AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed CNN-GWO-BiLSTM-Attention model improves the prediction accuracy by up to approximately 10% in terms of the coefficient of determination R2 for temperature and the residual distribution of the model is more concentrated with no obvious systematic deviation, enabling it to accurately capture nonlinear fluctuations and local high-frequency characteristics in monitoring data.
Heart disease remains a main cause of death worldwide, showing the necessity for accurate and timely automated diagnostic systems. Hence, precise and early detection is necessary and is done by some existing machine learning (ML) and deep learning (DL) algorithms. Existing ML and DL models, like convolutional neural ne...
As the global demand for sustainable energy grows, the importance of solar energy as a clean and inexhaustible source of energy has become increasingly prominent. However, the efficiency and yield of solar power generation are directly affected by the solar radiation intensity, so accurate prediction of solar radiat...
The proposed CNN–BiLSTM–Attention framework performed better than conventional benchmark models of machine learning and deep learning, achieving a Root Mean Square Error of 0.088 and Seasonal and atmospheric-condition evaluations also showed consistent performance across different seasonal and atmospheric conditions.
A hybrid model integrating a Temporal Convolutional Network, an attention mechanism, and a Bidirectional Long Short-Term Memory network is proposed for predicting turboshaft engine performance parameters, demonstrating that the model possesses excellent generalization performance and holds significant potential for pra...
Predicting PM2.5 concentration is a critical task for public health and urban planning. While deep learning models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) have shown promise in capturing temporal dependencies, they often neglect the complex spatial correlations between monitoring st...
This study proposes an improved attention-guided convolutional bidirectional long short-term memory (IAC-BiLSTM) model, which integrates a modified convolutional block attention module (CBAM) with a convolutional neural network (CNN) and BiLSTM architectures. Meanwhile, this model is applied to the behavior recognition...
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