• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.75-80
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• 2019

In machine learning, the performance of the system depends upon the nature of input data. The efficiency of the system improves when the behavior of the input data changes from un-normalized to normalized form. This paper experimentally demonstrated the performance of KNN, SVM, LDA and NB on Alzheimer's dataset. The dataset undertaken for the study consisted of 3 classes, i.e. Demented, Converted and Non-Demented. Analysis shows that LDA and NB gave an accuracy of 89.83% and 88.19% respectively in both the cases whereas the accuracy of KNN and SVM improved from 46.87% to 82.80% and 53.40% to 88.75% respectively when input data changed from un-normalized to normalized state. From the above results it was observed that KNN and SVM show significant improvement in classification accuracy on normalized data as compared to un-normalized data, whereas LDA and NB reflect no such change in their performance.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.5
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• pp.64-69
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• 2014

This article presents machining experiments to assess the relationship between the profile accuracy and the workpiece hardness using a natural diamond tool on an ultra-precision diamond turning machine. The study is intended to secure a corner cube prism pattern for reflective film capable of high-quality outcomes. The optical performance levels and edge images of corner cubes having various hardness levels of the copper-coated layer on a carbon steel plate are analyzed. The hardness of the workpiece has a considerable effect on the profile accuracy. The higher the hardness of the workpiece, the better the profile accuracy and the worse the edge wear of the diamond tool.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.2
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• pp.53-58
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• 2003

It is very important to measure linear cycle plane positioning accuracy of NC lathe as it effects all other parts of machines machined by them in industries. If the plane positioning accuracy of NC lathe is bad, the dimension accuracy and the change-ability of works will be bad in the assembly of machine parts. In this paper, computer software systems are organized to measure linear cycle plane positioning displacement of ATC(Automatic tool changer) on zx plane of NC lathe using two linear scales. And each sets of error data obtained from the test is descriptions to plots and the results of linear cycle plane positioning errors are expressed as nutriments by computer treatment.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.65-70
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• 2002

The rotational performance off machine tool spindle has a direct influence upon the surface finish of the finished workpiece. This running accuracy of the spindle is improved by increasing preload on the bearings, while it results in higher temperature rise and larger thermal deformation. Therefore, finding the optimal preload condition for high speed spindle is very important factors in spindle motion. in spindle motion. In this study, the effect of the preload on the roundness accuracy has been examined at the different cutting conditions. Experiments were carried out to investigate the effects of the bearing preload on the running accuracy of high speed spindle which was supported by two angular contact bearings.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.98-104
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• 1999

As the accuracy of manufactured goods needed high accuracy processing has made the efficiency of NC and measurement technology development, the innovation of machine tools has influence the development of the semi-conductor and optical technology. The movement errors can be expressed in terms of yaw, roll an pitch etc. In the case of expanding the error range, static, dynamic and servo gain errors can be included. Machining center might have twenty-one movement errs including three types of joint errors. Those errors have been measured on the condition of just loading of standard table. Regarding these measuring methods, the mechanical compliance of the structure has not been considered. In this paper, therefor, the influences of the additional load on the positioning accuracy are investigated. The results and the techniques proposed in this study can be considered very effective and useful to compensate more correctly the positioning motion.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.