• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• 디지털융복합연구
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• 제14권2호
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• pp.245-258
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• 2016

본 논문에서는 기계학습과 관련된 다양한 사례들에 대한 연구를 바탕으로 기계학습 응용 및 학습 알고리즘의 성능 개선 방안을 제시한다. 이를 위해 기계학습 기법을 적용하여 결과를 얻어낸 문헌을 자료로 수집하고 학문분야로 나누어 각 분야에서 적합한 기계학습 기법을 선택 및 추천하였다. 공학에서는 SVM, 의학에서는 의사결정나무, 그 외 분야에서는 SVM이 빈번한 이용 사례와 분류/예측의 측면에서 그 효용성을 보였다. 기계학습의 적용 사례분석을 통해 응용 방안의 일반적 특성화를 꾀할 수 있었다. 적용 단계는 크게 3단계로 이루어진다. 첫째, 데이터 수집, 둘째, 알고리즘을 통한 데이터 학습, 셋째, 알고리즘에 대한 유의미성 테스트 이며, 각 단계에서의 알고리즘의 결합을 통해 성능을 향상시킨다. 성능 개선 및 향상의 방법은 다중 기계학습 구조 모델링과 $+{\alpha}$ 기계학습 구조 모델링 등으로 분류한다.

• Smart Structures and Systems
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• 제24권6호
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.326-327
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• 2022

본 연구는 머신러닝을 이용한 임상적 의사결정을 위한 알고리즘으로 환자를 간호하는 간호인력이 장기간 누워있는 환자를 보살힐 경우 욕창예방간호 수행에 도움을 주기 위한 시스템 개발에 활용될 욕창 분류 알고리즘이다. 머신러닝을 실시한 결과 알고리즘의 learning accuracy는 82.14%, test accuracy는 82.58%로 나타났다.

• 한국지능시스템학회논문지
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• 제17권6호
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• pp.807-812
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• 2007

최근 단일 은닉층을 갖는 전방향 신경회로망 구조로, 기존의 경사 기반 학습알고리즘들보다 학습 속도가 매우 우수한 ELM(Extreme Learning Machine)이 제안되었다. ELM 알고리즘은 입력 가중치들과 은닉 바이어스들의 초기 값을 무작위로 선택하고 출력 가중치들은 Moore-Penrose(MP) 일반화된 역행렬 방법을 통하여 구해진다. 그러나 입력 가중치들과 은닉층 바이어스들의 초기 값 선택이 어렵다는 단점을 갖고 있다. 본 논문에서는 최적화 알고리즘 중 박테리아 생존(Bacterial Foraging) 알고리즘의 수정된 구조를 이용하여 ELM의 초기 입력 가중치들과 은닉층 바이어스들을 선택하는 개선된 방법을 제안하였다. 실험을 통하여 제안된 알고리즘이 많은 입력 데이터를 가지는 문제들에 대하여 성능이 우수함을 보였다.

• 한국지능시스템학회논문지
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• 제25권5호
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• pp.509-514
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• 2015

본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.69-73
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• 2017

본 논문에서는 기계학습 기반의 실시간 이미지 인식 알고리즘을 개발하고 개발한 알고리즘의 성능을 테스트 하였다. 실시간 이미지 인식 알고리즘은 기계 학습된 이미지 데이터를 바탕으로 실시간으로 입력되는 이미지를 인식한다. 개발한 실시간 이미지 인식 알고리즘의 성능을 테스트하기 위해 자율주행 자동차 분야에 적용해보았고 이를 통해 개발한 실시간 이미지 인식 알고리즘의 성능을 확인해보았다.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.16-30
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• 2023

The elderly population is increasing owing to a low fertility rate and an aging population. In addition, life expectancy is increasing, and the advancement of medicine has increased the importance of health to most people. Therefore, government and companies are developing and supporting smart healthcare, which is a health-related product or industry, and providing related services. Moreover, with the development of the Internet, many people are managing their health through online searches. The most convenient way to achieve such management is by consuming nutritional supplements or seasonal foods to prevent a nutrient deficiency. However, before implementing such methods, knowing the nutrient status of the individual is difficult, and even if a test method is developed, the cost of the test will be a burden. To solve this problem, we developed a questionnaire related to nutrient classification twice, based upon which an adaptive algorithm was designed. This algorithm was designed as a machine learning based algorithm for nutrient classification and its accuracy was much better than the other machine learning algorithm.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.169-180
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• 2021

Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.