| 國立臺灣大學 |
2013 |
Detecting a general inclusion in the shallow shell
|
Wang, Jenn-Nan; Cristo, Michele Di; Lin, Ching-Lung; Vessella, Sergio |
| 臺大學術典藏 |
2018-09-10T14:51:04Z |
Detecting a weak association by testing its multiple perturbations: A data mining approach
|
Lo, M.-T. and Lee, W.-C.; WEN-CHUNG LEE |
| 臺大學術典藏 |
2020-11-19T08:19:22Z |
Detecting a weak association by testing its multiple perturbations: A data mining approach
|
Lo M.-T.;Wen-Chung Lee; Lo M.-T.; WEN-CHUNG LEE |
| 國立臺灣科技大學 |
2019 |
Detecting Abnormal Massive Crowd Flows: Characterizing Fleeing en Masse by Analyzing the Acceleration of Object Vectors
|
Chondro, P.;Liu, C.-Y.;Chen, Chen C.-Y.;Ruan, S.-J. |
| 中國醫藥大學 |
2003-07 |
Detecting abnormal regional cerebral blood flow in patients with primary Sjogren's syndrome by technetium-99m ethyl cysteinate dimer single photon emission computed tomography of the brain - a preliminary report.
|
黃偉師(Wei-Shih Huang); 邱百誼(P.Y. Chiu); 高嘉鴻(Chia-Hung Kao)*; 蔡崇豪(Chon-Haw Tsai); 李正淳(Cheng-Chun Lee) |
| 中國醫藥大學 |
2003 |
Detecting abnormal regional cerebral blood flow in patients with primary Sjorgren's syndrome by technetium-99m ethyl cysteinate dimer single photon emission computed tomography of the brain - a preliminary report
|
Huang, WS; Chiu, PY; Kao, A; Tsai, CH; Lee, CC |
| 中國醫藥大學 |
2003 |
Detecting abnormal regional cerebral blood flow in patients with primary Sjorgren's syndrome by technetium-99m ethyl cysteinate dimer single photon emission computed tomography of the brain - a preliminary report
|
Huang, WS; Chiu, PY; Kao, A; Tsai, CH; Lee, CC |
| 臺大學術典藏 |
2019-07-29T07:49:56Z |
Detecting actionable items in meetings by convolutional deep structured semantic models
|
He, X.;Hakkani-Tur, D.;Chen, Y.-N.; Chen, Y.-N.; Hakkani-Tur, D.; He, X. |
| 臺大學術典藏 |
2020-05-04T08:22:19Z |
Detecting actionable items in meetings by convolutional deep structured semantic models.
|
Chen, Yun-Nung;Hakkani-T?r, Dilek;He, Xiaodong; Chen, Yun-Nung; Hakkani-T?r, Dilek; He, Xiaodong; YUN-NUNG CHEN |
| 國立成功大學 |
2019 |
Detecting advanced persistent threat Malware using machine learning-based threat hunting
|
Lin, T.-C.;Guo, C.-C.;Yangv, C.-S. |
| 國立成功大學 |
2023 |
Detecting Adversarial Examples of Fake News via the Neurons Activation State
|
Tseng, F.;Zeng, J.;Cho, H.;Yeh, K.;Chen, Chen C. |
| 國立成功大學 |
2024-08 |
Detecting Adversarial Examples of Fake News via the Neurons Activation State
|
Tseng;Fan-Hsun;Zeng;Jiang-Yi;Cho;Hsin-Hung;Yeh;Kuo-Hui;Chen;Chi-Yuan |
| 臺大學術典藏 |
2022-03-08T08:27:20Z |
Detecting aflatoxin B1 in foods and feeds by using sensitive rapid enzyme-linked immunosorbent assay and gold nanoparticle immunochromatographic strip
|
Liu, Biing-Hui; Hsu Y.-T.; Lu C.-C.; Yu F.-Y. |
| 南台科技大學 |
2019-04 |
Detecting All Possible Ionospheric Precursors by Kernel-Based Two-Dimensional Principal Component Analysis
|
Jyh-Woei Lin;Juing-Shian Chiou;Chun-Tang Chao; |
| 國立交通大學 |
2019-04-02T06:04:47Z |
Detecting Amplification Attacks with Software Defined Networking
|
Chen, Chih-Chieh; Chen, Yi-Ren; Lu, Wei-Chih; Tsai, Shi-Chun; Yang, Ming-Chuan |
| 國立聯合大學 |
2007 |
Detecting and Adjusting Ordinal and Cardinal Inconsistencies through a Graphical and Optimal Approach in AHP Models
|
Han-Lin Li, Li-Ching Ma |
| 國立聯合大學 |
2005 |
Detecting and Adjusting Ordinal and Cardinal Inconsistencies through a Graphical and Optimal Approach in AHP Models
|
Han-Lin Li,Li-Ching Ma |
| 國立交通大學 |
2014-12-08T15:14:32Z |
Detecting and adjusting ordinal and cardinal inconsistencies through a graphical and optimal approach in AHP models
|
Li, Han-Lin; Ma, Li-Ching |
| 中國文化大學 |
2015-11 |
Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM
|
Chen, Rou-Fei; Lin, Ching-Weei; Chen, Yi-Hui; He, Tai-Chien; Fei, Li-Yuan |
| 國立成功大學 |
2015-11 |
Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM
|
Chen, Rou-Fei; Lin, Ching-Weei; Chen, Yi-Hui; He, Tai-Chien; Fei, Li-Yuan |
| 臺大學術典藏 |
2018-09-10T05:59:05Z |
Detecting and classifying emotion in popular music
|
Liu, C.-C.; Yang, Y.-H.; Wu, P.-H.; Chen, H.H.; HOMER H. CHEN |
| 元智大學 |
2018 |
Detecting and Classifying Types of Arrhythmia using Convolution Neural Network
|
安艾尼; Venkat Anil Adibhatla |
| 臺大學術典藏 |
2020-11-19T08:19:22Z |
Detecting and correcting the bias of unmeasured factors using perturbation analysis: A data-mining approach
|
Wen-Chung Lee; WEN-CHUNG LEE |
| 臺大學術典藏 |
2021-02-20T08:35:35Z |
Detecting and counting harvested fish and identifying fish types in electronic monitoring system videos using deep convolutional neural networks
|
Tseng, C.-H.; Kuo, Y.-F.; Tseng, C.-H.; Kuo, Y.-F.; YAN-FU KUO |
| 臺大學術典藏 |
2019-11-04T06:42:21Z |
Detecting and counting harvested fish and measuring fish body lengths in video using deep learning methods
|
Tseng, Chi Hsuan;YAN-FU KUO; YAN-FU KUO; Tseng, Chi Hsuan; Tseng, Chi Hsuan; YAN-FU KUO; YAN-FU KUO |
