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Predicting the use of chatbot systems in education: a comparative approach using PLS-SEM and machine learning algorithms

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CURRENT PSYCHOLOGY 2024年第28期43卷 23656-23674页

作者： Durak, Hatice Yildiz Onan, Aytug Necmettin Erbakan Univ Eregli Fac Educ Dept Educ Sci Konya Turkiye Izmir Katip Celebi Univ Dept Comp Engn Izmir Turkiye

Adopting innovations in educational practice is a challenging task. In order to promote the use of technological innovations, acceptance of the technology by potential users is a prerequisite. Indeed, understanding the various factors that influence technology acceptance is critical for technology acceptance research. The use and acceptance of chatbots in education as a technological innovation is a topic that needs to be investigated. Chatbots, which offer close to human interaction between the user and technology through text and voice, can provide significant benefits in educational environments. The UTAUT2 model (extending UTAUT), which is widely used to evaluate technology acceptance, can serve as a framework for evaluating the acceptance and use of chatbots. This study aims to predict factors influencing students' use of chatbots in education within the UTAUT2 framework. PLS-SEM and machine learning tested the model, involving 926 students. According to the findings of the study, behavioral intentions were influenced by various factors including performance expectations and attitudes. Facilitating conditions and intentions significantly impacted chatbot usage time. Moderator effects were observed with age, gender, and usage experience affecting behavioral intentions. Support vector machine and logistic regression showed high prediction accuracies for behavioral intentions and usage time, respectively. These results provide insights for chatbot designers to meet user needs in educational settings.

关键词： Chatbot technology UTAUT2 Educational chatbot usage Usage intensity PLS-SEM machine learning algorithms

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Exploring machine learning algorithms and Numerical Representations Strategies to Develop Sequence-Based Predictive Models for Protein Networks 23rd

Exploring Machine Learning Algorithms and Numerical Represen...

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23rd International Conference on Computational Science and its Applications (ICCSA)

作者： Medina-Ortiz, David Salinas, Pedro Cabas-Moras, Gabriel Duran-Verdugo, Fabio Olivera-Nappa, Alvaro Uribe-Paredes, Roberto Univ Magallanes Dept Ingn Computac Ave Pdte Manuel Bulnes Punta Arenas 01855 Chile Univ Talca Escuela Ingn Bioinformat Ave Lircay S-N Talca Chile Univ Chile Dept Ingn Quim Biotecnol & Mat Beauche 851 Santiago Chile

ISBN: (纸本)9783031368042;9783031368059

Predicting the affinity between two proteins is one of the most relevant challenges in bioinformatics and one of the most useful for biotechnological and pharmaceutical applications. Current prediction methods use the structural information of the interaction complexes. However, predicting the structure of proteins requires enormous computational costs. machine learning methods emerge as an alternative to this bioinformatics challenge. There are predictive methods for protein affinity based on structural information. However, for linear information, there are no development guidelines for elaborating predictive models, being necessary to explore several alternatives for processing and developing predictive models. This work explores different options for building predictive protein interaction models via deep learning architectures and classical machine learning algorithms, evaluating numerical representation methods and transformation techniques to represent structural complexes using linear information. Six types of predictive tasks related to the affinity and mutational variant evaluations and their effect on the interaction complex were explored. We show that classical machine learning and convolutional network-based methods perform better than graph convolutional network methods for studying mutational variants. In contrast, graph-based methods perform better on affinity problems or association constants, using only the linear information of the protein sequences. Finally, we show an illustrative use case, expose how to use the developed models, discuss the limitations of the explored methods and comment on future development strategies for improving the studied processes.

关键词： Protein networks machine learning algorithms protein language models protein discovery deep learning architectures

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Demand Forecasting for Textile Products Using Statistical Analysis and machine learning algorithms 13th

Demand Forecasting for Textile Products Using Statistical An...

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13th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Lorente-Leyva, Eandro L. Alemany, M. M. E. Peluffo-Ordonez, Diego H. Araujo, Roberth A. Univ Politecn Valencia Camino Vera S-N Valencia 46022 Spain SDAS Researh Grp Ibarra Ecuador Mohammed VI Polytech Univ Modeling Simulat & Data Anal MSDA Res Program Ben Guerir Morocco North Tech Univ Ibarra Ecuador

ISBN: (纸本)9783030732790;9783030732806

The generation of an accurate forecast model to estimate the future demand for textile products that favor decision-making around an organization's key processes is very important. The minimization of the model's uncertainty allows the generation of reliable results, which prevent the textile industry's economic commitment and improve the strategies adopted around production planning and decision making. That is why this work is focused on the demand forecasting for textile products through the application of artificial neural networks, from a statistical analysis of the time series and disaggregation in different time horizons through temporal hierarchies, to develop a more accurate forecast. With the results achieved, a comparison is made with statistical methods and machine learning algorithms, providing an environment where there is an adequate development of demand forecasting, improving accuracy and performance. Where all the variables that affect the productive environment of this sector under study are considered. Finally, as a result of the analysis, multilayer perceptron achieved better performance compared to conventional and machine learning algorithms. Featuring the best behavior and accuracy in demand forecasting of the analyzed textile products.

关键词： Demand forecasting Textile products Statistical analysis machine learning algorithms Artificial neural networks Temporal hierarchies

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Comparison of machine learning algorithms for Position-Oriented Human Fall Detection 19

Comparison of Machine Learning Algorithms for Position-Orien...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Salem, Ziad Lichtenegger, Felix Weiss, Andreas Peter Leiner, Claude Sommer, Christian Krutzler, Christian Joanneum Res Forschungsgesellschaft MbH Inst Sensors Photon & Mfg Technol Joanneum Res A-7423 Pinkafeld Austria Joanneum Res Forschungsgesellschaft MbH Inst Sensors Photon & Mfg Technol Joanneum Res A-8160 Weiz Austria

ISBN: (纸本)9798350333398

Smart sensor systems are increasingly pervading all kind of application fields such as in industry, ambient assisted living, or lifestyle accessories. In this work, a smart system for position-oriented human fall detection is investigated using various machine-learning algorithms for data processing and evaluation. Data from an inertial measurement unit is combined with data from visible light positioning methods to achieve position-based fall detection. Furthermore, an experimental setup and test methods were created to generate appropriate datasets for this analysis. The classification accuracy is compared with three machine-learning algorithms commonly used for such tasks, which are Decision Tree, Naive Bayes and Support Vector machine. It is demonstrated that the combination of data from the two sensor systems can improve the recognition accuracy beyond 99% in the best case.

关键词： machine learning algorithms inertial measurement unit visible light positioning sensor fusion human activity recognition fall detection

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Application of machine learning algorithms for Patient Length of Stay Prediction in Emergency Department During Hajj

Application of Machine Learning Algorithms for Patient Lengt...

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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Hijry, Hassan Olawoyin, Richard Oakland Univ Ind & Syst Engn Dept Rochester MI 48063 USA

ISBN: (纸本)9781728162867

Hospital emergency departments (EDs) in vital locations face high patient demand during peak events such as the annual Islamic pilgrimage (the Hajj event) in Mecca, Saudi Arabia, the New Year celebration ceremony in New York, and the World Cup, etc. Variable patient arrival rates and hospital conditions, particularly the availability of beds for inpatients, impacts long waiting times and length of stay (LOS), causing pain and dissatisfaction to patients. Patient length of stay is chosen to be a measure of ED overcrowding as a compliance measure set by most hospitals. Clinicians need to get an opportunity to be proactive in ED overcrowding crises, specifically in the case of peak days. For this purpose, the research aims to build a model to forecast Hajj patient LOS, using machine learning algorithms through predictive input factors such as patient age, mode of arrival, and patient's type of condition in the ED. Therefore, using machine learning algorithms, such as artificial neural networks, linear and logistic regressions, to forecast ED LOS allows clinicians to prepare for high levels of congestion and provide insights to determine the LOS of patients during vital times.

关键词： machine learning algorithms Overcrowding Length of Stay Emergency Department Hajj event

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Opportunities of machine learning algorithms for education

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Discover Education 2024年第1期3卷 1-19页

作者： Ovtšarenko, Olga TTK University of Applied Sciences Tallinn Estonia Vilnius Gediminas Technical University Vilnius Lithuania

machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D Modeling" e-course on the Moodle platform at TTK University of Applied Sciences, Tallinn, Estonia. By predicting trends, identifying patterns, and optimizing resource allocation, machine learning can improve the efficiency of e-learning and provide students with tailored recommendations for acquiring relevant knowledge and skills. The results of the study show that machine learning algorithms can be used to process the available e-course log data, using the clickstream of e-course resources and for their automated processing. The results suggest potential applications in personalized course recommendations, prediction and dropout prevention strategies, resulting in a more effective and personalized educational experience. Future research will focus on improving models of available registration data, exploring and using advanced machine learning techniques to improve the accuracy and usefulness of predictions, and providing faster recommendations to help students navigate their studies more effectively. © The Author(s) 2024.

关键词： Data processing e-learning Features engineering Logging data machine learning algorithms Model training Moodle

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Predicting property prices with machine learning algorithms

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JOURNAL OF PROPERTY RESEARCH 2021年第1期38卷 48-70页

作者： Ho, Winky K. O. Tang, Bo-Sin Wong, Siu Wai Univ Hong Kong Dept Real Estate & Construct Hong Kong Peoples R China Univ Hong Kong Dept Urban Planning & Design Hong Kong Peoples R China Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China

This study uses three machine learning algorithms including, support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices. It applies these methods to examine a data sample of about 40,000 housing transactions in a period of over 18 years in Hong Kong, and then compares the results of these algorithms. In terms of predictive power, RF and GBM have achieved better performance when compared to SVM. The three performance metrics including mean squared error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) associated with these two algorithms also unambiguously outperform those of SVM. However, our study has found that SVM is still a useful algorithm in data fitting because it can produce reasonably accurate predictions within a tight time constraint. Our conclusion is that machine learning offers a promising, alternative technique in property valuation and appraisal research especially in relation to property price prediction.

关键词： machine learning algorithms SVM RF GBM property valuation

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Performance Evaluation of Various machine learning algorithms for Lung Cancer Prediction Using Demographic Data 3rd

Performance Evaluation of Various Machine Learning Algorithm...

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3rd International Conference on Artificial Intelligence and Knowledge Processing (AIKP)

作者： Sharmila, Mulagada Surya Kumar, K. Shiridi Ganie, Shahid Mohammad Hemachandran, K. Rege, Manjeet Woxsen Univ Sch Business Hyderabad 502345 Telangana India Woxsen Univ AI Res Ctr Dept Analyt Hyderabad 502345 India Univ St Thomas Dept Software Engn & Data Sci St Paul MN USA

ISBN: (纸本)9783031686160;9783031686177

Lung cancer is among the top deadly diseases, affecting human beings globally. Therefore, it is crucial to predict and detect this disease as early as possible, allowing the doctors and the patients to take the appropriate and essential actions. Techniques like machine learning can be applied to the same. In this study, we used machine learning to predict cancer in the lungs. We explored five machine learning algorithms, viz., Decision Tree (DT), Support vector machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF). A publicly available dataset that contains demographic information of 284 patients with 16 parameters is used to conduct this study. An extensive explorative analysis is performed to improve the quality assessment of the dataset. Among five algorithms, Logistic Regression (LR) exhibited best findings in terms of accuracy, precision, recall, specificity, f1-score, and negative predicted values (NPV). Compared to similar research works, the proposed model achieved better results based on various performance evaluation metrics. The proposed model can be used for other illnesses that have similar symptoms by using transfer learning approach.

关键词： Lung cancer prediction machine learning algorithms Predictive modeling Disease prediction Lifestyle Data

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Comparative Analysis of Transformer Fault Classification Based on DGA Data Using machine learning algorithms 6

Comparative Analysis of Transformer Fault Classification Bas...

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6th Global Power, Energy and Communication Conference (GPECOM)

作者： Coban, Melih Fidan, Murat Aytar, Oktay Bolu Abant Izzet Baysal Univ Electelect Engn Dept Bolu Turkiye

ISBN: (纸本)9798350351088;9798350351095

Dissolved gas analysis (DGA) is considered a leading technique for fault classification in power transformers. However, accurate analysis results can only be achieved if the measured gases are interpreted, appropriately. In DGA interpretation, traditional techniques, artificial intelligence techniques such as machine learning algorithms, and hybrid techniques are generally used. In this study, four well-known machine learning algorithms have been compared in terms of DGA fault classification: Support Vector machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Decision Tree (DT). The lowest accuracy rate was obtained as 63.63% using the NB algorithm and raw data. In addition to raw data, data converted to logarithmic form has been also used to develop classification models. The highest accuracy rate was determined as 94.54% using the DT algorithm and logarithmic data. The obtained results have been demonstrated the efficiency and stability of the DT algorithm for transformer fault classification, especially when the data was appropriately preprocessed.

关键词： transformer fault classification machine learning algorithms dissolved gas analysis

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Forecasting District Heating Demand using machine learning algorithms 16

Forecasting District Heating Demand using Machine Learning A...

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16th International Symposium on District Heating and Cooling (DHC)

作者： Saloux, Etienne Candanedo, Jose A. Nat Resources Canada CanmetENERGY 1615 Blvd Lionel Boulet Varennes PQ J3X 1P7 Canada

Short-term forecasting of thermal energy demand is critical to optimally manage on-site renewable energy generation and the charge and discharge of energy storage devices in district heating and cooling (DHC) systems. As part of a larger study on advanced predictive control for a solar district heating system with 52 homes - the Drake Landing Solar Community (DLSC) - this paper investigates the use of machine learning algorithms to predict the aggregated heating load of the community. In this study, the initial approach to estimate the heating load of the DLSC employed a piecewise linear regression based on the outdoor air temperature. Such an approach yields significant errors, in particular when weather forecasts are used instead of actual outdoor air temperature measurements. It has been found that machine learning algorithms, such as decision trees, can significantly improve the accuracy of predicted heating loads by incorporating the effect of additional influencing factors (e.g., time of the day, day of the week, solar radiation, etc.). In this study, the predicted heating demand obtained from different algorithms are compared under two different scenarios;(a) by using actual weather conditions from measured data;(b) by using weather forecasts. The potential implementation of such models for control purposes is discussed. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： solar community heating demand prediction machine learning algorithms weather forecast

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