• Journal of Biosystems Engineering
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• v.17 no.4
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• pp.370-381
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• 1992

Optical properties of fruits can provide information for nondestructive quality evaluation. An attempt is made here to develop an optical method for quality evaluation of apple using the spectral reflectance of its surface. Optical fiber was used to transmit light conveniently from light source to sample and sample to detector. Spectral reflectances of two types of Fuji variety-one of which was exposed to the sunlight directly (non-bagging) and the other was wrapped with bag (bagging) - were investigated from the wavelength ranging of 400nm to 700nm. The relationships between reflectance characteristics and quality indices such as chlorophyll content, anthocyanin content, and soluble solids were analyzed.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.89-95
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• 2023

Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.3
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• pp.47-53
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• 2001

This paper investigates the characteristics of interlaminar fracture toughness of foam core sandwich structures under opening mode by using the double cantilever beam (DCB) specimens which are Carbon/Epoxy and foam core composites. Instead of using a DCB specimen of symmetric geometry, a non-symmetric DCB specimen was used to calculate the interlaminar fracture toughness. Three approaches for calculating the energy release rate(G$\sub$IC/) were used and fracture toughness of foam core sandwich structures made by autoclave, vacuum bagging and hotpress were compared. Experiment, analysis using nonlinear beam bending theory, and numerical work by FEM methods were performed. Bonding surface compensation and equivalent moment of inertia were used to calculate the energy release rate in nonlinear analytical work. Conclusions of experimental, nonlinear analytical and FEM methods were compared. It is, also, shown that the vacuum bagging forming can substitute the method of autoclave without serious loss of Mode I energy release rate(G$\sub$I/).

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1229-1237
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• 2005

Combining multiple classifiers has been actively exploited to improve classification performance. It is required to construct a pool of accurate and diverse base classifier for obtaining a good ensemble classifier. Conventionally ensemble learning techniques such as bagging and boosting have been used and the diversify of base classifiers for the training set has been estimated, but there are some limitations in classifying gene expression profiles since only a few training samples are available. This paper proposes an ensemble technique that analyzes the diversity of classification rules obtained by genetic programming. Genetic programming generates interpretable rules, and a sample is classified by combining the most diverse set of rules. We have applied the proposed method to cancer classification with gene expression profiles. Experiments on lymphoma cancer dataset, prostate cancer dataset and ovarian cancer dataset have illustrated the usefulness of the proposed method. h higher classification accuracy has been obtained with the proposed method than without considering diversity. It has been also confirmed that the diversity increases classification performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.41-48
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• 2012

Red tide is a natural phenomenon temporary blooming harmful algal with changing sea color from normal to red, which fish and shellfish die en masse. It also give a bad influence to coastal environment and sea ecosystem. The damage of sea farming by a red tide has been occurred each year which it cost much to prevent disasters of red tide blooms. Red tide damage and prevention cost of red tide disasters can be minimized by means of prediction of red tide blooms. In this paper, we proposed the red tide blooms prediction method using ensemble train. The proposed method use the bagging and boosting ensemble train methods for enhancing red tide prediction and forecast. The experimental results demonstrate that the proposed method achieves a better red tide prediction performance than other single classifiers.

• Proceedings of the Korea Association of Information Systems Conference
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• 2002.11a
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• pp.289-312
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• 2002

This study suggests the Link weight analysis approach to choose input variables and an integrated model to make more accurate bankruptcy prediction model. the Link weight analysis approach is a method to choose input variables to analyze each input node's link weight which is the absolute value of link weight between an input nodes and a hidden layer. There are the weak-linked neurons elimination method, the strong-linked neurons selection method in the link weight analysis approach. The Integrated Model is a combined type adapting Bagging method that uses the average value of the four models, the optimal weak-linked-neurons elimination method, optimal strong-linked neurons selection method, decision-making tree model, and MDA. As a result, the methods suggested in this study - the optimal strong-linked neurons selection method, the optimal weak-linked neurons elimination method, and the integrated model - show much higher accuracy than MDA and decision making tree model. Especially the integrated model shows much higher accuracy than MDA and decision making tree model and shows slightly higher accuracy than the optimal weak-linked neurons elimination method and the optimal strong-linked neurons selection method.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.41-55
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• 2017

This study predicts the average scores of top 150 PGA golf players on 132 PGA Tour tournaments (2013-2015) using data mining techniques and statistical analysis. This study also aims to predict the Top 10 and Top 25 best players in 4 different playoffs. Linear and nonlinear regression methods were used to predict average scores. Stepwise regression, all best subset, LASSO, ridge regression and principal component regression were used for the linear regression method. Tree, bagging, gradient boosting, neural network, random forests and KNN were used for nonlinear regression method. We found that the average score increases as fairway firmness or green height or average maximum wind speed increases. We also found that the average score decreases as the number of one-putts or scrambling variable or longest driving distance increases. All 11 different models have low prediction error when predicting the average scores of PGA Tournaments in 2015 which is not included in the training set. However, the performances of Bagging and Random Forest models are the best among all models and these two models have the highest prediction accuracy when predicting the Top 10 and Top 25 best players in 4 different playoffs.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.10a
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• pp.105-110
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• 2001

Autoclave curing using the vacuum bagging method is widely used for the manufacture of advanced composite prepreg airframe structures. Due to increasing use of advanced composites, specific techniques have been developed to repair damaged composite structures. In order to repair the damaged part, it is required that the damaged areas be removed, such as skin and/or honeycomb core, by utilizing the proper method and then repairing the area by laying up prepreg (and core) then curing under vacuum using the vacuum bagging materials. It shall be cured either in an oven or autoclave per the original specification requirements. Delamination can be observed in the sound areas during and/or after a couple times exposure to the elevated curing temperature due to the repeated repair condition. This study was conducted for checking the degree of degradation of properties of the cured parts and delamination between skin prepreg and honeycomb core. Specimens with glass honeycomb sandwich construction and glass/epoxy prepreg were prepared. The specimens were cured 1 to 5 times at $260^{circ}F$ in an autoclave and each additionally exposed 50, 100 and 150 hours in the $260^{circ}F$ oven. Each specimen was tested for tensile strength, compressive strength, flatwise tensile strength and interlaminar shear strength. To monitor the characteristics of the resin itself, the cured resin was tested using DMA and DSC. As a results, the decrease of Tg value were observed in the specific specimen which is exposed over 50 hrs at $260^{circ}F$. This means the change or degradative of resin properties is also related to the decrease of flatwise tensile properties. Accordingly, minimal exposure on the curing temperature is recommended for parts in order to prevent the delation and maintain the better condition.