• Journal of Information Processing Systems
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• v.16 no.2
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• pp.239-245
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• 2020

Blockchain, machine learning, and big data are among the key components of the future IT track. These technologies are used in various fields; hence their increasing application. This paper discusses the technologies developed in various research fields, such as data representation, Blockchain application, 3D shape recognition and classification, query method, classification method, and search algorithm, to provide insights into the future paradigm. In this paper, we present a summary of 18 high-quality accepted articles following a rigorous review process in the fields of Blockchain, machine learning, and big data.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.75-78
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• 1997

In this paper we present new sound spectral analysis methods or prosthetic heart valve sounds. Phonocardiograms(PCG) of prosthetic heart valve were analyzed in order to derive frequency domain feature suitable or the classification of the valve state. The fast orthogonal search method and MUSIC (MUltiple SIgnal Classification) method are described or finding the significant frequencies in PCG. The fast orthogonal search method is effective with short data records and cope with noisy, missing and unequally-spaced data. MUSIC method's key to the performance is the division of the information in the autocorrelation matrix or the data matrix into two vector subspaces, one a signal subspace and the other a noise subspace.

• Journal of Microbiology
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• v.34 no.2
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• pp.184-191
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• 1996

The cellular fatty acids and quinones of streptomycetes isolated from volcanic soils were analysed. The strains contained fatty acids of 14 to 17 carbon chains, and 12-methyltetradecanoic acid and 14 methylpentadecanoic acid were dominant in most strains. The total profiles consisted of 74% branched fatty acid family, 16.8% linear family and 8.2% unsaturated family. The largest cluster of grey spore meases defined by numerical classification was separated from the remainders in the principal component analysis, but the other clusters were overlapped with one another. In the analysis of respiratory quinones, all of the strains contained either the menaquinone of 9 isoprene units with 6 hydrogenations of 8 hydrogenations as the major species. The distribution of menaquinones among the clusters could provide an important key in the chemotaxonomy of streptomycetes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1463-1480
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• 2014

It is well known that sparse code is effective for feature extraction of face recognition, especially sparse mode can be learned in the kernel space, and obtain better performance. Some recent algorithms made use of single kernel in the sparse mode, but this didn't make full use of the kernel information. The key issue is how to select the suitable kernel weights, and combine the selected kernels. In this paper, we propose a novel multiple kernel sparse representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. Initially, several possible kernels are combined and the sparse coefficient is computed, then the kernel weights can be obtained by the sparse coefficient. Finally convergence makes the kernel weights optimal. The experiments results show that our algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithms.

• Genomics & Informatics
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• v.3 no.2
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• pp.55-60
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• 2005

Peroxiredoxins (Prx's) are a superfamily of peroxidases that are ubiquitous in all super-kingdoms. Previous biochemical and structural studies have suggested that Prx's could be divided into five subfamilies (1-Cys, Typical 2-Cys, Atypical 2-Cys C-, L- and R- types). In this work, we have developed a set of regular expression patterns describing subfamily-specific spatial constraints of the key catalytic residues. Using these patterns, 1,016 Prx's available in public databases were classified into the five subfamilies. Our method performed well for most of the types except for Atypical 2 Cys R type.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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• pp.93-97
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• 2010

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

• The Journal of Information Technology and Database
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• v.7 no.1
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• pp.67-82
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• 2000

In this paper we have developed a message transfer protocol, CMP, which improves MSP's message processing capability. The proposed method has taken into account document classification to improve the efficiency of message processing. The difference between the conventional MSP and CMP has been addressed. The CMP's performance has been shown by various experiments including number, alphabet, Korean letter, Chinese letter, music sound and compression file transmission. And security capability of both protocols has been compared based on the specification of FIPS 140-2. The CMP's overall performance is shown to be superior to that of MSP on the processing speed in the performance perspective and on the function of cryptographic module interface and cryptographic key management in the security perspective respectively.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.11-18
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• 2019

One of the key objectives of control systems is to maintain a system in a specific stable state. To achieve this goal, a variety of control techniques can be used and it is often uses a feedback control method. As known this kind of control methods requires mathematical model of the system. This article presents seesaw unstable system with two propellers which are controlled without use of a mathematical model instead. The goal was to control it using training data. For system control we use a logistic regression technique which is one of machine learning method. We tested our controller on the real model created in our laboratory and the experimental results show that instability of the seesaw system can be fixed at a given angle using the decision boundary estimated from the classification method. The results show that this control method for structural equilibrium can be used with relatively more accuracy of the decision boundary.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.474-475
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• 2013

In healthcare systems, the accuracy of a classifier for classifying medical diseases depends on a reduced dataset. Key to achieve true classification results is the reduction of data to a set of optimal number of significant features. The initial step towards data reduction is the integration of heterogeneous data sources to a unified reduced dataset which is further reduced by considering the range of values of all the attributes and then finally filtering and dropping out the least significant features from the dataset. This paper proposes a three step data reduction model which plays a vital role in the classification process.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.207-212
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• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.