algorithm, training is done and then testing is performed. Mean absolute percentage error is found to be 0.39%. The price movement is also directly related to market sentiment. We aim to employ a statistical technique to the opinion of different traders and finding the overall sentiment. Sentiments are taken from tweets and then filtering the tweets are performed. After that, features are extracted and by using Naïve Bayes algorithm, the results are classified as positive or negative.Item Automated Characters Recognition and Family Relationship Extraction.(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Bajracharya, A.; Shrestha, S.; Upadhyaya, S.; Shrawan, B.K.; Shakya, S.“Automated characters recognition and family relationship extraction” is an application of Natural Language Processing to identify characters from the story and determine the family relationship among them. This application is the use of specialized computer programs to identify entities, classify them and extract characters from them and determine relationship between them. This paper follows basic steps of NLP i.e. Tokenization, POS tagging, sentence parsing followed by the pronoun resolution implementing various algorithms and finally extracting entities and relations among them. Heretofore, we have successfully resolved pronoun from simple sentences by resolving Noun Phrase using the recursive algorithm for tree generation and hence extracting relation between the Noun Phrase (NP). Basic approach towards this project is to do Tokenization and POS tagging first. Then, sentence which is recursive composition of Noun phrase, verb phrase and prepositional phrase is parsed and recursive tree is generated. Then tree is traversed to determine the noun phrase which is replaced by the entity object of that particular noun phrase. Pronoun resolution is the essence of NLP and is of different type. Here, Co reference resolution has been used. After resolving the entire pronoun, then finally relationship is extracted from the story by comparing the relation ID of each Entity. Given the simple story, entities are being extracted and relationship is also determined. Understanding the approach of NLP and implementing them to showcase its use is the main theme of this project which is being done with as accurate result as possible. This paper can act as a base.