The concentration of PM2.5 is an important index to measure the degree air pollution. When it exceeds standard value, considered cause pollution and lower quality, which harmful human health can a variety diseases, i.e., asthma, chronic bronchitis, etc. Therefore, prediction helpful reduce its harm. In this paper, hybrid model called CNN-BiLSTM-Attention proposed predict over next two days. Fir...

In this paper, we propose an emotion recognition model based on convolutional neural network (CNN), long short term memory (LSTM) and channel attention mechanism, aiming at the low classification accuracy of machine learning methods uneven spatial distribution electroencephalogram (EEG) electrodes. This can effectively integrate frequency, space time information EEG signals, improve by adding m...

Federated incremental learning best suits the changing needs of common Federal Learning (FL) tasks. In this area, large sample client dramatically influences final model training results, and unbalanced features are challenging to capture. paper, a federated framework is designed; firstly, part data preprocessed obtain initial global model. Secondly, help get importance whole each client, enhan...

Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation performance. To make CNNs more efficient, dynamic (DyNet) have been proposed increase the complexity of model by using Squeeze-and-Excitation (SE) module adaptively obtain importance each convolution kernel through attention mechanism. H...

Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce several compressive sensing (CS) techniques were implemented recent years but these are often iterative computationally complex to realize power-constrained user equ...

To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- distance-related attenuation scattering when light propagates water, we propose a method based on an attention mechanism adversarial autoencoder for enhancing images. Firstly, pixel channel mechanisms are utilized to extract rich discriminative image information from multiple spaces. Secondly, a...

Pedestrian detection is widely used in daily life, but it difficult to study highway environment, such as occlusion overlap. In order reduce error rate of pedestrian detection, an algorithm based on YOLOv5s was proposed. Since vehicle leads the reduction effective features targets, CBAM attention and SE-NET mechanism module introduced network maximize extraction features. prevent spatial charac...

Abstract Attentional mechanisms play an important role in artificial intelligence, and inspired by other work, we apply them the field of recommendation algorithms. By combining factorization machine neural network together through parallel structure, linear model is used for binary feature interaction, nonlinear relationship between features explored using network. We propose Att-NFM to learn ...