• Journal of Information Processing Systems
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• v.13 no.4
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• pp.914-927
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• 2017

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.206-211
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• 2010

This paper describes a skipping method for handling various illuminating effects in video sequences. The weighted prediction in H.264/AVC improves coding efficiency and image quality. However, it requires massive computation overheads for entire system, and thus, reducing the computation complexity becomes more important. Compared to the weighted prediction method in the H.264/AVC, the proposed method can decrease the bitrate down to 15%. Moreover, the proposed algorithm can reduce computation complexity down to 30%, compared to the localized weighted prediction which does not skip unnecessary calculation.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.489-496
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• 2015

In this paper, a new weighted prediction scheme is proposed to improve the coding efficiency for video scenes containing brightness variations. Conventional weighted prediction is applied by the reference picture and use only one weighted parameter set. Thus, it is only useful for GBV(Glabal Brightness Variation). In order to solve this problem, the proposed algorithm use three kind of schemes depending on situation. Experimental results show that maximum coding efficiency gain of the proposed method is up to 10.2% in luminance. Average computional time complexity is increased about 163% in encoder and about 101% in decoder.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.467-474
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• 2006

In this paper we present a weighted support vector machine(SVM) and a weighted least squares support vector machine(LS-SVM) for the prediction in the heteroscedastic regression model. By adding weights to standard SVM and LS-SVM the better fitting ability can be achieved when errors are heteroscedastic. In the numerical studies, we illustrate the prediction performance of the proposed procedure by comparing with the procedure which combines standard SVM and LS-SVM and wild bootstrap for the prediction.

• ETRI Journal
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• v.33 no.1
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• pp.27-31
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• 2011

This paper addresses estimating the frequency of a cisoid in the presence of white Gaussian noise, which has numerous applications in communications, radar, sonar, and instrumentation and measurement. Due to the nonlinear nature of the frequency estimation problem, there is threshold effect, that is, large error estimates or outliers will occur at sufficiently low signal-to-noise ratio (SNR) conditions. Utilizing the ideas of averaging to increase SNR and weighted linear prediction, an optimal frequency estimator with smaller threshold SNR is developed. Computer simulations are included to compare its mean square error performance with that of the maximum likelihood (ML) estimator, improved weighted phase averager, generalized weighted linear predictor, and single weighted sample correlator as well as Cramer-Rao lower bound. In particular, with smaller computational requirement, the proposed estimator can achieve the same threshold and estimation performance of the ML method.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.717-722
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• 2004

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer using neural network with weighted fuzzy membership functions (NNWFM). NNWFM is capable of self-adapting weighted membership functions to enhance accuracy in prediction from the given clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from the enhanced bounded sums of n set of weighted fuzzy membership functions. Two number of prediction rules extracted from NNWFM outperforms to the current published results in number of rules and accuracy with 99.41%.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.138-143
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• 2022

The employment prediction model proposed in this paper uses 16 independent variables, including self-introductions of M University students who applied for IPP and work-study internship, and 3 dependent variable data such as large companies, mid-sized companies, and unemployment. The employment prediction model for large companies was developed using Random Forest and Word2Vec with the result of F1_Weighted 82.4%. The employment prediction model for medium-sized companies and above was developed using Logistic Regression and Word2Vec with the result of F1_Weighted 73.24%. These two models can be actively used in predicting employment in large and medium-sized companies for M University students in the future.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.78-82
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• 1999

This paper is a study on weighted cepstrum used broadly for robust speech recognition. Especially, we propose the weighted function of asymmetric glottal pulse shape. which is used for weighted cepstrum extracted by PLP(Perceptual Linear Predictive) based on auditory model. Also, we analyze this glottal weighted cepstrum from the glottal pulse of glottal model in connection with the cepstrum. And we obtain speech features analyzed by both the glottal model and the auditory model. The isolated-word recognition rate is adopted for the test of proposed method in the car moise and street environment. And the performance of glottal weighted cepstrum is compared with both that of weighted cepstrum extracted by LP(Linear Prediction) and that of weighted cepstrum extracted by PLP. The result of computer simulation shows that recognition rate of the proposed glottal weighted cepstrum is better than those of other weighted cepstrums.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.851-860
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• 2009

Recently, among the computational methods of protein-protein interaction prediction, vast amounts of domain based methods originated from domain-domain relation consideration have been developed. However, it is true that multi domains collaboration is avowedly ignored because of computational complexity. In this paper, we implemented a protein interaction prediction system based the Interaction Significance matrix, which quantified an influence of domain combination pair on a protein interaction. Unlike conventional domain combination methods, IS matrix contains weighted domain combinations and domain combination pair power, which mean possibilities of domain collaboration and being the main body on a protein interaction. About 63% of sensitivity and 94% of specificity were measured when we use interaction data from DIP, IntAct and Pfam-A as a domain database. In addition, prediction accuracy gradually increased by growth of learning set size, The prediction software and learning data are currently available on the web site.