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Application Of Support Vector Machine In Predicting The Market's Monthly Trend Direction, Ali Alali
Application Of Support Vector Machine In Predicting The Market's Monthly Trend Direction, Ali Alali
Dissertations and Theses
In this work, we investigate different techniques to predict the monthly trend direction of the S&P 500 market index. The techniques use a machine learning classifier with technical and macroeconomic indicators as input features. The Support Vector Machine (SVM) classifier was explored in-depth in order to optimize the performance using four different kernels; Linear, Radial Basis Function (RBF), Polynomial, and Quadratic. A result found was the performance of the classifier can be optimized by reducing the number of macroeconomic features needed by 30% using Sequential Feature Selection. Further performance enhancement was achieved by optimizing the RBF kernel and SVM parameters …
Exploiting Contextual Information For Prosodic Event Detection Using Auto-Context, Junhong Zhao, Wei-Qiang Zhang, Hua Yang, Michael T. Johnson, Jia Liu, Shanhong Xia
Exploiting Contextual Information For Prosodic Event Detection Using Auto-Context, Junhong Zhao, Wei-Qiang Zhang, Hua Yang, Michael T. Johnson, Jia Liu, Shanhong Xia
Electrical and Computer Engineering Faculty Research and Publications
Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information …
A Survey Of Systems For Predicting Stock Market Movements, Combining Market Indicators And Machine Learning Classifiers, Jeffrey Allan Caley
A Survey Of Systems For Predicting Stock Market Movements, Combining Market Indicators And Machine Learning Classifiers, Jeffrey Allan Caley
Dissertations and Theses
In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification …
Hybrid Spr Algorithm To Select Predictive Genes For Effectual Cancer Classification, Aruna Sundaram, Nandakishore Lellapalli Venkata, Rajagopalan Sarukai Parthasarathy
Hybrid Spr Algorithm To Select Predictive Genes For Effectual Cancer Classification, Aruna Sundaram, Nandakishore Lellapalli Venkata, Rajagopalan Sarukai Parthasarathy
Turkish Journal of Electrical Engineering and Computer Sciences
Designing an automated system for classifying DNA microarray data is an extremely challenging problem because of its high dimension and low amount of sample data. In this paper, a hybrid statistical pattern recognition algorithm is proposed to reduce the dimensionality and select the predictive genes for the classification of cancer. Colon cancer gene expression profiles having 62 samples of 2000 genes were used for the experiment. A gene subset of 6 highly informative genes was selected by the algorithm, which provided a classification accuracy of 93.5%.
A Combined Protective Scheme For Fault Classification And Identification Of Faulty Section In Series Compensated Transmission Lines, Resul Çöteli̇
A Combined Protective Scheme For Fault Classification And Identification Of Faulty Section In Series Compensated Transmission Lines, Resul Çöteli̇
Turkish Journal of Electrical Engineering and Computer Sciences
The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained …
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Turkish Journal of Electrical Engineering and Computer Sciences
A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, statistical textural features are extracted from the ROIs and they are classified using support vector machines. To test the performance of the method, 57 ROIs selected from the Mammographic Image Analysis …
A Video-Based Eye Pupil Detection System For Diagnosing Bipolar Disorder, Gökay Akinci, Edi̇z Polat, Orhan Murat Koçak
A Video-Based Eye Pupil Detection System For Diagnosing Bipolar Disorder, Gökay Akinci, Edi̇z Polat, Orhan Murat Koçak
Turkish Journal of Electrical Engineering and Computer Sciences
Eye pupil detection systems have become increasingly popular in image processing and computer vision applications in medical systems. In this study, a video-based eye pupil detection system is developed for diagnosing bipolar disorder. Bipolar disorder is a condition in which people experience changes in cognitive processes and abilities, including reduced attentional and executive capabilities and impaired memory. In order to detect these abnormal behaviors, a number of neuropsychological tests are also designed to measure attentional and executive abilities. The system acquires the position and radius information of eye pupils in video sequences using an active contour snake model with an …