Today with the fast-growing of artificial intelligence (AI), intelligence systems have been strongly studied and developed accomplished outstanding results, such as in the areas of robotics, intelligent assistance systems, medical image-based disease diagnosis, and so on. Developed machine learning methods for increasingly feature extraction, optimal model in terms of accuracy, processing speed have become a major research trend. Some research orientations in this subdomain include developing solutions to optimize deep learning models and learning hyperparameters for high discriminated feature extraction and outperformed pattern recognition. This project concentrates on studying and proposing novel approaches in machine learning techniques and feature extraction from images. The methods are designed to provide better pattern recognition in real datasets. Resulted solutions support intelligent decisions and suggestions such as recognizing diseases using visual data in medical diagnosis, detecting abnormal objects or dangerous behaviors in surveillance systems. We expect this research to be completed and contributed to the trend machine learning solutions that facilitate implementing and operating intelligent systems on resource-limited hardware to solve difficult data problems for the classification and recognition of complex objects and events. The result is toward AI technologies into practice application with acceptable costs.
Develop AI technology based on deep learning models for feature extraction and pattern recognition to solve specific problems in computer vision such as image classification, object detection, medical image processing, action recognition, and scene understanding for surveillance systems.
