From tropical forests to coastal reserves, destinations built around natural heritage are facing mounting pressure to manage tourism more precisely. The pandemic disrupted visitor flows and revealed ...
WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification BEIJING, Feb.06, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the ...
Abstract: Interharmonics diagnosis is increasingly important due to the rise of power electronic devices, renewable energy systems, and related technologies. The IEC 61000-4-7 standardizes the ...
ABSTRACT: This paper proposes a hybrid AI framework that integrates technical indicators, fundamental data, and financial news sentiment into a stacked ensemble learning model. The ensemble combines ...
The increasing demand for sustainable materials has highlighted the importance of biocomposites; however, their computational analysis is challenging due to the high cost associated with traditional ...
AI transforms RF engineering through neural networks that predict signal behavior and interference patterns, enabling proactive system optimization and enhanced performance. The convergence of machine ...
Although electroencephalogram (EEG) is widely used to monitor brain activity in epilepsy, limitations related to the accessibility and reproducibility of measurements may restrict its everyday use.
The integration of artificial neural networks (ANNs) with thermal analysis techniques, such as thermogravimetry (TG) and differential scanning calorimetry (DSC), presents great potential to accurately ...
Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...
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