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2. Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

Author

Trakunphutthirak, Ruangsak and Lee, Vincent C. S.

Abstract

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy.

Published
2022
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3. Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control

Author

Smith, Phillip, Aleti, Aldeida, Lee, Vincent C. S., Hunjet, Robert, and Khan, Asad

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems
Abstract

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.

Published
2019
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5. Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction

Author

Zheng, Yizhen, Lee, Vincent C. S., Wu, Zonghan, Pan, Shirui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlapalem, Kamal, editor, Cheng, Hong, editor, Ramakrishnan, Naren, editor, Agrawal, R. K., editor, Reddy, P. Krishna, editor, Srivastava, Jaideep, editor, and Chakraborty, Tanmoy, editor

Published
2021
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6. Data Communication Assistance via Swarm Robotic: A Behaviour Creation Comparison

Author

Smith, Phillip, Khan, Asad, Aleti, Aldeida, Lee, Vincent C. S., Hunjet, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, El Fallah Seghrouchni, Amal, editor, and Sarne, David, editor

Published
2020
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9. Cross-entropy-based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot.

Author

Jianqi Zhang, Xu Yang, Wei Wang, Jinchao Guan, Wenbo Liu, Hainian Wang, Ling Ding, and Lee, Vincent C. S.

Abstract

Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross-entropy-based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT-UMbot) for road cracks. Specifically, the CEAFC method uses cross-entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT-UMbot based on a four-wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state-of-the-art performance with high efficiency. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Knowledge Sharing via Informal Communities in a Globally Distributed Organization

Author

Chen, Penny, Cheung, Yen, Lee, Vincent C. S., Hart, Adam, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Chen, Jian, editor, Nakamori, Yoshiteru, editor, Yue, Wuyi, editor, and Tang, Xijin, editor

Published
2016
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15. Incremental and Adaptive Clustering Stream Data over Sliding Window

Author

Dang, Xuan Hong, Lee, Vincent C. S., Ng, Wee Keong, Ong, Kok Leong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bhowmick, Sourav S., editor, Küng, Josef, editor, and Wagner, Roland, editor

Published
2009
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16. Feature Selection Techniques, Company Wealth Assessment and Intra-sectoral Firm Behaviours

Author

Barnes, Mark B., Lee, Vincent C. S., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Heutte, Laurent, editor, and Loog, Marco, editor

Published
2007
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17. Development and Test of an Artificial-Immune- Abnormal-Trading-Detection System for Financial Markets

Author

Lee, Vincent C. S., Yang, Xingjian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Zhang, Xiao-Ping, editor, and Huang, Guang-Bin, editor

Published
2005
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18. A Hybrid Artificial Intelligent-Based Criteria-Matching with Classification Algorithm

Author

Sim, Alex T. H., Lee, Vincent C. S., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Lipo, editor, and Jin, Yaochu, editor

Published
2005
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19. Volatility Transmission Between Stock and Bond Markets: Evidence from US and Australia

Author

Fang, Victor, Lee, Vincent C. S., Lim, Yee Choon, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gallagher, Marcus, editor, Hogan, James P., editor, and Maire, Frederic, editor

Published
2005
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20. Credit Risks of Interest Rate Swaps: A Comparative Study of CIR and Monte Carlo Simulation Approach

Author

Fang, Victor, Lee, Vincent C. S., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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21. A Hybrid Fuzzy-Neuro Model for Preference-Based Decision Analysis

Author

Lee, Vincent C. S., Sim, Alex T. H., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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22. An Algorithm for Artificial Intelligence-Based Model Adaptation to Dynamic Data Distribution

Author

Lee, Vincent C. S., Sim, Alex T. H., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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23. An Alternative Methodology for Mining Seasonal Pattern Using Self-Organizing Map

Author

Denny, Lee, Vincent C. S., Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Dai, Honghua, editor, Srikant, Ramakrishnan, editor, and Zhang, Chengqi, editor

Published
2004
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30. Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data.

Author

Trakunphutthirak, Ruangsak and Lee, Vincent C. S.

Subjects
DATA mining, ACADEMIC achievement, LEARNING Management System, WEB browsing, MACHINE learning, PROGRESSIVE collapse, TEMPORAL databases
Abstract

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy. [ABSTRACT FROM AUTHOR]

Published
2022
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