AbstractRegression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions have been studied the deep neural networks image classification tasks, options regression tasks are very limited. One of key challenges is that high-dimensional feature representation learned by existing popular ...

Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-consuming and labor-intensive to collect annotate bitemporal samples containing desired changes. Transfer from pretrained effective alleviate label insufficiency in remote sensing (RS) change detection (CD). We explore the use of semantic information during pretraining. Different traditional supervised pre...

Artificial intelligence is set to transform the next generation of intelligent buildings through application information and semantic data models machine learning algorithms. Semantic enable understanding real-world for building automation, integration control applications. This article explored use models, a subfield artificial intelligence, knowledge representation reasoning (KRR) across wide...

Artificial Intelligence is an important asset of Industry 4.0. Current discoveries within machine learning and particularly in deep enable qualitative change the industrial processes, applications, systems products. However, there challenge related to explainability (and, therefore, trust to) decisions made by models (aka black-boxes) their poor capacity for being integrated with each other. Ex...