Computer-aided image analysis for better understanding of images has been time-honored
approaches in the medical computing field. In the conventional machine learning approach …

Uncertainty and imprecision accompany the world we live in and occur in almost every
event. How to better interpret and manage uncertainty and imprecision play a vital role in …

How to manage conflict is still an open issue in Dempster–Shafer (DS) evidence theory. The
conflict coefficient k in DS evidence theory cannot represent conflict reasonably, especially …

One of the major challenges in anatomical landmark detection, based on deep neural
networks, is the limited availability of medical imaging data for network learning. To address …

We revisit logistic regression and its nonlinear extensions, including multilayer feedforward
neural networks, by showing that these classifiers can be viewed as converting input or …

In this paper, to address the issue that ensembling k-nearest neighbor (kNN) classifiers with
resampling approaches cannot generate component classifiers with a large diversity, we …

Classifier fusion remains an effective method to improve classification performance. In
applications, the classifiers learnt using different attributes may work with various frames of …

Live fish recognition is one of the most crucial elements of fisheries survey applications
where the vast amount of data is rapidly acquired. Different from general scenarios …

Due to its major focus on knowledge representation and reasoning, artificial intelligence was
bound to deal with various frameworks for the handling of uncertainty: probability theory, but …

Conflict issue has been a topic of immense interest in evidence theory because the current
methods still do not accurately reflect the conflict degree between evidence bodies. Thus …