• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.87-95
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• 2024

This study is on modulo scheduling algorithms for multicore processor in machine learning applications. Machine learning algorithms are designed to perform a large amount of operations such as vectors and matrices in order to quickly process large amounts of data stream. To support such large amounts of computations, processor architectures to support applications such as artificial intelligence, neural networks, and machine learning are designed in the form of parallel processing such as multicore. To effectively utilize these multi-core hardware resources, various compiler techniques are being used and studied. In this study, among these compiler techniques, we analyzed the modular scheduler, which is especially important in one core's computation pipeline. This paper looked at and compared the iterative modular scheduler and the swing modular scheduler, which are the most widely used and studied. As a result, both schedulers provided similar performance results, and when measuring register pressure as an indicator, it was confirmed that the swing modulo scheduler provided slightly better performance. In this study, a technique that divides recurrence edge is proposed to improve the minimum initiation interval of the modulo schedulers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.125-135
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• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

• Journal of Ocean Engineering and Technology
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• v.34 no.2
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• pp.147-154
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• 2020

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.476-482
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• 2020

In this paper, we propose a block-based modularity architecture design method for distributed machine learning. The proposed architecture is a block-type module structure with various machine learning algorithms. It allows free expansion between block-type modules and allows multiple machine learning algorithms to be organically interlocked according to the situation. The architecture enables open data communication using the metadata query protocol. Also, the architecture makes it easy to implement an application service combining various edge computing devices by designing a communication method suitable for surrounding applications. To confirm the interlocking between the proposed block-type modules, we implemented a hardware-based modularity application system.

• Journal of IKEEE
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• v.22 no.4
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• pp.948-952
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• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.111-117
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• 2020

Recently, with the high interest of machine learning, the need for educational controllers to interface with physical devices has increased. However, existing controllers are limited in terms of high cost and area of utilization for educational purposes. In this paper, motion control controllers using brain waves are proposed for the purpose of students' machine learning applications. The brain motion that occurs when imagining a specific action is measured and sampled, then the sample values were learned through Tensor Flow and the motion was recognized in contents such as games. Movement variation for motion recognition consists of directionality and jump motion. The identification of the recognition behavior is sent to a game produced by an Unreal Engine to operate the character in the game. In addition to brain waves, the implemented controller can be used in various fields depending on the input signal and can be used for educational purposes such as machine learning applications.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.245-258
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• 2016

This paper aims to present the way to bring about significant results through performance improvement of learning algorithm in the research applying to machine learning. Research papers showing the results from machine learning methods were collected as data for this case study. In addition, suitable machine learning methods for each field were selected and suggested in this paper. As a result, SVM for engineering, decision-making tree algorithm for medical science, and SVM for other fields showed their efficiency in terms of their frequent use cases and classification/prediction. By analyzing cases of machine learning application, general characterization of application plans is drawn. Machine learning application has three steps: (1) data collection; (2) data learning through algorithm; and (3) significance test on algorithm. Performance is improved in each step by combining algorithm. Ways of performance improvement are classified as multiple machine learning structure modeling, $+{\alpha}$ machine learning structure modeling, and so forth.