• Title/Summary/Keyword: online machine learning model

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Predicting Students' Engagement in Online Courses Using Machine Learning

  • Alsirhani, Jawaher;Alsalem, Khalaf
    • International Journal of Computer Science & Network Security
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    • v.22 no.9
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    • pp.159-168
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    • 2022
  • No one denies the importance of online courses, which provide a very important alternative, especially for students who have jobs that prevent them from attending face-to-face in traditional classes; Engagement is one of the most important fundamental variables that indicate the course's success in achieving its objectives. Therefore, the current study aims to build a model using machine learning to predict student engagement in online courses. An online questionnaire was prepared and applied to the students of Jouf University in the Kingdom of Saudi Arabia, and data was obtained from the input variables in the questionnaire, which are: specialization, gender, academic year, skills, emotional aspects, participation, performance, and engagement in the online course as a dependent variable. Multiple regression was used to analyze the data using SPSS. Kegel was used to build the model as a machine learning technique. The results indicated that there is a positive correlation between the four variables (skills, emotional aspects, participation, and performance) and engagement in online courses. The model accuracy was very high 99.99%, This shows the model's ability to predict engagement in the light of the input variables.

A Study on the Prediction Models of Used Car Prices for Domestic Brands Using Machine Learning (머신러닝을 활용한 브랜드별 국내 중고차 가격 예측 모델에 관한 연구)

  • Seungjun Yim;Joungho Lee;Choonho Ryu
    • Journal of Service Research and Studies
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    • v.13 no.3
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    • pp.105-126
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    • 2023
  • The domestic used car market continues to grow along with the used car online platform service. The used car online platform service discloses vehicle specifications, accident history, inspection history, and detailed options to service consumers. Most of the preceding studies were predictions of used car prices using vehicle specifications and some options for vehicles. As a result of the study, it was confirmed that there was a nonlinear relationship between used car prices and some specification variables. Accordingly, the researchers tried to solve the nonlinear problem by executing a Machine Learning model. In common, the Regression based Machine Learning model had the advantage of knowing the actual influence and direction of variables, but there was a disadvantage of low Cost Function figures compared to the Decision Tree based Machine Learning model. This study attempted to predict used car prices of six domestic brands by utilizing both vehicle specifications and vehicle options. Through this, we tried to collect the advantages of the two types of Machine Learning models. To this end, we sequentially conducted a regression based Machine Learning model and a decision tree based Machine Learning model. As a result of the analysis, the practical influence and direction of each brand variable, and the best tree based Machine Learning model were selected. The implications of this study are as follows. It will help buyers and sellers who use used car online platform services to predict approximate used car prices. And it is hoped that it will help solve the problem caused by information inequality among users of the used car online platform service.

A Machine Learning-based Customer Classification Model for Effective Online Free Sample Promotions (온라인 무료 샘플 판촉의 효과적 활용을 위한 기계학습 기반 고객분류예측 모형)

  • Won, Ha-Ram;Kim, Moo-Jeon;Ahn, Hyunchul
    • The Journal of Information Systems
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    • v.27 no.3
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    • pp.63-80
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    • 2018
  • Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

A Case Study on the Application of Plant Classification Learning for 4th Grade Elementary School Using Machine Learning in Online Learning (온라인 학습에서 머신러닝을 활용한 초등 4학년 식물 분류 학습의 적용 사례 연구)

  • Shin, Won-Sub;Shin, Dong-Hoon
    • Journal of Korean Elementary Science Education
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    • v.40 no.1
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    • pp.66-80
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    • 2021
  • This study is a case study that applies plant classification learning using machine learning to fourth graders in elementary school in online learning situations. In this study, a plant classification learning education program associated with 2015 revision science curriculum was developed by applying the Artificial Intelligence biological classification teaching Learning model. The study participants were 31 fourth graders who agreed to participate voluntarily. Plant classification learning using machine learning was applied six hours for three weeks. The results of this study are as follows. First, as a result of image analysis on artificial intelligence, participants were mainly aware of artificial intelligence as mechanical (27%), human (23%) and household goods (23%). Second, an artificial intelligence recognition survey by semantic discrimination found that artificial intelligence was recognized as smart, good, accurate, new, interesting, necessary, and diverse. Third, there was a difference between men and women in perception and emotion of artificial intelligence, and there was no difference in perception of the ability of artificial intelligence. Fourth, plant classification learning using machine learning in this study influenced changes in artificial intelligence perception. Fifth, plant classification learning using machine learning in this study had a positive effect on reasoning ability.

Machine Learning Approach for Prediction of VOD Usage (머신러닝을 활용한 VOD 이용건수 예측)

  • Jeon, Jong Seok;Jang, Ha Eun;Oh, Joo Hee
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.5
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    • pp.507-513
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    • 2022
  • This study developed a model for predicting the number of VOD uses of IPTV, an online market in the film industry. A machine learning-based prediction model was established using the VOD usage data collected by the Korean Film Council from 2017 to 2021. Through literature research and cluster analysis, the difference between the offline market and the online market is revealed, and a new category of VOD usage is proposed. The purpose is to help IPTV companies establish marketing strategies as well as support decision-making by developing a machine learning-based VOD usage prediction model.

A Box Office Type Classification and Prediction Model Based on Automated Machine Learning for Maximizing the Commercial Success of the Korean Film Industry (한국 영화의 산업의 흥행 극대화를 위한 AutoML 기반의 박스오피스 유형 분류 및 예측 모델)

  • Subeen Leem;Jihoon Moon;Seungmin Rho
    • Journal of Platform Technology
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    • v.11 no.3
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    • pp.45-55
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    • 2023
  • This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

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Development of Online Machine Learning Model for AHU Supply Air Temperature Prediction using Progressive Sampling and Normalized Mutual Information (점진적 샘플링과 정규 상호정보량을 이용한 온라인 기계학습 공조기 급기온도 예측 모델 개발)

  • Chu, Han-Gyeong;Shin, Han-Sol;Ahn, Ki-Uhn;Ra, Seon-Jung;Park, Cheol Soo
    • Journal of the Architectural Institute of Korea Structure & Construction
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    • v.34 no.6
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    • pp.63-69
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    • 2018
  • The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).

Online Selective-Sample Learning of Hidden Markov Models for Sequence Classification

  • Kim, Minyoung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.3
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    • pp.145-152
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    • 2015
  • We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

Online abnormal events detection with online support vector machine (온라인 서포트벡터기계를 이용한 온라인 비정상 사건 탐지)

  • Park, Hye-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.2
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    • pp.197-206
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    • 2011
  • The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

Heart Attack Prediction using Neural Network and Different Online Learning Methods

  • Antar, Rayana Khaled;ALotaibi, Shouq Talal;AlGhamdi, Manal
    • International Journal of Computer Science & Network Security
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    • v.21 no.6
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    • pp.77-88
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    • 2021
  • Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.