Výsledky vyhledávání - "Transformer Neural Network"

Akademický článek

GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors

Autor: Candra Zonyfar, Soualihou Ngnamsie Njimbouom, Sophia Mosalla, Jeong-Dong Kim

Publikováno v: Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-18 (2024)

Abstract State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remai

Externí odkaz: https://doaj.org/article/12cbfa8efbcd4f1ba8c069e2ebd4a4fa

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Akademický článek

Application of three Transformer neural networks for short-term photovoltaic power prediction: A case study

Autor: Jiahao Wu, Yongkai Zhao, Ruihan Zhang, Xin Li, Yuxin Wu

Publikováno v: Solar Compass, Vol 12, Iss , Pp 100089- (2024)

In order to solve the potential safety hazards caused by the fluctuation of photovoltaic (PV) power generation, it is necessary to predict it in advance and take countermeasures as soon as possible. Based on the three models of vanilla Transformer, I

Externí odkaz: https://doaj.org/article/1934156796054b1aaf922b27a33c25b6

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Akademický článek

Distribution-agnostic landslide hazard modelling via Graph Transformers

Autor: Belvederesi, Gabriele ^a, Tanyas, Hakan ^b, Lipani, Aldo ^a, Dahal, Ashok ^b, Lombardo, Luigi ^{b, ⁎}

Publikováno v: In Environmental Modelling and Software January 2025 183

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Akademický článek

The Visual Saliency Transformer Goes Temporal: TempVST for Video Saliency Prediction

Autor: Nikos Lazaridis, Kostas Georgiadis, Fotis Kalaganis, Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Spiros Nikolopoulos, Ioannis Kompatsiaris

Publikováno v: IEEE Access, Vol 12, Pp 129705-129716 (2024)

The Transformer revolutionized Natural Language Processing and Computer Vision by effectively capturing contextual relationships in sequential data through its attention mechanism. While Transformers have been explored sufficiently in traditional com

Externí odkaz: https://doaj.org/article/682bb2794dfb4408baf1f72c1a7001dc

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Akademický článek

A Large Dataset to Enhance Skin Cancer Classification With Transformer-Based Deep Neural Networks

Autor: Mirco Gallazzi, Sara Biavaschi, Alessandro Bulgheroni, Tommaso M. Gatti, Silvia Corchs, Ignazio Gallo

Publikováno v: IEEE Access, Vol 12, Pp 109544-109559 (2024)

The advent of Deep Learning methodologies has revolutionized the field of medical image analysis, particularly in skin lesion diagnosis and classification. This paper proposes an explorative approach utilizing Transformer-based deep neural networks t

Externí odkaz: https://doaj.org/article/1ac0f7f964384156a53c82dbcb458585

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Akademický článek

Hybrid Transformer Network for Soil Moisture Estimation in Precision Irrigation

Autor: Neethu Madhukumar, Eric Wang, Yvette Everingham, Wei Xiang

Publikováno v: IEEE Access, Vol 12, Pp 48898-48909 (2024)

Accurate root zone soil moisture (RZSM) estimation is essential for precision irrigation (PI) systems that seek to optimize water use efficiency. Large-scale in-situ sensors for direct measurement are costly, while existing satellites lack depth reso

Externí odkaz: https://doaj.org/article/47a4fc97031d4f5a8da5a7cfc1563cc0

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Akademický článek

A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs

Autor: Jing Hao, Lun M. Wong, Zhiyi Shan, Qi Yong H. Ai, Xieqi Shi, James Kit Hon Tsoi, Kuo Feng Hung

Publikováno v: Diagnostics, Vol 14, Iss 17, p 1948 (2024)

Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edent

Externí odkaz: https://doaj.org/article/74f36a97765242f3b353d43017b0fbc9

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Akademický článek

Short-term PV power forecast using hybrid deep learning model and Variational Mode Decomposition

Autor: Thanh Nguyen Trong, Huu Vu Xuan Son, Hieu Do Dinh, Hirotaka Takano, Tuyen Nguyen Duc

Publikováno v: Energy Reports, Vol 9, Iss , Pp 712-717 (2023)

In recent decades, the dramatic transformation from conventional energy to renewables, such as photovoltaic (PV), has been extensively occurred to address the increasing electricity demand and environmental issues. Nevertheless, the operation of PV s

Externí odkaz: https://doaj.org/article/e4320c710d434bd783fe3f3de6a1e805

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Akademický článek

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

Autor: Xuebin Xie, Yunpeng Yang

Publikováno v: Applied Sciences, Vol 14, Iss 10, p 4188 (2024)

To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission sig

Externí odkaz: https://doaj.org/article/ce2dcc30455b46bea2f2a03159bc3824

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Akademický článek

Short-term load forecasting for power systems with high-penetration renewables based on multivariate data slicing transformer neural network

Autor: Wen Lu, Xingjie Chen

Publikováno v: Frontiers in Energy Research, Vol 12 (2024)

Introduction: The characteristics of intermittency and volatility brought by a high proportion of renewable energy impose higher requirements on load forecasting in modern power system. Currently, load forecasting methods mainly include statistical m

Externí odkaz: https://doaj.org/article/1f39647c11b14e8fb8693a7e6b7dcab3

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