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Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step...
We propose a neural-network training algorithm that is robust to data imbalance in classification. In our proposed algorithm, weights are introduced to training examples, effectively modifying the trajectory traversed in the parameter space during the learning process. Furthermore, the proposed algorithm would reduce to the normal stochastic gradient decent learning if the data is balanced. On the...
Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for...
Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-raised-cosine shaped pulses are simulated in extreme noisy conditions having joint impurities...
In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as...
Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’...
We present a novel approach to automated estimation of agreement intensity levels from facial images. To this end, we employ the MAHNOB Mimicry database of subjects recorded during dyadic interactions, where the facial images are annotated in terms of agreement intensity levels using the Likert scale (strong disagreement, disagreement, neutral, agreement and strong agreement). Dynamic modelling of...
Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive auto-scaling systems. In this study, predictive...
Design of a neural network classifier involves selection of input features and a network structure from a very large search space, preferably respecting the problem's constraints. Most published methods just focus on the feature selection aspect and do not consider any approach for determining a model structure that best fits the application at their hand. Moreover, the design criteria usually include...
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted...
This paper presents a technique for real time human hand gesture recognition system for automatic tap operation. Hand gesture recognition based machines have been widely developed in recent years. Most of the techniques use skin color models due to its robustness and simplicity. In this work there are two major steps for achieving this goal: firstly, the detection of skin color and secondly, the classification...
This work describes results of noise filtering techniques based on a Non-Linear Principal Component Analysis (NLPCA), applied to Hyperspectral CASI remote sensed data. NLPCA was carried out using Artificial Neural Networks (ANN) which are capable of modeling data non-linearity. NLPCA operated feature extraction on which original data were reconstructed, filtering the noise which affects first VNIR...
This paper proposes a new model for predicting the optimal warfarin dosing for African American patients. The prediction model is created using the multivariable regression method. The accuracy of dosing prediction is directly related to patient's safety. We show that the proposed model has better accuracy compare to all other available prediction methods for optimal dosing of warfarin.
In this paper a comparison between two single classifier methods (support vector machine, artificial neural network) and two ensemble methods (bagging, and boosting) is applied to a real-world mining problem. The four methods are used to classify, thus monitoring underground dam levels and underground pumps energy consumption on a double-pump station deep gold in South Africa. In terms of misclassification...
This paper regards the exploitation of RSS in localization techniques within UWB networks. Both fingerprinting and model based approaches are studied and evaluated using a real UWB measurement campaign. As for fingerprinting approach, SVM, KNN, and ANN techniques are proposed and compared. As for model based approach, refined RSS models are proposed in order to better characterize the RSSI-distance...
The volume of electronic transactions has raised a lot in last years, mainly due to the popularization of ecommerce, such as online retailers. We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore it is important and necessary to developed and apply techniques that can assist in fraud detection, which motivates our...
The flooding velocity is an important but difficult to accurately predict parameter for the packed column design. With the appearance of new packing shapes, traditional empirical models are insufficient to satisfy the requirement of engineering applications. In this paper, a novel approach using least squares-support vector machine (LS-SVM) is proposed to predict the flooding velocity in the randomly...
This paper presents a system for detecting breast cancer based on moments. Instead of trying to improve the applied classifier we focused on improving the input attributes. We extracted new features from database samples using the first four moments namely, mean, variance, skewness and kurtosis. Through simulations, 10-fold cross validation method was applied to the Wisconsin breast cancer database...
Total losses in transmission and distribution (T&D) of electrical energy including nontechnical losses (NTL) are huge and are affecting the good interest of utility company and its customers. In this context, importance of customer load profile evaluation for detection of illegal consumers is explained in this paper. Classification of the customers based on load profile evaluation using SVMLIB...
Neural networks are a useful alternative to Gaussian mixture models for acoustic modeling; however, training multilayer networks involves a difficult, nonconvex optimization that requires some “art” to make work well in practice. In this paper we investigate the use of arccosine kernels for speech recognition, using these kernels in a hybrid support vector machine/hidden Markov model recognition system...
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