2.8. Multiscale Principal Component Analysis Multiscale PCA (MSPCA) combines the capability of PCA to extract the cross-correlation between the variables and wavelets to divide deterministic features from stochastic processes and approximately de-correlate the autocorrelation among the measurements. Figure 2.3 illustrates the MSPCA procedures. Figure 2.3. shows the MSPCA procedures. For combining the profit of PCA and wavelets, the capacity for each variable are decomposed to its wavelet coefficients
Neural Networks in Investments I. ABSTRACT Investment managers often find themselves overwhelmed with the large amount of data obtained from the financial markets. Most of the data available is numeric and noisy in nature, making the decision-making process harder. These decisions usually rely on the integration of statistical measures that attempt to compress much of the data and qualitative depictions such as graphs and bar charts with news events and other pertinent information. Investment
. middle of paper ... ... they address computationally difficult issues. However, based on my research of sensory evaluation models that are likely to solve the given problem, I found one that works well. This model is known as the Multilayer Perceptron (MLP) currently selected by Coors. However, I would also recommend a sub-model called the Multiple Input Multiple Output (MIMO). This sub-model is a specific alternate of the Back-propagation design. Multiple Input, Multiple Output (MIMO) model
Introduction Most of telecommunication companies consider the customer as the most important asset for them. For that reason, nowadays, a challenging problem that encounters telecommunication companies is when the customer leaves the company to another service provider for a reason or another [1]. In most cases, this churn can happen in rates which seriously affect the profitability of the companies since it is easy for the customers to switch companies. In market, where the competition between the
The confusion matrix is important because it tells us our True Positive, True Negative, False Positive, False Negative values. Our True Positive (TP) value is found in the upper left corner of the matrix and is 99. Our True Negative (TN) value is located the bottom right corner of the matrix and its value is 181. Our False Positive (FP), bottom left, and False Negative (FN), top right, are 73 and 89 respectively. False Positive and False Negative represent classification errors while the True Positive
algorithms Multilayer Perceptron and Support Vector Machine. We estimated the performance of these classification techniques in terms of different evaluation measures like Accuracy, Kappa statistic, True-Positive Rate, False-Positive Rate, Precision, Recall, and F-Measure. The proposed technique had an accuracy of 99.4 % with the Statlog data set, 97.7 % with the Covertype data set and 90 % with the 3 data set; and in every aspect, it performed better than Multilayer Perceptron and Support Vector Machine
algorithm.” International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012, pp.109-120 [10] Anshuman Sharmaa and M.R. Aloneb “A Novel Approach for Improve the Detection Rate in Intrusion Detection System Using Multilayer Perceptron Algorihtm” Information Sciences and Computing Volume 2013, Number 1, pp.1-7 [11] http://www.sans.org/security-resources/idfaq/switched3.gif
1. Introduction Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning
classification algorithms Multilayer Perceptron and Support Vector Machine. We evaluated the performance of these classification methods in terms of different measures like Accuracy, Kappa statistic, True-Positive Rate, False-Positive Rate, Precision, Recall, and F-Measure. The proposed method had an accuracy of 99.4 % with the WBC data set, 97.7 % with the WDBC data set, and 84.4 % with the MM data set; and in every aspect, it performed better than Multilayer Perceptron and Support Vector Machine based
equations. On top of mathematics and physics related classes, I took two classes on programming where I programmed in Java working with various data structures (eg. linked lists) - on my own I have learned the Python language and have worked with perceptrons using Python - and I took general and abnormal psychology classes gaining insight into the different perspectives of psychology and learning the symptoms of some disorders. At University at Albany I took Introduction to Biopsychology and Behavioral
As a non-structural measure, flood forecasting (such as discharge, water level, or flow volume) is a crucial part of flow regulation and water resources management. Worldwide, flood disasters account for about one-third of all natural disasters in terms of number and economic losses (Berz 2000). As stated by Dutta and Herath (2004), out of the total number of flood events in the world during the past 30 years, 40% occurred in Asia and Southeast Asia countries stand for the second worst region in
Neural Network Concept in Artificial Intelligence Abstract Since the 1980's there have been renewed research efforts dedicated to neural networks. The present interest is largely due to the difficult problems confronted by artificial intelligence, and due to the deeper understanding of how the brain works, the recent developments in theoretical models, technologies and algorithms. One motivation of neural network research is the desire to build a new breed of powerful computers to solve a variety
2.3 VESSEL SEGMENTATION Retinal vessel segmentation is important for the diagnosis of numerous eye diseases and plays an important role in automatic retinal disease screening systems. Automatic segmentation of retinal vessels and characterization of morphological attributes such as width, length, tortuosity, branching pattern and angle are utilized for the diagnosis of different cardiovascular and ophthalmologic diseases. Manual segmentation of retinal blood vessels is a long and tedious task which
Artificial intelligence has expanded drastically over the last few years. In healthcare it is especially important since it can assist with patient care and treatment. Artificial intelligence is exactly as it sounds; it is a machine, like a computer, that goes beyond those parallels taking a step further by thinking and predicting what its user would do next. In their paper, Advances in artificial intelligence research in health, Khanna, Sattar and Hansen describe artificial intelligence and its
Introduction Artificial intelligence is a branch of science that deals with electronic devices or machines that help in finding solutions of the complex problems in the same pattern as humans do. This usually comprises on features and traits borrowing from human intelligence, and applies them as computer algorithms in a friendly way. An efficient approach can be adopted depending on the appropriate requirements, which affect how artificial intelligent behavior appears. Artificial intelligence in
In this context, I would like to give a brief outline of my master’s research projects, which are I found to be very exciting. The first project was to design a Handwritten Recognition system capable of classifying the digits using the Multilayer Perceptron architecture. Another project was a comparative study of machine learning methodologies such as Bayesian Linear Regression (BLR), Support Vector Machines (SVMs), and Relevance Vector Machines (RVMs), using handwritten character data from postal
Introduction Proactively avoiding vehicle crashes in improving traffic safety on freeways, may have much better benefits than minimizing the values once a crash has occurred. A crash is defined as an accident involving a vehicle collision. To appliance crash prevention, it is essential that the future occurrence of a crash can be predicted on the basis of hazardous traffic flow conditions that are present prior to the existence of the crash. To predict the variation of crash likely over time and
CHAPTER III COLOR DESCRIPTION AND EXTRACTION 3.1 INTRODUCTION Image retrieval is the process of handling large volume of image database in order to achieve the efficiency in identifying similar images over the retrieved results. In Image retrieval, a choice of various techniques is used to represent images for searching, indexing and retrieval with either supervised or unsupervised learning models. The color feature extraction process consists of two parts: grid based representative of color selection