Next-generation data center network enabled by machine learning: Review, challenges, and opportunities
Data center network (DCN) is the backbone of many emerging applications from smart
connected homes to smart traffic control and is continuously evolving to meet the diverse …
connected homes to smart traffic control and is continuously evolving to meet the diverse …
Attention-based Gate Recurrent Unit for remaining useful life prediction in prognostics
R Lin, H Wang, M Xiong, Z Hou, C Che - Applied Soft Computing, 2023 - Elsevier
An essential process in prognostics and health management (PHM) is remaining useful life
(RUL) prediction. The traditional Recurrent Neural Networks (RNNs) and their variants are …
(RUL) prediction. The traditional Recurrent Neural Networks (RNNs) and their variants are …
Theoretical Assessment for Weather Nowcasting Using Deep Learning Methods
AB Upadhyay, SR Shah, RA Thakkar - Archives of Computational Methods …, 2024 - Springer
Weather is influenced by various factors such as temperature, pressure, air movement,
moisture/water vapor, and the Earth's rotating motion. Accurate weather forecasting at a high …
moisture/water vapor, and the Earth's rotating motion. Accurate weather forecasting at a high …
Precipitation nowcasting with generative diffusion models
In recent years traditional numerical methods for accurate weather prediction have been
increasingly challenged by deep learning methods. Numerous historical datasets used for …
increasingly challenged by deep learning methods. Numerous historical datasets used for …
Sediment prediction in the great barrier reef using vision transformer with finite element analysis
Suspended sediment is a significant threat to the Great Barrier Reef (GBR) ecosystem. This
catchment pollutant stems primarily from terrestrial soil erosion. Bulk masses of sediments …
catchment pollutant stems primarily from terrestrial soil erosion. Bulk masses of sediments …
Transformers predicting the future. Applying attention in next-frame and time series forecasting
R Cholakov, T Kolev - arXiv preprint arXiv:2108.08224, 2021 - arxiv.org
Recurrent Neural Networks were, until recently, one of the best ways to capture the timely
dependencies in sequences. However, with the introduction of the Transformer, it has been …
dependencies in sequences. However, with the introduction of the Transformer, it has been …
Physics-informed generative neural network: an application to troposphere temperature prediction
The troposphere is one of the atmospheric layers where most weather phenomena occur.
Temperature variations in the troposphere, especially at 500 hPa, a typical level of the …
Temperature variations in the troposphere, especially at 500 hPa, a typical level of the …
IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans
M Shorfuzzaman - Computing, 2023 - Springer
The ongoing COVID-19 (novel coronavirus disease 2019) pandemic has triggered a global
emergency, resulting in significant casualties and a negative effect on socioeconomic and …
emergency, resulting in significant casualties and a negative effect on socioeconomic and …
Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach
U Muñoz-Aseguinolaza, I Fernandez-Iriondo… - Heliyon, 2023 - cell.com
Recent developments in technology and research have offered a wide variety of new
techniques for image and data analysis within the medical field. Medical research helps …
techniques for image and data analysis within the medical field. Medical research helps …
A convolutional neural network model using weighted loss function to detect diabetic retinopathy
Nowadays, artificial intelligence (AI) provides tremendous prospects for driving future
healthcare while empowering patients and service providers. The extensive use of digital …
healthcare while empowering patients and service providers. The extensive use of digital …