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Machine learning models improve the prediction of groundwater depth in the Ningxia area of China

For the Ningxia area, located in the arid and semi-arid regions of China, groundwater is one of the most important sources of drinking water. However, there has been little research on the application of machine learning models in predicting groundwater in this area.
Jiarui Cai Convolution Neural Network Chinese Academy Of Sciences Chinese Academy Nanjing University Information Science
Source: phys.org

Business News | Global Recognition of Literary Talents by Exceller Books

Get latest articles and stories on Business at LatestLY. New Delhi [India], January 31: Exceller Books has introduced The International Excellence Award with the aim of acknowledging and honoring writers across diverse disciplines. These awards are bestowed upon writers in recognition of their noteworthy contributions to the realms of literature and academia. Within the Academic Reference/Textbook Writers category, accolades have been granted to four exceptional authors: Bhavya Venkatesh, the a...
New Delhi Bhavya Venkatesh Amit Tiwari Richu Karan Garg Geteilte Himmel Rohit Jaysing Bhor

"A time-series Wasserstein GAN method for state-of-charge estimation of" by Xinyu Gu, K. W. See et al.

Estimating the state-of-charge (SOC) of lithium-ion batteries is essential for maintaining secure and reliable battery operation while minimizing long-term service and maintenance expenses. In this work, we present a novel Time-Series Wasserstein Generative Adversarial Network (TS-WGAN) approach for SOC estimation of lithium-ion batteries, characterized by a well-designed data preprocessing process and a distinctive WGAN-GP architecture. In the data preprocessing stage, we employ the Pearson cor...
Series Wasserstein Generative Adversarial Network Convolution Neural Network Convolutional Neural Network Cnn Lithium Ion Battery Tate Of Charge Soc Time Series Forecasting
Source: uow.edu.au

"Multi-features fusion for short-term photovoltaic power prediction" by Ming Ma, Xiaorun Tang et al.

In recent years, in order to achieve the goal of 'carbon peaking and carbon neutralization', many countries have focused on the development of clean energy, and the prediction of photovoltaic power generation has become a hot research topic. However, many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation, and they rarely consider the multi-features fusion methods for power prediction. This paper first...
Temporal Convolutional Network Recurrent Neural Network Convolution Neural Network Northwest China Extreme Gradient Boosting Long Short Term Memory
Source: uow.edu.au

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"A COVID-19 X-ray image classification model based on an enhanced convo" by Ashwini Kumar Pradhan, Debahuti Mishra et al.

The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR...
Convolution Neural Network Geometry Group Neural Network Visual Geometry Group Caps Net Residual Neural Network
Source: uow.edu.au

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