Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …

Surface defect detection using deep learning approaches has become a promising area of
research, but the difficulty of accurately locating and segmenting various forms of defects …

Outcome-based education (OBE) is a well-proven teaching strategy based upon a
predefined set of expected outcomes. The components of OBE are Program Educational …

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of
dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed …

Online diagnosis is one of the data services, which can use the machine learning model
placed on the cloud and collected physical data from internet of medical things (IoMT) for …

Aiming at compound fault recognition, multi-label learning easily has a strong
comprehension on relevance between simultaneous mechanism faults, such as bearing …

Bearings and tools are the important parts of the machine tool. And monitoring automatically
the fault of bearings and the wear of tools under different working conditions is the …

In complex working site, bearings used as the important part of machine, could
simultaneously have faults on several positions. Consequently, multi-label learning …

In this paper, we propose a multi-class classification method using kernel supports and a
dynamical system under differential privacy. For small datasets, kernel methods, such as …

In the actual working site, the equipment often works in different working conditions while the
manufacturing system is rather complicated. However, traditional multi-label learning …