• 한국IT서비스학회지
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• 제8권4호
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• pp.129-140
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• 2009

The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

• Journal of information and communication convergence engineering
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• 제14권1호
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• pp.45-50
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• 2016

Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• 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.

• 한국BIM학회 논문집
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• 제6권4호
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Industrial Engineering and Management Systems
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• 제14권3호
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• 전력전자학회논문지
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• 제26권4호
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• pp.241-248
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• 2021

A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.769-782
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• 2022

A study on agricultural automation became more important. In Korea, sweet persimmon farmers spend a lot of time and effort on classifying profitable persimmons. In this paper, we propose and implement an efficient grading algorithm for persimmons before shipment. We gathered more than 1,750 images of persimmons, and the images were graded and labeled for classifications purpose. Our main algorithm is based on EfficientDet object detection model but we implemented more exquisite method for better classification performance. In order to improve the precision of classification, we adopted a machine learning algorithm, which was proposed by PyCaret machine learning workflow generation library. Finally we acquired an improved classification model with the accuracy score of 81%.