• Journal of Information Processing Systems
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• v.15 no.2
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.809-819
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• 2020

Gender prediction accuracy increases as convolutional neural network (CNN) architecture evolves. This paper compares voting and ensemble schemes to utilize the already trained five CNN models to further improve gender prediction accuracy. The majority voting usually requires odd-numbered models while the proposed softmax-based voting can utilize any number of models to improve accuracy. The ensemble of CNN models combined with one more fully-connected layer requires further tuning or training of the models combined. With experiments, it is observed that the voting or ensemble of CNN models leads to further improvement of gender prediction accuracy and that especially softmax-based voters always show better gender prediction accuracy than majority voters. Also, compared with softmax-based voters, ensemble models show a slightly better or similar accuracy with added training of the combined CNN models. Softmax-based voting can be a fast and efficient way to get better accuracy without further training since the selection of the top accuracy models among available CNN pre-trained models usually leads to similar accuracy to that of the corresponding ensemble models.

• Korean Journal of Biological Psychiatry
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• v.30 no.1
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• pp.24-30
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• 2023

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• KEPCO Journal on Electric Power and Energy
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• v.3 no.2
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• pp.133-140
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• 2017

The performance prediction software developed in this paper is a process analysis tool that enables one to foretell the behavior of processes when certain conditions of operation are altered. The immediate objective of this research is to predict the process characteristics of combined heat and power plant under varying operating conditions. A cogeneration virtual power plant that mimics the mechanical performance of the actual plant was constructed and the performance of the power plant was predicted in the following varying atmospheric conditions: temperature, pressure and humidity. This resulted in a positive outcome where the performance of the power plant under changing conditions were correctly predicted as well as the calorific value of low calorific gas fuel such as shale gas and PNG. The performance prediction tool can detect the operation characteristics of the power plant through the performance index analysis and thus propose the operation method taking into consideration the changes in environmental conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.492-503
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• 2013

H.264/AVC has shown high coding efficiency by using various coding tools, including intra and inter prediction. However, there are still many more redundancy components in intra prediction than in inter prediction. In this paper, a novel intra prediction method is proposed with adaptive mode selection. The combined intra prediction modes and simplified gradient modes are added in order to refine the directional feature and gradation region. Suitable modes are selected according to the neighboring blocks that provide a high compression rate and lower computational complexity. The improvement of the proposed method is 1.96% in terms of the bitrate, 0.25 dB in PSNR, and 1.72 times in terms of the computational complexity.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.2
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• pp.142-148
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• 2005

This paper proposes a cell age model for Penicillium chrysogenum fed-batch cultivation to supply a qualitative insight into morphology-associated dynamics. The average ages of the segregated cell populations, such as growing cells, non-growing cells and intact productive cells, were estimated by this model. A combined model was obtained by incorporating the aver-age ages of the cell sub-populations into a known but modified segregated kinetic model from literature. For simulations, no additional effort was needed for parameter identification since the cell age model has no internal parameters. Validation of the combined model was per-formed by 20 charges of industrial-scale penicillin cultivation. Meanwhile, only two charge-dependent parameters were required in the combined model among approximately 20 parameters in total. The model is thus easily transformed into an adaptive model for a further application in on-line state variables prediction and optimal scheduling.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.05a
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• pp.70-73
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• 2004

The approach in this thesis is for prediction of deformed strip profile in hot rolling mill. This approach shows how to make an expression as a mathematical form in predicting strip profile. This approach is based on the velocity field, shear stress and material flow on the strip edge along width direction and lateral displacement and stress along width are analytically calculated. Roll force is calculated in each section and then combined together to show roll force distribution along width. All the assumptions to make equation form for this approach are supported by FEM simulation result and the result of model is verified by FEM result. So, this model will supply very useful tool to the researcher and engineers which takes less time and has similar accuracy in predicting roll force profile comparing to FEM simulation. This model has to be combined with deformed roll profile model, which include thermal crown prediction and wear prediction model to predict deformed strip profile.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.133-138
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• 2003

The prediction of protein secondary structure has been an important bioinformatics tool that is an essential component of the template-based protein tertiary structure prediction process. It has been known that the predicted secondary structure information improves both the fold recognition performance and the alignment accuracy. In this paper, we describe several novel ideas that may improve the prediction accuracy. The main idea is motivated by an observation that the protein's structural information, especially when it is combined with the evolutionary information, significantly improves the accuracy of the predicted tertiary structure. From the non-redundant set of protein structures, we derive the 'potential' parameters for the protein secondary structure prediction that contains the structural information of proteins, by following the procedure similar to the way to derive the directional information table of GOR method. Those potential parameters are combined with the frequency matrices obtained by running PSI-BLAST to construct the feature vectors that are used to train the support vector machines (SVM) to build the secondary structure classifiers. Moreover, the problem of huge model file size, which is one of the known shortcomings of SVM, is partially overcome by reducing the size of training data by filtering out the redundancy not only at the protein level but also at the feature vector level. A preliminary result measured by the average three-state prediction accuracy is encouraging.