• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.34-37
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• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.497-499
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• 1998

비젼 시스템을 바탕으로 한 무인 주행 시스템은 카메라로부터 입력된 영사에서 도로와 비 도로를 적절히 인식하여 그것을 바탕으로 주행을 위한 여러 장치들을 제어하는 시스템이라 할 수 있다. 한편 이와 같이 영상의 인식 결과가 핸들 제어나 속도 제어의 성능을 결정할 때 무엇보다 도로의 환경 변화에 강건한 비젼 시스템의 구현이 요구된다. 본 논문에서는 비젼 시스템과 핸들 제어 시스템 두 부분을 구현하였는데, 비젼 시스템에서는 입력 영상에 대해 학습이 가능한 Multilayer Perceptron(MLP)을 이용하여 도로와 비 도로를 적절한 신뢰도로 나눈 후 피라미드 알고리즘을 거쳐 최종 도로 영역을 추출해 낸다. 핸들 제어를 위해 도로 영역의 외곽선을 모델링한 후 차량의 주행 방향 벡터를 구한다. 그 값이 핸들 제어 시스템에서의 MLP의 입력이 되어 차량의 핸들 각도를 결정하게 된다. 끝으로 옥외 차량 시뮬레이션을 통하여 본 논문에서 제안된 알고리즘의 유용성을 확인한다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.507-509
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• 2012

심전도 질환 데이터는 일반적으로 분류기를 사용한 실험이 많다. 심전도 신호는 QRS-Complex와 R-R interval을 추출하는 경우가 많은데 본 실험에서는 R-R interval을 추출하여 실험하였다. 심전도 데이터의 분류 실험은 일반적으로 SVM(Support Vector Machine)과 MLP(Multilayer Perceptron)으로 실험되지만 본 실험은 Decision Tree를 사용하여 정확도 향상을 추구하였다. 그리고 정확도 비교 분석을 위해 SVM과 MLP 분류기 실험을 같이 수행하였고, 동일한 데이터와 간격으로 실험한 타 논문의 결과와 비교해 보았다. Decision Tree를 다른 분류기와 타 논문의 결과와 비교해 보니 정확도 부분에서는 Decision Tree가 가장 우수하였다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.239-240
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• 2006

In this study, we examined all peaks of confocal Raman spectra as peaks are the most important features for discrimination between basal cell carcinoma (BCC) and normal tissue (NOR). 14 peaks were extracted from these peaks using decision tree. For dimension reduction, frequently selected 4 peaks were chosen. They are located at 1014, 1095, 1439, $1523cm^{-1}$. These peaks were used as an input feature of the multilayer perceptron networks (MLP). According to the experimental results, MLP gave classification error rate of about 6.5%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.67-70
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• 1998

There were many cases to apply artificial intelligence to medicine. In this paper, we present the prediction model of the development of the NIDDM(noninsulin-dependent diabetes mellitus). It is not difficult that doctor diagnose patient as DM(diabetes mellitus). However NIDDM is usually developmented later on 40 years old and symptom appeares gradually. So screening test or prediction model is needed absolutely. Our model predicts development of NIDDM with still normal data 2 year ago. Prediction models developed are both MLP(multilayer perceptron) with backpropagation training and RBFN(radial basis function network). Performance of both models were evaluated with likelihood ratio. MLP was about two and RBFN was about three. We expect that models developed can prevent development of DM and utilize normal data.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.360-366
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• 2003

Although multilayer perceptrons (MLPs) present several advantages against other pattern recognition methods, MLP-based speaker verification systems suffer from slow enrollment speed caused by many background speakers to achieve a low verification error. To solve this problem, the quantitative discriminative cohort speakers (QnDCS) method, by introducing the cohort speakers method into the systems, reduced the number of background speakers required to enroll speakers. Although the QnDCS achieved the goal to some extent, the improvement rate for the enrolling speed was still unsatisfactory. To improve the enrolling speed, this paper proposes the qualitative DCS (QlDCS) by introducing a qualitative criterion to select less background speakers. An experiment for both methods is conducted to use the speaker verification system based on MLPs and continuants, and speech database. The results of the experiment show that the proposed QlDCS method enrolls speakers in two times shorter time than the QnDCS does over the online error backpropagation(EBP) method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.64-70
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• 2007

Raman spectroscopy is known to have strong potential for providing noninvasive dermatological diagnosis of skin cancer. According to the previous work, various well known methods including maximum a posteriori probability (MAP) and multilayer perceptron networks (MLP) showed competitive results. Since even the small errors often leads to a fatal result, we investigated the method that reduces classification error perfectly by screening out some ambiguous patterns. Those ambiguous patterns can be examined by routine biopsy. We incorporated an ambiguous pattern class in MAP, linear classifier using minimum squared error (MSE), MLP and reduced coulomb energy networks (RCE). The experiments involving 216 confocal Raman spectra showed that every methods could perfectly classify BCC by screening out some ambiguous patterns. The best results were obtained with MSE. According to the experimental results, MSE gives perfect classification by screening out 8.8% of test patterns.

• Journal of the Korea Computer Industry Society
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• v.3 no.5
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• pp.547-556
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• 2002

This paper was proposed a wave path loss prediction algorithm using multilayer perceptron (MLP) model and wave propagation characteristic parameters for Wireless LAN in indoor radio environments. Receiving power was predicted by calculating indoor path loss in a Wireless LAN that has transmission power of 100mW and frequency of 2.4GHz, and was compared with measured. In the result of measurement shows that there is a difference between predicted and measured receiving power which can be reduced by an accurate analysis of the various path loss factors. In order to fix the access point(AP) positions was used the proposed a wave path loss prediction algorithm, and designed the optimum cell for Wireless LAN.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.103-111
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• 2021

Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.