KA LUN, EDDIE LAW

查看斯高帕斯 (Scopus) 概要

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每年研究成果

指紋

查看啟用 KA LUN, EDDIE LAW 的研究主題。這些主題標籤來自此人的作品。共同形成了獨特的指紋。

1 類似的個人檔案

34 Conference contribution
23 Article
5 Conference article
4 Paper
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Deep Learning Models for Time Series Forecasting: A Review
Li, W. & Law, K. L. E., 2024, 於: IEEE Access. 12, p. 92306-92327 22 p.
研究成果: Review article › 同行評審

開啟存取
1 引文斯高帕斯（Scopus）
double PT: Enhancing Meta-Learning Performance with Pre-Train and Meta-Pre-Train Processes
Wang, L. & LAW, KA. LUN. EDDIE., 2023, Proceedings - 2023 IEEE 35th International Conference on Tools with Artificial Intelligence, ICTAI 2023. IEEE Computer Society, p. 688-692 5 p. (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI).
研究成果: Conference contribution › 同行評審
Efficient Point Cloud Object Classifications with GhostMLP
Lai, H. & Law, K. L. E., 5月 2023, 於: Remote Sensing. 15, 9, 2254.
研究成果: Article › 同行評審

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Multi-Blockchain Model for Data Sharing with Bell-LaPadula Access Control
Chong, H. C. & Eddie Law, K. L., 2023, Proceedings - 2023 IEEE International Conference on Blockchain, Blockchain 2023. Institute of Electrical and Electronics Engineers Inc., p. 55-61 7 p. (Proceedings - 2023 IEEE International Conference on Blockchain, Blockchain 2023).
研究成果: Conference contribution › 同行評審
UL-CNN: An Unsupervised CNN Model for User Association in Wireless Networks
Ma, M., Liu, Y. & Eddie Law, K. L., 2023, 2023 8th International Conference on Signal and Image Processing, ICSIP 2023. Institute of Electrical and Electronics Engineers Inc., p. 866-870 5 p. (2023 8th International Conference on Signal and Image Processing, ICSIP 2023).
研究成果: Conference contribution › 同行評審

Faculty of Applied Sciences Researchers Publish New Study Findings on Engineering (Deep Learning Models for Time Series Forecasting: A Review)
KA LUN, EDDIE LAW
18/07/24
1 的項目媒體報導
新聞/媒體: Press/Media
Researchers at Faculty of Applied Sciences Publish New Data on Remote Sensing (Efficient Point Cloud Object Classifications with GhostMLP)
Ka Lun, Eddie Law
30/05/23
1 的項目媒體報導
新聞/媒體: Press/Media

羅家倫

指紋

過去五年中的合作和熱門研究領域

深入探索詳細資料

Deep Learning Models for Time Series Forecasting: A Review

double PT: Enhancing Meta-Learning Performance with Pre-Train and Meta-Pre-Train Processes

Efficient Point Cloud Object Classifications with GhostMLP

Multi-Blockchain Model for Data Sharing with Bell-LaPadula Access Control

UL-CNN: An Unsupervised CNN Model for User Association in Wireless Networks

Faculty of Applied Sciences Researchers Publish New Study Findings on Engineering (Deep Learning Models for Time Series Forecasting: A Review)

Researchers at Faculty of Applied Sciences Publish New Data on Remote Sensing (Efficient Point Cloud Object Classifications with GhostMLP)

羅 家倫

指紋

過去五年中的合作和熱門研究領域

研究成果

Deep Learning Models for Time Series Forecasting: A Review

double PT: Enhancing Meta-Learning Performance with Pre-Train and Meta-Pre-Train Processes

Efficient Point Cloud Object Classifications with GhostMLP

Multi-Blockchain Model for Data Sharing with Bell-LaPadula Access Control

UL-CNN: An Unsupervised CNN Model for User Association in Wireless Networks

新聞/媒體

Faculty of Applied Sciences Researchers Publish New Study Findings on Engineering (Deep Learning Models for Time Series Forecasting: A Review)

Researchers at Faculty of Applied Sciences Publish New Data on Remote Sensing (Efficient Point Cloud Object Classifications with GhostMLP)

羅家倫