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Spare parts are indispensable resources to ensure equipment the normal operation and continuous production, especially for urban raü vehicles. When the spare parts storage is insufficient, the equipment can't be replaced or repair ed in time, which can cause serious loss. Therefore, it is important to forecast the demand of the urban rail vehicle spare parts. A combination forecasting method based...
Based on the actual traffic detection data of the upstream and downstream video monitor blind area, Support Vector Machines (SVM) algorithm was used to realize the short-term traffic flow forecasting, and VISSIM simulation technology was used build the traffic blind area prediction model. The video blind spot detection algorithm with practical engineering application value and the traffic incident...
The SVM can realize data classification and prediction, the selection of penalty parameter c and kernel function g in training models directly affect the forecasting accuracy of the classification, the article use the K-CV method for c, g parameters optimization and processing, in wine species identification as an example to predict classification, improves the forecast accuracy, has reached the expected...
Comenia script is a novel handwritten text introduced at primary schools in the Czech Republic. This paper describes a method for handwritten text recognition (HWR) of this font. In particular it proposes a method for preprocessing and normalization of data and optical character recognition based on SVM classifier. We have trained and statistically evaluated several models, where we have focused on...
In recent years, higher education has been gaining importance in graduate students to make successful careers. So, academic organizations are given utmost importance for quality in academics to build the careers of the students. Faculty performance plays a vital role in academic institutions. In this paper, the performance of faculty members is evaluated on the basis of different parameters are taken...
Class imbalance is a major problem in machine learning. It occurs when the number of instances in the majority class is significantly more than the number of instances in the minority class. This is a common problem which is recurring in most datasets, including the one used in this paper (i.e. direct marketing dataset). In direct marketing, businesses are interested in identifying potential buyers,...
In the modern electricity market, it is very significant to have a precise electricity price forecasting in medium term time horizon. There are few studies done concentrating on the medium term forecasting of the electricity price. Medium term time horizon of electricity price forecasting is very useful and of great importance. It has many applications such as future maintenance scheduling of the...
It is completed the introduction and analysis for modeling method of glutamic acid fermentation process. Then modeling method using support vector machine is adopted, select a grid search-crossover integrated validation method to choose the parameters of the model in modeling, making the prediction model more accurate. Finally the comparison of SVM and BP networks model, simulation results show that...
This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of...
Degradation data is an important information source which is usually used to predict products' lifetime, for instance in accelerated degradation testing (ADT) and health management. Degradation data can be easier and cheaper obtained than failure data. As a result, it has been widely applied. However, due to some restrictions of funds and the development cycle, the degradation data of some products...
With the rapid growth of wind power connected to the power system, the problems caused by the volatile nature of wind speed have drawn more and more attention from system operator and researchers. The effective wind speed and power prediction of is the key to solve all these problems. This paper proposed a method based on prediction error revision, which use wavelet transform and Grid Search optimized...
Lending loans to borrowers is considered one of the main profit sources for banks and financial institutions. Thus, careful assessment and evaluation should be taken when deciding to grant credit to potential borrowers. With the rapid growth of credit industry and the massive volume of financial data, developing effective credit scoring models is very crucial. The literature in this area is very dense...
Different ranking algorithms have been proposed to fulfil the need of ranking. The problem is that most of the existing algorithms and models are just applicable on a specific data. When the data is imbalanced and heterogeneous, finding the records belonging to the minority class is significant especially in failure cases. So considering ranking as a classification problem of predicting the specific...
The free calcium oxide (f-CaO) contents directly reflects the quality of cement clinker. The content of free calcium oxide can't be measured directly on-line at the practical production and the chemical analysis is complicated and time consuming. A number of samples has been collected from the cement DCS control system, through data processing and smart algorithms, the f-CaO soft-measuring model is...
This paper proposes an investment in the stock exchange of Thailand (SET) using ARIMA model and support vector machine. Today, the investors are interesting to invest the stock market because it provides for higher profits than ones from deposit banking. Although the stock market can provide a high benefit for investors, it comes with a high risk too. Thus, this is a reason why we are proposing ARIMA...
Along with the increase number of users for the credit, the screening of applicants becomes very significant. If the credit of applicants is bad, the bank will obtain a great loss. Support vector machine (SVM) is one of the most popular kinds of algorithms for the new consumer's credit approval. However, there is a disadvantage that the more close to the optimal hyper plane, the greater possibility...
It should improve the forecasting accuracy in the study of precipitation prediction. It is difficultly to predict climate because of the dynamic characteristics of sample set as well as the effect of environmental factors. In order to improve the accuracy, a novel model based on time series and environmental factors was introduced in this paper. Firstly, the environmental factors were nonlinear screened...
The prediction of water inflow is significant to the preventing and controlling water of mine pit. In this paper SVM is applied to the prediction of water inflow, and the workflow and prediction model is given. Combing with the practical demands, a prediction was made about water inflow of a new working face. The result shows that the maximum value of water inflow of the new mining face is higher...
Software reliability prediction classifies software modules as fault-prone modules and less fault-prone modules at the early age of software development. As to a difficult problem of choosing parameters for Support Vector Machine (SVM), this paper introduces Particle Swarm Optimization (PSO) to automatically optimize the parameters of SVM, and constructs a software reliability prediction model based...
The icing of overhead transmission lines is the main problem for the safety of power grid, It is necessary to establish the model of icing load prediction on overhead transmission lines. The prediction model utilizes the ambient temperature, humidity, wind speed, wind direction, sunlight and air pressure as training data of input, the icing load is the output of prediction model based on Support Vector...
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