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ESN load forecasting model has high stability, and is able to learn fast and not easy to fall into local optimum, compared with standard recurrent neural network. In the process of constructing the typical ESN model, the choice of parameters is always empirical or random. The forecasting performance of ESN was analyzed on the basis of its key parameters. While the dynamic reserve pool has black box...
In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset...
Network security has become a very important issue and attracted a lot of study and practice. To detect or prevent network attacks, a network intrusion detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. One of the major advantages of applying machine learning to network intrusion detection is that we don't need expert knowledge...
In this paper we propose five Neural Network models for forecasting public transit. These models are evaluated in terms of accuracy and robustness. The research has two major objectives: to identify the best performing machine learning model in predicting bus travel time and to establish a set of methods in order to obtain a detailed dataset (a variety of practical input values) which will further...
We compare the performance of multilayer perceptrons (MLPs) obtained using back propagation (BP), decision boundary making (DBM) algorithm and extreme learning machine (ELM), and investigate better method for developing aware agents (A-agent) that are suitable for implementation in portable/wearable computing devices (P/WCD). The DBM has been proposed by us for inducing compact and high performance...
In the past decade, there has been a rapid growth in the number of journal, conference and workshop publications from academic research. The growth seems to be accelerated as time goes by. Accordingly, it has become increasingly difficult for researchers to efficiently identify papers related to a given topic, leading to missing important references or even repetitive work. Moreover, even when these...
Work related low back disorders (LBDs) due to manual material handling (MMH) tasks have long been recognized as one of the main occupational disabling injury that affects the quality of life of the industrial working population in the U.S. One of the efforts to comprehend the nature and phenomenon of LBDs due to MMH tasks was undertaken by Marras [18]. Based on multiple experiments they created a...
In this paper, a scheme of adaptable distance calculation based on t-distribution is proposed on the basis of analysis of the scheme of SOM network anomaly detection. This method establishes a confidence interval between the test sample and BMU distance using t-distribution. It makes sure that network anomaly occurs when the distance between the test sample and BMU is not within the range of the confidence...
This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance...
To solve those problems of the low recognition rate, the slow running speed and poor robustness of the existing customers' identification system, an information fusion method based on Extreme Leaning Machine (ELM), Support Vector Machine (SVM) and DS evidence theory was proposed. For customer recognition problems, this information fusion model integrates advantages of ELM, SVM and DS, and can solve...
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