• The korean journal of orthodontics
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• v.52 no.2
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4527-4548
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• 2017

This paper proposes a method to generate panoramic images by combining conventional feature extraction algorithms (e.g., SIFT, SURF, MPEG-7 CDVS) with sensed data from inertia sensors to enhance the stitching results. The challenge of image stitching increases when the images are taken from two different mobile phones with no posture calibration. Using inertia sensor data obtained by the mobile phone, images with different yaw, pitch, and roll angles are preprocessed and adjusted before performing stitching process. Performance of stitching (e.g., feature extraction time, inlier point numbers, stitching accuracy) between conventional feature extraction algorithms is reported along with the stitching performance with/without using the inertia sensor data. In addition, the stitching accuracy of video data was improved using the same sensed data, with discrete calculation of homograph matrix. The experimental results for stitching accuracies and speed using sensed data are presented in this paper.

• International Journal of Contents
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• v.1 no.2
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• pp.5-9
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• 2005

Currently most Internet documents including data are made based on predefined templates, but templates are usually formed only for main data and are not helpful for information retrieval against indexes, advertisements, header data etc. Templates in such forms are not appropriate when Internet documents are used as data for information retrieval. In order to process Internet documents in various areas of information retrieval, it is necessary to detect additional information such as advertisements and page indexes. Thus this study proposes a method of detecting the layout of Web pages by identifying the characteristics and structure of block tags that affect the layout of Web pages and calculating distances between Web pages. This method is purposed to reduce the cost of Web document automatic processing and improve processing efficiency by providing information about the structure of Web pages using templates through applying the method to information retrieval such as data extraction.

• Journal of the Society of Disaster Information
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• v.7 no.2
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• pp.140-150
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• 2011

Life Environment is rapidly changing and large scale disasters are increasing from the global warming. Although the disaster repair resources are deployed to the disaster fields, the prevention of the disasters is the most effective countermeasures. the disaster sign data is based on the rule of Heinrich. Automatic extraction of the disaster sign data from the web is the focused issues in this paper. We defined the automatic extraction processes and applied information, such as accident nouns, disaster filtering nouns, disaster sign nouns and rules. Using the processes, we implemented the disaster sign data management system. In the future, the applied information must be continuously updated, because the information is only the extracted and analytic result from the some disaster data.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.79-89
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• 2010

Cellular and Smart phone through the business and real life is associated with an increasing number of information processing, Breaches associated with mobile terminal Tile has occurred and cause Crime and damage. In this paper, Cellular and Smart phone for mobile forensics SYN scheme and JTAG scheme to target Cellular and Smart phone for the extraction of forensic data will be studied. SYN, JTAG approach to forensic analysis indicate with the process, Every Smart phone's OS specific performance and data extraction were compared. In the laboratory, Cell and smart phone with the SYN scheme and JTAG scheme to extract forensic data Improvement compared to the extraction is presented.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.209-212
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• 2003

In this paper, we proposed new speech feature parameter through parts-based feature extraction of speech spectrum using Non-Negative Matrix Factorization (NMF). NMF can effectively reduce dimension for multi-dimensional data through matrix factorization under the non-negativity constraints, and dimensionally reduced data should be presented parts-based features of input data. For speech feature extraction, we applied Mel-scaled filter bank outputs to inputs of NMF, than used outputs of NMF for inputs of speech recognizer. From recognition experiment result, we could confirm that proposed feature parameter is superior in recognition performance than mel frequency cepstral coefficient (MFCC) that is used generally.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.536-538
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• 2005

As multimedia data and huge-Quantity video data having been increasingly and commonly used, the key frame algorithm, as one of the methods for manipulating these kinds of data, became an important matter and has been studied for many years. But the formerly proposed key frame extraction methods take much processing time or need complex calculations due to decoding processes. In order to solve these problems which the former methods have and to enhance the key frame extraction efficiency, a novel key frame extraction method in compressed domain is proposed in this paper. In this method we get an edge histogram for each I-frame in DCT domain and then extract the key frames by means of histogram difference metric. Experimental results show that our algorithm achieves fast processing speed and high accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1475-1479
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• 2004

High dimensional data requires efficient feature extraction techliques. Though PCA(Principal Component Analysis) is a famous feature extraction method it requires huge memory space and computational cost is high. In this paper we use incremental PCA for feature extraction on high dimensional data. Through experiment we show that proposed method is superior to APEX model.

• ETRI Journal
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• v.33 no.5
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• pp.720-730
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• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.