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PHM (Prognostic and Health Management) is a new concept, which is to ensure the normal operation of the complicated system, to achieve its functionality and reliability better. In this situation, the remaining useful life (RUL) prediction has aroused more and more concerns. Although the life prediction methods are many, there are not a set of systemic of evaluation parameters to evaluate the accuracy...
MARA Junior Science College (MRSM) Lenggong is one of the educational institutes under Majlis Amanah Rakyat (MARA). Based on the current academic performance and selected criteria of 6A's in the Penilaian Menengah Rendah (PMR, now it is known as PT3), rationally there should be no reason for the failure to achieve excellent results in the Sijil Pelajaran Malaysia (SPM). However, every time the results...
Solar energy prediction is a key to the power management in the electronic embedded system that operates using the harvested solar energy. This paper proposes accuracy improvement approaches for the solar energy prediction based on artificial neural networks, in order to increase the robustness of solar-energy-powered systems. Two complementary neural network models, multilayer perceptron (MLP) network...
Oil is the lifeblood of the global economy. Recently, oil prices have witnessed fluctuations and the prediction of oil prices has become a challenge for researchers. The aim of this research is to design a model that is able to predict the prices of crude oil with good accuracy. We used the daily data from 1999 to 2012 with 14 input factors to predict the price of West Texas Intermediate (WTI), which...
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi-class problem. We describe the label-power set-based...
Research in student retention is traditionally survey-based, where researchers use questionnaires to collect student data to analyse and to develop student predictive model. The major issues with survey-based study are the potentially low response rates, time consuming and costly. Nevertheless, a large number of datasets that could inform the questions that students are explicitly asked in surveys...
Neural network is easy to fall into the minimum and over-fitting in the application. The paper proposes a novel dynamic weight neural network ensemble model (DW-NNE). The Bagging algorithm generates certain neural network individuals which then are selected by the k-means clustering algorithm. In addition, for the integrated output problems, the paper proposes a dynamic weight model which is based...
Effort estimation is important part of software project management. Based on applied strategy these models can be classified into groups of algorithmic and non-algorithmic models. In this study we present the model for expert effort estimation developed using data mining techniques - a multilayer perceptron (MLP) artificial neural network. The data set used in the study contains 785 records collected...
Finding patterns of interaction and predicting the future structure of networks has many important applications, such as recommendation systems and customer targeting. Community structure of social networks may undergo different temporal events and transitions. In this paper, we propose a framework to predict the occurrence of different events and transition for communities in dynamic social networks...
In the cognitive radio system, spectrum prediction attracts more and more attention, which predicts future spectrum holes to save energy of spectrum sensing and to improve the efficiency of spectrum access. The current research on spectrum prediction is similar to the hard decision in the communication system. However, the hard decision loses amount of channel information during the process of obtaining...
Patient length of stay is the most commonly employed outcome measure for hospital resource consumption and to monitor the performance of the hospital. Predicting the patient's length of stay in a hospital is an important aspect for effective planning at various levels. It helps in efficient utilization of resources and facilities. So, there exist a strong demand to make accurate and robust models...
The randomness of the wind velocity causes the fluctuation of wind power. Therefore, It is necessary to forecast the wind power in a certain time. In this paper, the ultra-short term predication of wind power has been carried out based on the Auto-Regressive-and-Moving-Average (ARMA) model. The wind power was predicted by prediction steps of ARMA in section II. According to the corresponding national...
This paper presents a hybrid intelligent approach to forecast short-term output power of a PV system. The proposed hybrid method is composed of a data filtering technique based on wavelet transform (WT) and generalized regression neural network (GRNN). In order to validate the prediction capability of the proposed WT+GRNN model, test results are compared with other soft computing models (SCMs). This...
The androgynous peripheral driving mechanism is a flexible series multi-body system, of which assembly precision is primarily dependent on worker's experience. In order to improve the assembly efficiency and reduce the assembly cost, a system used to predict the synchronous accuracy for guide assembly is developed based on Matlab-GUI. This system consists of three modules, i.e., data management module,...
Due to high competition in today's business and the need for satisfactory communication with customers, companies understand the inevitable necessity to focus not only on preventing customer churn but also on predicting their needs and providing the best services for them. The purpose of this article is to predict future services needed by wireless users, with data mining techniques. For this purpose,...
Most of the modern hard disk drives support Self-Monitoring, Analysis and Reporting Technology (SMART), which can monitor internal attributes of individual drives and predict impending drive failures by a thresholding method. As the prediction performance of the thresholding algorithm is disappointing, some researchers explored various statistical and machine learning methods for predicting drive...
There is much discussion on the performance and selection of exchange rate regimes, and it is still in controversy. For the failure in the works, we collect more data including recent years and removing the periods of the currency crisis, then analyze the matching relationship between the exchange rate regimes and the condition of countries in the exhaustive CHAID decision tree, and build the exchange...
In this paper we propose high quality prosody models for enhancing the quality of text-to-speech (TTS) synthesis for providing better human computer interaction. In this study prosody refers to duration and intonation patterns of the sequence of syllables. In this work, prosody models are developed using feedforward neural networks, and prosodic information is predicted from linguistic and production...
For rice blast gray system with complex nonlinearity, utilizing of gray ant colony model and RBF neural network model characteristics, gray ant colony and RBF neural network combination model is presented in this paper. after 10 years (2002-2011) prediction analysis of rice blast, the prediction accuracy of this project is up to 97.84%, and verifies the validity of the prediction model.
Corporation financial distress has been an important issue for study in the financial fields. This paper uses traditional BP neural network model and proposes PNN model to predicate financial distress. The sample consists of 276 companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange over the period 2001–2010. Factor analysis is used to lower correlation and reduce dimensionality...
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