• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.

• The Journal of Information Systems
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• v.33 no.1
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• pp.31-44
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• 2024

Purpose The purpose of this study is to use climate data to find the algorithm with the highest Hallabong production prediction ability and to predict future Hallabong production in areas where Hallabong cultivation is expected to be possible. Design/methodology/approach The research is conducted in two stages. In the first step, find the algorithm with the highest predictive power among XGBoost, Random Forest, SVM, and LSTM methodologies. In the second stage, the algorithm found in the first stage is applied to predict future Hallabong production in three regions where Hallabong production is expected to be possible. Findings As with many prediction studies, we found that XGBoost showed the highest prediction power. Even in areas where Hallabong production is expected to be possible, Hallabong production was predicted to be highest in Hongcheon, Gangwon-do, which has the highest latitude.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.465-470
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• 1998

A cross-flow fan often generates discrete noise call blade passing frequency tones. Several methods have been investigated to reduce this BPF noise, where the random distribution of blades is the most promising one. A simple and effective algorithm to determine a random distribution of blades is proposed which considers fan. performance as well as noise characteristics. The proposed method is verified by a simple numerical model and is applied in manufacturing cross-flow fan samples. Also some experiments are carried out and the experimental results are analyzed.

• Journal of Multimedia Information System
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• v.2 no.1
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• pp.179-186
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• 2015

This paper describes the application of a simulated annealing to a random signal-based learning. The simulated annealing is used to generate the reinforcement signal which is used in the random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural network. It is poor at hill-climbing, whereas simulated annealing has an ability of probabilistic hill-climbing. Therefore, hybridizing a random signal-based learning with the simulated annealing can produce better performance than before. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. And the other is the optimization of fuzzy control rules using inverted pendulum.

• ETRI Journal
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• v.35 no.6
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• pp.949-959
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• 2013

Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.4
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• pp.262-268
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• 2004

This thesis is proposing novel RPWM (Random PWM) techniques that can locate PWM pulse to do random. RPWM techniques to propose locates SVPWM (Space Vector PWM) pulse by number of each random and principle to locate of pulse applies different random function and locate pulse. For propriety verification of proposed techniques, achieve an simulation and experiment that use MATLAB/SIMULINK about proposed RPWM techniques algorithm and IGBT inverter composition that use DSP(TMS320C31). Specially, analyze harmonic spectra of inverter output current when the induction motor speed is more than 10,000 rpm, confirm that RPWM's effect in high speed degree appears. Proposed RPWM techniques propriety prove from reduction effect of harmonic magnitude that corresponds to an integer times of switching frequency.

• The KIPS Transactions:PartC
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• v.18C no.1
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• pp.7-14
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• 2011

Power analysis attack on low-power consumed security devices such as smart cards is very powerful, but it is required that the correlation between the measured power signal and the mid-term estimated signal should be consistent in a time instant while running encryption algorithm. The power signals measured from the security device applying the random clock do not match the timing point of analysis, therefore random clock is used as counter measures against power analysis attacks. This paper propose a new constant pitch based time alignment for power analysis with random clock power trace. The proposed method neutralize the effects of random clock used to counter measure by aligning the irregular power signals with the time location and size using the constant pitch. Finally, we apply the proposed one to AES algorithm within randomly clocked environments to evaluate our method.

• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.451-459
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• 2016

While an object detection algorithm plays a key role in many computer vision applications, it requires extensive computation to show robustness under varying lightning and geometrical distortions. Recently, some approaches formulate the problem in a classification framework and show improved performances in object recognition. Among them, random fern algorithm drew a lot of attention because of its simple structure and high recognition rates. However, it reveals performance degradation under the illumination changes and noise addition, since it computes patch features based only on pixel intensities. In this paper, we propose a new structure of combined random fern which incorporates depth information into the conventional random fern reflecting 3D structure of the patch. In addition, a new structure of object tracker which exploits the combined random fern is also introduced. Experiments show that the proposed method provides superior performance of object detection under illumination change and noisy condition compared to the conventional methods.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.125-140
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• 2005

This study suggests a suitable random transverse tining for reduction tire/road noise from concrete pavement. Through literature reviews, random transverse tining that can disperse the energy concentrated to the specific frequency was suggested using the LCG(linear congruential generators) algorithm. The spacing of tining from this study is applied to Daegu-Pohang express highway. For the purpose oi comparison, two other random tining sections were included that are research products from Chung-Ang university and Wisconsin DOT. In result of pass-by noise measurement by car, though designed section is superior to the others as noise reduction by reducing pitch noise, the effectiveness is not large. In case of traffic noise measurement, lower noise was observed at random transverse tining sections than uniformly transverse tining section, too. But there are seine differences between pass-by noise and traffic noise.