As an air pollution phenomenon, haze has become one of the focuses social discussion. Research into causes and concentration prediction is significant, forming basis prevention. The inversion Aerosol Optical Depth (AOD) based on remote sensing satellite imagery can provide a reference for major pollutants in haze, such as PM2.5 PM10 concentration. This paper used to study problems chose PM2.5, ...

Vertigo is a type of dizziness characterised by the subjective feeling movement despite being stationary. One in four individuals community experience symptoms at any given time, and it can be challenging for clinicians to diagnose underlying cause. When result malfunction inner-ear, eyes flicker this called nystagmus. In article we describe first use Deep Neural Network architectures applied d...

Predicting the future trajectories of pedestrians is a challenging problem that has range application, from crowd surveillance to autonomous driving. In literature, methods approach pedestrian trajectory prediction have evolved, transitioning physics-based models data-driven based on recurrent neural networks. this work, we propose new prediction, with introduction novel 2D convolutional model....

Attention mechanism has been regarded as an advanced technique to capture long-range feature interactions and boost the representation capability for convolutional neural networks. However, we found two ignored problems in current attentional activations-based models: approximation problem insufficient capacity of attention maps. To solve together, initially propose module networks by developin...

The advancement of convolutional neural networks (CNNs) on various vision applications has attracted lots attention. Yet the majority CNNs are unable to satisfy strict requirement for real-world deployment. To overcome this, recent popular network pruning is an effective method reduce redundancy models. However, ranking filters according their “importance” different criteria may be inconsistent...

Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights. In this paper, we propose a new method using genetic algorithms for evolving the architectures and connection weight initialization value...