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Deep Learning-Enhanced Potentiometric Aptasensing with Magneto-Controlled Sensors.

• Authors : Mou J; CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Yantai, 264003, Shandong, P. R. China.; University of Chinese Academy of Sciences, Beijing, 100049, P. R. China.

Subjects: Deep Learning* ; Aptamers, Nucleotide*/Aptamers, Nucleotide*/Aptamers, Nucleotide*/chemistry ; Biosensing Techniques*/Biosensing Techniques*/Biosensing Techniques*/methods

• Source: Angewandte Chemie (International ed. in English) [Angew Chem Int Ed Engl] 2023 Jan 16; Vol. 62 (3), pp. e202210513. Date of Electronic Publication: 2022 Dec 13.Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 0370543 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1521-3773

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Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital.

• Authors : Li, Gang; Li, Chenbi; Wang, Chengli

Subjects: MACHINE learning; DATA augmentation; RANDOM forest algorithms

• Source: PLoS ONE; 2/23/2024, Vol. 19 Issue 2, p1-13, 13p

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Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements.

• Authors : Liu, Wei; Wang, He; Xi, Zhenzhu

Subjects: TRANSFORMER models; DEEP learning; GAUSSIAN distribution

• Source: Remote Sensing; Jan2024, Vol. 16 Issue 1, p62, 20p

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Three-dimensional visualization of the vascular bundle in a branched bamboo node.

• Authors : Shan Li; Qianying Yang; Yangao Wang

Subjects: THREE-dimensional imaging; BAMBOO; X-ray computed microtomography

• Source: Frontiers in Plant Science; 2023, p1-14, 14p

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Data‐driven modelling methods in sintering process: Current research status and perspectives.

• Authors : Yan, Feng; Zhang, Xinmin; Yang, Chunjie

Subjects: BLAST furnaces; SINTERING; DEEP learning

• Source: Canadian Journal of Chemical Engineering; Aug2023, Vol. 101 Issue 8, p4506-4522, 17p

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LightCDNet: Lightweight Change Detection Network Based on VHR Images.

• Authors : Xing, Yuanjun; Jiang, Jiawei; Xiang, Jun

• Source: IEEE Geoscience & Remote Sensing Letters; 2023, Vol. 20, p1-5, 5p

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Deep Learning‐Enhanced Potentiometric Aptasensing with Magneto‐Controlled Sensors.

• Authors : Mou, Junsong; Ding, Jiawang; Qin, Wei

Subjects: DEEP learning; DEBYE length; APTAMERS

• Source: Angewandte Chemie International Edition; 1/16/2023, Vol. 62 Issue 3, p1-9, 9p

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A review of deep learning-based recommender system in e-learning environments.

• Authors : Liu, Tieyuan; Wu, Qiong; Chang, Liang

Subjects: RECOMMENDER systems; DIGITAL learning; RECURRENT neural networks

• Source: Artificial Intelligence Review; Dec2022, Vol. 55 Issue 8, p5953-5980, 28p

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Direct Multi‐Modal Inversion of Geophysical Logs Using Deep Learning.

• Authors : Alyaev, Sergey; Elsheikh, Ahmed H.

Subjects: ARTIFICIAL neural networks; DEEP learning; INVERSE problems

• Source: Earth & Space Science; Sep2022, Vol. 9 Issue 9, p1-13, 13p

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Occlusion-Aware Unsupervised Learning of Depth From 4-D Light Fields.

• Authors : Jin, Jing; Hou, Junhui

Subjects: KERNEL (Mathematics); GRAPHICS processing units; COHERENCE (Physics)

• Source: IEEE Transactions on Image Processing; 2022, Vol. 31, p2216-2228, 13p

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