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Artificial neural networks (ANNs) have rarely been used in the field of medical education, especially in the prediction of learning performances. This study aims to evaluate the potential application of ANN models for predicting learning performances, in comparison with multivariate logistic regression models. The predictor variables included demographics, high-school backgrounds, first-year grade-point...
Cataclysmic variable (CV) stars are binary stars that consist of two components: a white dwarf primary, and a mass transferring secondary. Due to the relative faint of cataclysmic variable and a large number of irregular changes, it is not easy to get valuable data and important research results on observation. But they have significant meaning on the subsequent research of these spectra. In general,...
Machine Learning (ML) provides a theoretical and methodological framework that allows to quantify the relationship between the user's Quality of Experience (OoE) and the network's Quality of Service (QoS). In the literature, several ML-based QoS/QoE correlation models have been proposed. All of those models use inductive supervised learning techniques and most of them are built in an offline batch...
Gait analysis applications are not only limited to medical, rehabilitation and sports, but it can also play a decisive role in security and surveillance as a behavioral biometric factor. Gait recognition is non-invasive and doesn't need any cooperation from subject in case of video surveillance. This paper presents a framework for human recognition based on gait without using markers or sensors using...
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’...
Micro-blog is easily posted and communicated because of its short text length. Meanwhile, its brevity limits the feature expression. This paper presents a blogger modeling method for Chinese micro-blog sentiment classification. The paper would adopt a multidimensional strategy to accomplish the feature extraction task. Compared with traditional feature extraction methods and combined with the interactive...
Financial forecasting plays a critical role in present economic context where neural networks have become a good alternative technique over traditional methods. Vast ranges of neural models are developed to achieve better accuracy in forecasting. In addition, the ways to find out a good neural architecture is being explored by the research community. In the literature, main problems are figured out...
On the basis of analyzing disadvantages of conventional prediction model of air-and-screen cleaning device, a new regression model based on support vector machine was proposed to predict and control of cleaning process precisely. Parameters of ε-SVR models were determined utilizing non-heuristic Grid Search, heuristic GA and PSO which could avoid the choice of randomness. The effect of samples in...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
Continuously increasing amounts of data in data warehouses are providing companies with ample opportunity to conduct analytical customer relationship management (CRM). However, how to utilize the information retrieved from the analysis of these data to retain the most valuable customers, identify customers with additional revenue potential, and achieve cost-effective customer relationship management,...
An electromagnetic parameter extraction method using SVM (Support Vector Machines) and open-ended rectangular waveguide, is established in this paper. A lot of S parameters, which are used to train the SVM, are calculated from the finite element models. After being trained, the SVM is used to predict the permittivity of the material using the known scattering coefficients. The simulation results show...
In this paper we demonstrate the effectiveness of employing basic sentiment components for analyzing the chief sentiment of Chinese sentence among nine categories of sentiments (including “No emotion”). Compared to traditional lexicon based methods, our research explores emotion intensities of words and phrases in an eight dimensional sentiment space as features. An emotion matrix kernel is designed...
In this paper, we propose a compound pyramid model to predict protein secondary structure, where homology analysis and an improved support vector machine (SVM) technology are used for predicting protein secondary structure. The homology analysis is based on BP network model which uses pair-wise sequence alignment, and SVM classification considers the physical and chemical properties of amino acids...
Water quality monitoring is a prerequisite for water pollution control. The rapid development of remote sensing technology will provide a new technical support. In this paper, with the study of the Weihe River in Shaanxi Province, RWQMS (Remote-sensing Water Quality Monitoring System V1.0) is designed and implemented based on SPOT-5 remote-sensing image and the system structure and key technologies...
SVM possess great potential and superior performance owing to the structural risk minimization (SRM) principle in SVM that has greater generalization ability and is superior to the empirical risk minimization (ERM) principle as adopted in neural networks. Considering the characteristics of the thunderstorm in Chongqing, the thunderstorm prediction model based on least square support vector machine...
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA)...
Yield is a very important criterion to measure the semiconductor wafer fabrication facilities (FABs) productivity. The finished products will be check by Wafer Acceptance Test (WAT) and Circuit Probe (CP) to classified into ferior goods or inferior goods. This research applied the data from WAT and CP for the selection of the most important measuring parameters to improve the yield. Three methods,...
Protein secondary structure prediction plays an important role in protein engineering. But its accuracy has not been improved dramatically in recent years and many methods rely on servers to get first data. So an independent homology analysis method is proposed. Besides, this method is successfully applied to the homology analysis module located at comprehensive layer in our compound pyramid model...
The end effects of Hilbert-Huang transform are produced in the Empirical Mode Decomposition(EMD) and the Hilbert transform for Intrinsic Mode Functions(IMF), which have a badly effect on Hilbert-Huang transform. In order to overcome this problem, the multi-objective allocation Genetic Algorithm (GA) to solve the kernel parameters selection of Least Squares Support Vector Machine (LSSVM)(GLHHT) is...
Support Vector Machine (SVM) is applied to the modern educational measurement's diagnostic classification of 0,1 scoring test, and then comparisons of the classification results with some of the typical cognitive diagnostic classification are made. The results show that using SVM to cognitive diagnostic classification, which only needs a small sample for training, can ensure a high correct classification...
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