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Every educational institute feels proud when its admission closes with expected number of students. The prospective student enters the campus with lots of hopes, dreams and expectations. When their expectations are not met or if they undergo for critical circumstances and makes them drop from their registered program. Predicting undergraduate student dropouts are a major challenge in educational system...
Given a very large dataset of moderate-to-high dimensionality, how to mine useful patterns from it? In such cases, dimensionality reduction is essential to overcome the "curse of dimensionality". Although there exist algorithms to reduce the dimensionality of Big Data, unfortunately, they all fail to identify/eliminate non-linear correlations between attributes. This paper tackles the problem...
Demand side management (DSM) is a key mechanism to make smart grids cost efficient using electricity price forecasting issue. Price forecasting method takes the big price data into account, and gives estimates of the future electricity price. However, most of existing price forecasting methods cannot avoid redundancy at feature selection and lack of an integrated framework that coordinates the steps...
Customer satisfaction is an important factor governing adoption and retention of multimedia products and services, such as Over-The-Top(OTT) video transmission. Quality of Experience involves user-centric evaluation of various services. However, users differ in terms of their ratings of service quality. Some rating differences are due to unreliability (outlier users who are not motivated, or are not...
Quality of Experience (QoE) becomes a topic of utmost eminence for service providers and the major factor in the success of multimedia services. Thus, it is challenging to investigate thoroughly the human side of QoE in order to find out the impact of factors that affect user satisfaction. In this paper, we provide a structured way to build an accurate and objective QoE model. In order to serve this...
In this paper the key indicators of sports tourism competitiveness were selected through the factor analysis method, by using the factor analysis method in Spss22.0 software, all kinds of sports tourism data in each city and county of Hainan were analyzed according to the factor analysis method and the sports tourism competitiveness of each city and county were also evaluated in comprehensive scores...
This study investigates the relative efficacy of using n-grams extracted terms, the aggregation of such terms, and a combination of feature extraction techniques in building an automated essay-type grading (AETG) system. The paper focused on the modification of the Principal Component Analysis (PCA) by integrating n-grams terms as input into the PCA algorithm. Hardcopies of examiners' marking schemes...
In this paper the application of a new method of features selection was presented. Its effects were compared with several other methods of features selection. The study were performed using a data set containing samples of the sound signal emitted by the arteriovenous fistula. The aim was to create a solution with multiclass classification based on the k-NN classifier family allowing for effective...
Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheets, cash flows, and income statements) in Form 10-K or annual reports of companies. Balance sheets, cash flows, and income statements have some structure in them and are semi-structured...
An approach was developed using maximum eigenvalue principal components analysis(PCA) neural network for direct sequence spread spectrum (DSSS) signals to reconstruct the pseudo noise(PN) sequence blindly in low SNR conditions. Firstly, the received signals were divided into column vectors to form an observation matrix according to a temporal window, whose duration is one period of PN sequence. Then,...
We consider the problem of estimating of the number of components that are correlated between two sets of high-dimensional data. In many applications the number of available samples is very small, in which case conventional techniques do not accurately determine the model order. Recent approaches for the sample-poor scenario are based on a combined PCA-CCA (principal component analysis-canonical correlation...
We present a tailored sparse principal component analysis approach to identify parts of the Hepatitis C virus (HCV) proteome that may be particularly susceptible to immune pressure and thus may help in the design of an effective vaccine. Considering the highly data-limited HCV NS5B protein, the proposed method reveals two reasonably small sets of potentially vulnerable sites which can serve as new...
Recursive Principal Components Analysis is explored as a method to identify and classify fault sources in a 12MW steam dual fuel power plant. The algorithm assessment is performed off-line by using data of relevant plant wide-information. A simple contributions matrix based in normalized data is proposed to diagnose plant faults. Results indicate it is possible to detect, classify and possibly even...
Providing high quality recommendations is significant for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering (CF) is one of the most well-known and successful techniques to generate recommendations. However, CF suffers from an inherent issue that does not think over the auxiliary information such as item content information...
Gene — Gene Interaction is a logical interaction between two genes that affects the observable behavior of one organism. This genetic interaction helps to identify pathways of associated genes for various diseases. In this paper we have used two metrics, like correlation & entropy to find the level of interaction between the genes applied on Gene Interaction networks. We have applied our algorithm...
This work analyses consumption obtained from high-power appliances in order to determine it characteristic curve. Due it high consumption, induction cooker, washer and dryer were studied. Meter FLUKE 1735 was used to acquire a samples per second. Acquired data as: voltage, current, voltage and current harmonics. Principal components analysis was used to reduce the size of each load data matrix and...
This paper describes the adaptation and validation of an instrument aimed to determine self-regulation skills, learning strategies and affective strategies of engineering students from the Tecnologico de Monterrey, Mexico City Campus. A statistical validation with Cronbach's alphas and a social validation in which students indicated whether or not they agree with their results on each dimension were...
In pattern recognition, the usage of appropriate similarity measure is crucial for acquiring robust performance. In this paper, we present two similarity measures, which compute a similarity between two matrices, based on the temporal feature variation and sequence length difference, respectively. Especially, the matrix object used in this paper has unique characteristic. Each row and column has different...
Respiratory gating is a powerful tool for tackling motion-related issues in chest PET imaging. On current scanners the respiratory signal is obtained from external devices, whereas with Data-Driven methods it can be extracted directly from the data. The aim of this work is to show the increased potential of the application of Principal Component Analysis (PCA) on TOF data. We propose a methodology...
Background subtraction is a basic task of video analysis. Many methods have been proposed earlier for background subtraction, it is still challenging. In the present work, a novel method using canonical correlation analysis for background subtraction is proposed. The correlation between current image and background image obtained through canonical correlation analysis is used to detect the foreground...
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