KICACT 2017

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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.
Artificial Neural Network based Emotions Recognition System for Tamil Speech.
(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Paranthaman, D.; Thirukumaran, S.
Emotion has become the important part in communication between human and machine, so the detection of emotions has become important part in pattern recognition through Artificial Neural Network (ANN). Human's emotions can be detected based on the physiological measurements, facial expressions and speech. Since human shows different expressions for a particular emotion when they are speaking therefore the emotions can be quantified. The English speech dataset is provided with descriptions of each emotional context available in Emotional Prosody Speech and Transcripts in the Linguistic Data Consortium (LDC). The main objective of this project describes the ANN based approach for Tamil speech emotions recognition by analyzing four basic emotions sad, angry, happy and neutral using the mid-term features. Tamil speeches are recorded with four emotions by males and females using the software “Cubase”. The time duration is set to three seconds with the sampling frequency of 44.1 kHz as it is the logical and default choice for most digital audio material. For the simulations, these recorded speech samples are categorized into different datasets and 40 samples are included in each dataset. Preprocessing includes sampling, normalization and segmentation and is performed on the speech signals. In the sampling process the analog signals are converted into digital signals then each speech sentence is normalized to ensure that all the sentences are in the same volume range. Next, the signals are separated into frames in the segmentation process. Then, the mid-term features such as speech rate, energy, pitch and Mel Frequency Cepstral Coefficients (MFCC) are extracted from the speech signals. Mean and Variance values are calculated from the extracted features. To create the classifier for the emotions, the above statistical results as an input matrix with their related emotions-target matrix are fed to train, validate and test. The neural network back propagation algorithm is executed by the classifier to recognize completely new samples of Tamil speech datasets. Each of the datasets consists of different combinations of speech sentences with different emotions. Then, the new speech samples are assigned to identify the recognition rate of the speech emotions using the confusion matrix. In conclusion, the selected mid-term features of Tamil speech signals classify the four emotions with the overall accuracy of 83.45%. Thus, the mid-term features selected are proven to be the good representations of emotions for Tamil speech signals and correctly recognize the Tamil speech emotions using ANN. The input gathered by a group of experienced drama artists who have the voice with the good emotional feelings would help to increase the accuracy of the dataset.

KICACT 2017

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