In this research, we proposed a Deep Convolutional Neural Network (DCNN) model for
image-based plant leaf disease identification using data augmentation and hyperparameter …

Accurate segmentation of computerized tomography (CT) images is of great significance to
clinical diagnosis. However, because of the high similarity of gray values, it is a challenging …

Convolutional neural networks (CNNs) have attracted increasing attention in recent years
because of their powerful abilities to extract and represent spatial/temporal information …

Tea chrysanthemum detection at its flowering stage is one of the key components for
selective chrysanthemum harvesting robot development. However, it is a challenge to detect …

The data-driven fault diagnosis of bearings is important in the marine electric thruster. For
avoiding information loss when manually extracting features and unreliable diagnosis by a …

Abstract SMT (Surface Mount Technology) has been the backbone of PCB (Printed Circuit
Board) production for the last couple of decades. Even though the speed and accuracy of …

The past decade has demonstrated the potential of human activity recognition (HAR) with
WiFi signals owing to non-invasiveness and ubiquity. Previous research has largely …

When a complex large power system is in an emergency, the conventional discrete
emergency control strategy implemented will cause excess load or derivative accidents like …

Water scarcity has urged the need for adequate water demand forecasting to facilitate
efficient planning of municipal infrastructure. However, the development of water …

Automated vehicles are prone to traffic accidents in severe weather conditions. Real-time
vehicle detection can improve the driving safety of automated vehicles. This paper proposes …