In this paper, we will present an educational game that we developed in order to teach a chemistry lesson, namely drawing a Lewis diagram. We also conducted an experiment to gather data about the cognitive and emotion...
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ISBN:
(纸本)9783319244891;9783319244884
In this paper, we will present an educational game that we developed in order to teach a chemistry lesson, namely drawing a Lewis diagram. We also conducted an experiment to gather data about the cognitive and emotional states of the learners as well as their behaviour throughout our game by using three types of sensors (electroencephalography, eye tracking, and facial expression recognition with an optical camera). Primary results show that a machine learning model (logisticregression) can predict with some success whether the learner will give a correct or a wrong answer to a task presented in the game, and paves the way for an adaptive version of the game. This latter will challenge or assist learners based on some features extracted from our data in order to provide real-time adaptation specific to the user.
For many years Malaysia faced the drug addiction issues. The most serious case is relapse phenomenon among treated drug addict (drug addict who have under gone the rehabilitation programme at Narcotic Addiction Rehabi...
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ISBN:
(纸本)9780735412415
For many years Malaysia faced the drug addiction issues. The most serious case is relapse phenomenon among treated drug addict (drug addict who have under gone the rehabilitation programme at Narcotic Addiction Rehabilitation Centre, PUSPEN). Thus, the main objective of this study is to find the most significant factor that contributes to relapse to happen. The binary logisticregression analysis was employed to model the relationship between independent variables (predictors) and dependent variable. The dependent variable is the status of the drug addict either relapse, (Yes coded as 1) or not, (No coded as 0). Meanwhile the predictors involved are age, age at first taking drug, family history, education level, family crisis, community support and self motivation. The total of the sample is 200 which the data are provided by AADK (National Antidrug Agency). The finding of the study revealed that age and self motivation are statistically significant towards the relapse cases..
The real-time health monitoring and fault diagnosis system of a hydraulic pump is important for the role of the pump as the power source of the entire hydraulic system. Thus, this study proposes health assessment base...
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The real-time health monitoring and fault diagnosis system of a hydraulic pump is important for the role of the pump as the power source of the entire hydraulic system. Thus, this study proposes health assessment based on logisticregression (LR) and fault classification based on softmax regression. The real-time state of the system is obtained by processing the data of vibration signals collected from the pumps, and maintenance can be performed as long as the failure or malfunction occurs. The vibration signal is decomposed into several product functions by local mean decomposition, and the product functions that contain fault information form a feature vector by extracting energy values and corresponding time-domain statistical magnitudes. Multidimensional scaling is used for feature reduction. The LR model and softmax regressionmodel are trained by the reduced features to obtain health conditions and classify possible fault modes, respectively. The maximum likelihood method is applied to determine the parameters of the LR model, and the gradient descent method is used to determine the parameters of the softmax regressionmodel. This method has been applied to process the vibration signals of a real hydraulic pump to verify its effectiveness and feasibility.
A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters is proposed to avoid the error that is caused by the difference between the real data distribution and the hypoth...
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A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters is proposed to avoid the error that is caused by the difference between the real data distribution and the hypothetic Gaussian distribution and avoid the computational burden working in the logisticregression classification directly for hyperspectral data. The multi-fractal spectra and parameters are calculated firstly with training samples along the spectral dimension of hyperspectral data. Secondly, the logistic regression model is employed in our work because the logisticregression classification model is a distribution-free nonlinear model which is based on the conditional probability without the Gaussian distribution assumption of the random variables, and the obtained multi-fractal parameters are applied to establish the multi-class logisticregression classification model. Finally, the Newton–Raphson method is applied to estimate the model parameters via the maximum likelihood algorithm. The classification results of the proposed model are compared with the logisticregression classification model based on an adaptive bands selection method by using the Airborne Visible/Infrared Imaging Spectrometer and airborne Push Hyperspectral Imager data. The results illuminate that the proposed approach achieves better accuracy with lower computational cost simultaneously. [ABSTRACT FROM AUTHOR]
The problem of estimation of the parameters in a logistic regression model is considered under multicollinearity situation when it is suspected that the parameter of the logistic regression model may be restricted to ...
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The problem of estimation of the parameters in a logistic regression model is considered under multicollinearity situation when it is suspected that the parameter of the logistic regression model may be restricted to a subspace. We study the properties of the preliminary test based on the minimum phi-divergence estimator as well as in the phi-divergence test statistic. The minimum phi-divergence estimator is a natural extension of the maximum likelihood estimator and the phi-divergence test statistics is a family of the test statistics for testing the hypothesis that the regression coefficients may be restricted to a subspace.
The prime objective of this study was to investigate the factors having the significance on the choice of cattle marketing channels, and in addition, participation of the farmers in mainstream formal market. Fiftyfive...
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The prime objective of this study was to investigate the factors having the significance on the choice of cattle marketing channels, and in addition, participation of the farmers in mainstream formal market. Fiftyfive (n=55) farmers were purposively and randomly selected for a questionnaire-based data collection mini-survey. The study was conducted in the Musekwa Valley in Vhembe District, Limpopo Province, South Africa. Descriptive data were collected and fitted to a Binary logistic regression model to determine the significant factors. The majority of the cattle farmers preferred the informal market (56.4%) ahead of mainstream formal market (43.6%). Farm record keeping, distance to the mainstream formal marketing channel and farm productivity revealed the high significance. Workshops should be conducted for the farmers on farm record keeping and farm productivity improvements while marketing infrastructure could be developed nearer the farmers’ villages to promote participation of the farmers in mainstream formal marketing channels.
Although it is known that synonymous codons are not chosen randomly, the role of the codon usage in gene regulation is not clearly understood, yet. Researchers have investigated the relation between the codon usage an...
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Although it is known that synonymous codons are not chosen randomly, the role of the codon usage in gene regulation is not clearly understood, yet. Researchers have investigated the relation between the codon usage and various properties, such as gene regulation, translation rate, translation efficiency, mRNA stability, splicing, and protein domains. Recently, a universal codon usage based mechanism for gene regulation is proposed. We studied the role of protein sequence patterns on the codons usage by related genes. Considering a subsequence of a protein that matches to a pattern or motif, we showed that, parts of the genes, which are translated to this subsequence, use specific ratios of synonymous codons. Also, we built a multinomial logisticregression statistical model for codon usage, which considers the effect of patterns on codon usage. This model justifies the observed codon usage preference better than the classic organism dependent codon usage. Our results showed that the codon usage plays a role in controlling protein levels, for genes that participate in a specific biological function. This is the first time that this phenomenon is reported.
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