• Journal of Periodontal and Implant Science
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• 제48권2호
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• 전기전자학회논문지
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• 제21권2호
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• pp.136-141
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• 2017

본 논문에서는 딥러닝을 PC에 적용하기 위한 메모리 최적화에 관한 알고리즘을 제안한다. 제안된 알고리즘은 일반 PC에서 기존의 딥러닝 구조에서 요구되는 연산처리 과정과 데이터 량을 감소시켜 메모리 및 연산처리 시간을 최소화한다. 본 논문에서 제안하는 알고리즘은 분별력이 있는 랜덤 필터를 이용한 컨볼루션 층 구성 과정, PCA를 이용한 데이터 축소 과정, SVM을 사용한 CNN 구조 생성 등의 3과정으로 이루어진다. 분별력이 있는 랜덤 필터를 이용한 컨볼루션 층 구성 과정에서는 학습과정이 필요치 않아서 전체적인 딥러닝의 학습시간을 단축시킨다. PCA를 이용한 데이터 축소 과정에서는 메모리량과 연산처리량을 감소시킨다. SVM을 사용한 CNN 구조 생성에서는 필요로 하는 메모리량과 연산 처리량의 감소 효과를 극대화 시킨다. 제안된 알고리즘의 성능을 평가하기 위하여 예일 대학교의 Extended Yale B 얼굴 데이터베이스를 사용하여 실험한 결과, 본 논문에서 제안하는 알고리즘이 기존의 CNN 알고리즘과 비교하여 비슷한 성능의 인식률을 보이면서 연산 소요시간과 메모리 점유율에 있어 우수함이 확인되었다. 본 논문에서 제안한 알고리즘을 바탕으로 하여 일반 PC에서도 많은 데이터와 연산처리를 가진 딥러닝 알고리즘을 구현할 수 있으리라 기대된다.

• International Journal of Internet, Broadcasting and Communication
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• 제10권4호
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• pp.50-64
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• 2018

This paper describes a visual object detection approach utilizing ensemble based machine learning. Object detection methods employing 1D features have the benefit of fast calculation speed. However, for real image with complex background, detection accuracy and performance are degraded. In this paper, we propose an ensemble learning algorithm that combines a 1D feature classifier and 2D DNF (Disjunctive Normal Form) classifier to improve the object detection performance in a single input image. Also, to improve the computing efficiency and accuracy, we propose a feature selecting method to reduce the computing time and ensemble algorithm by combining the 1D features and 2D DNF features. In the verification experiments, we selected the Haar-like feature as the 1D image descriptor, and demonstrated the performance of the algorithm on a few datasets such as face and vehicle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.608-627
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• 2016

Wireless mesh networks (WMNs) based on IEEE 802.11s have emerged as one of the prominent technologies in multi-hop communications. However, the deployment of WMNs suffers from serious interference problem which severely limits the system capacity. Using multiple radios for each mesh router over multiple channels, the interference can be reduced and improve system capacity. Nevertheless, interference cannot be completely eliminated due to the limited number of available channels. An effective approach to mitigate interference is to apply dynamic channel switching (DCS) in WMNs. Conventional DCS schemes trigger channel switching if interference is detected or exceeds a predefined threshold which might cause unnecessary channel switching and long protocol overheads. In this paper, a P-learning based dynamic switching algorithm known as learning automaton (LA)-based DCS algorithm is proposed. Initially, an optimal channel for communicating node pairs is determined through the learning process. Then, a novel switching metric is introduced in our LA-based DCS algorithm to avoid unnecessary initialization of channel switching. Hence, the proposed LA-based DCS algorithm enables each pair of communicating mesh nodes to communicate over the least loaded channels and consequently improve network performance.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.203-211
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• 2020

This study proposes a method for estimating gender and age that is robust to various external environment changes by applying deep learning-based learning. To improve the accuracy of the proposed algorithm, an improved CNN network structure and learning method are described, and the performance of the algorithm is also evaluated. In this study, in order to improve the learning method based on CNN composed of 6 layers of hidden layers, a network using GoogLeNet's inception module was constructed. As a result of the experiment, the age estimation accuracy of 5,328 images for the performance test of the age estimation method is about 85%, and the gender estimation accuracy is about 98%. It is expected that real-time age recognition will be possible beyond feature extraction of face images if studies on the construction of a larger data set, pre-processing methods, and various network structures and activation functions have been made to classify the age classes that are further subdivided according to age.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.209-214
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• 2016

The purpose of this study was to classify healty persons and Parkinson disease patients from the vocal characteristics of healty persons and the of Parkinson disease patients using Machine Learning algorithms. So, we compared the most widely used algorithms for Machine Learning such as J48 algorithm and REPTree algorithm. In order to evaluate the classification performance of the two algorithms, the results were compared with depending on vocal characteristics. The classification performance of depending on vocal characteristics show 88.72% and 84.62%. The test results showed that the J48 algorithms was superior to REPTree algorithms.

• Journal of Information Processing Systems
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• 제5권2호
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• pp.105-116
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• 2009

To solve the general problems surrounding the application of genetic algorithms in stereo matching, two measures are proposed. Firstly, the strategy of simplified population-based incremental learning (PBIL) is adopted to reduce the problems with memory consumption and search inefficiency, and a scheme for controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities. In addition, an alternative version of the proposed algorithm, without the use of a probability vector, is also presented for simpler set-ups. Secondly, programmable graphics-hardware (GPU) consists of multiple multi-processors and has a powerful parallelism which can perform operations in parallel at low cost. Therefore, in order to decrease the running time further, a model of the proposed algorithm, which can be run on programmable graphics-hardware (GPU), is presented for the first time. The algorithms are implemented on the CPU as well as on the GPU and are evaluated by experiments. The experimental results show that the proposed algorithm offers better performance than traditional BMA methods with a deliberate relaxation and its modified version in terms of both running speed and stability. The comparison of computation times for the algorithm both on the GPU and the CPU shows that the former has more speed-up than the latter, the bigger the image size is.

• 대한전자공학회논문지TE
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• 제39권2호
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• pp.66-75
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• 2002

본 논문에서는 역전파 신경회로망의 학습능률을 향상시키기 위한 방법으로 발생한 오차에 따라서 학습파라미터와 은닉층의 수를 적응적으로 변경시킬 수 있는 적응 역 전파 학습알고리즘을 제안하였다. 제안한 알고리즘은 역전파 신경회로망이 국소점으로 수렴하는 문제를 해결할 수 있고 최적의 수렴환경을 만들 수 있다. 제안된 알고리즘을 평가하기 위하여 배타적 논리합, 3-패리티 및 7${\times}$5 영문자 폰트의 학습을 이용하였다. 실험결과, 기존에 제안된 알고리즘들에 비하여 국소점에 빠지게 되는 경우가 감소하였고 약 17.6%~64.7%정도 학습능률이 향상하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.503-506
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• 2005

In this study, optimization design technique for control of solid-fluid coupled force (sloshing) using evolutionary method is suggested. Artificial neural networks(ANN) and genetic algorithm(GA) is employed as evolutionary optimization method. The ANN is used to analysis of the sloshing and the genetic algorithm is adopted as an optimization algorithm. In the creation of ANN learning data, the design of experiments is adopted to higher performance of the ANN learning using minimum learning data and ALE(Arbitrary Lagrangian Eulerian) numerical method is used to obtain the sloshing analysis results. The proposed optimization technique is applied to the minimization of sloshing of the water in the tank lorry with baffles under 2 second lane change.