A5030352025- AI in cancer detection
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Plant-based Medicinal Research
- Artificial Intelligence in Healthcare and Education
- Gear and Bearing Dynamics Analysis
- Advanced Glycation End Products research
- Currency Recognition and Detection
- Spectroscopy and Chemometric Analyses
- Phytochemistry and Biological Activities
- Skin Diseases and Diabetes
- Digital Media Forensic Detection
- Artificial Intelligence in Healthcare
- Colorectal Cancer Screening and Detection
- Advanced Data Processing Techniques
- Optical measurement and interference techniques
- Machine Fault Diagnosis Techniques
- Diabetes and associated disorders
- Industrial Technology and Control Systems
- Advanced Steganography and Watermarking Techniques
- Animal Behavior and Welfare Studies
- Food Quality and Safety Studies
- Leaf Properties and Growth Measurement
Jiangsu University
2025
Air Force General Hospital PLA
2025
Air Force Medical University
2025
National Yang Ming Chiao Tung University
2017-2022
National Applied Research Laboratories
2022
University of Maryland, College Park
2004
Artificial intelligence (AI)-driven medical assistive technology has been widely used in the diagnosis, treatment and prognosis of diabetes complications. Here we conduct a bibliometric analysis scientific articles field AI complications to explore current research trends cutting-edge hotspots. On April 20, 2024, collected screened relevant published from 1988 2024 PubMed. Based on tools such as CiteSpace, Vosviewer bibliometix, construct knowledge maps visualize literature information,...
A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme that system divided into two layers of motion tracking and torque distribution, three systems, including driving, braking, front-wheel steering are controlled collaboratively four-wheel distribution. In layer tracking, a vehicle model with two-degree-of-freedom employed predict reference values...
For digital pathology, automatic recognition of different tissue types in histological images is important for diagnostic assistance and healthcare. Since generally contain more than one type, multi-class texture analysis plays a critical role to solve this problem. The chest X-ray the most commonly accessible radiological examinations screening diagnosis many lung diseases. This study examines statistical features including use Convolutional Layer, ReLU Layer Pooling detect pneumonia from...
The objective assessment of histological images is paramount importance for the early diagnosis colorectal cancer (CRC). However, subjectivity evaluation interobserver variation and traditional visual time-consuming costly. On other hand, automatic recognition analysis digital pathology, challenging remains due to variability containing more than one tissue type characteristics images. In this work, we applied different deep learning techniques based on Convolutional Neural Networks (CNNs)...
Technological advances in digitization with a variety of image manipulation techniques enable the creation printed documents illegally. Correspondingly, many researchers conduct studies determining whether document counterfeit or original. This study examines several statistical feature sets from Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor Haralick and fractal filters to identify text by using support vector machine (SVM)...
Medical image classification is a novel technology that presents new challenge. It essential pathological images are automatically and correctly classified to enable doctors provide precise treatment. Convolutional neural networks have demonstrated their effectiveness in classifying deep learning, which may dozens or hundreds of layers, illustrate the relationship between them terms different network features. layers consisting small kernels take weights as input guide through an activation...
A crescent model is proposed for chick wing image processing and feather pattern recognition, thereby implementing sex separation by machine vision technology. The shape delineates the region of interest in a an arc large radius small at two off-centred circles. Wing feathers are divergently distributed region, manifesting as oriented stripe pattern. Male gradually change length from short to long then accordance with envelope. Female alternate lengths, following long–short–long Based on...