A5028065505- Smart Agriculture and AI
- Advanced Computing and Algorithms
- Brain Tumor Detection and Classification
- Remote Sensing in Agriculture
- Drilling and Well Engineering
- Animal Disease Management and Epidemiology
- Horticultural and Viticultural Research
- Date Palm Research Studies
- Lung Cancer Diagnosis and Treatment
- Spectroscopy and Chemometric Analyses
- Technology Use by Older Adults
- Robotic Path Planning Algorithms
- Plant and Fungal Interactions Research
- Cryptographic Implementations and Security
- CCD and CMOS Imaging Sensors
- Oil and Gas Production Techniques
- Advanced Measurement and Detection Methods
- E-commerce and Technology Innovations
- Mosquito-borne diseases and control
- Biometric Identification and Security
- Remote-Sensing Image Classification
- Species Distribution and Climate Change
- Insect and Arachnid Ecology and Behavior
- Digital Media Forensic Detection
- Physical Unclonable Functions (PUFs) and Hardware Security
Zhejiang A & F University
2023-2025
State Forestry and Grassland Administration
2023
Huazhong University of Science and Technology
2021
Timely and accurate identification of tea tree pests is critical for effective pest control. We collected image data sets eight common to accurately represent the true appearance various aspects pests. The dataset contains 782 images, each containing 1~5 different species randomly distributed. Based on this dataset, a garden detection recognition model was designed using Yolov7-tiny network target algorithm, which incorporates deformable convolution, Biformer dynamic attention mechanism,...
Detection of lung nodules is key in the treatment early-stage cancer. Computed tomography (CT) scanning technology an essential contactless tool. However, stray radiation caused by a patient's slight movements and equipment operation can impair CT images, hindering accurate nodule detection. To address these issues, this study proposes artificial intelligence-based anti-interference detection method, which primarily structured with Yolov8 combines modules adaptive gating sparse attention...
This paper proposes an improved model to solve the problems of insufficient feature extraction, detail loss, gradient disappearance and redundant output in a deep residual shrinkage network(ReSNet) for image recognition. We introduce ReSNet based on spatial attention mechanism ensure that network can comprehensively extract features details enhance learning ability ReSNet. In this experiment, four models including, network, combined with mechanism, contraction are used verify classification...