Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …

Over the past several decades the dramatic increase in the availability of computational
resources, coupled with the maturation of machine learning, has profoundly impacted …

Sensor-based activity recognition seeks the profound high-level knowledge about human
activities from multitudes of low-level sensor readings. Conventional pattern recognition …

Plant species classification is crucial for biodiversity protection and conservation. Manual
classification is time-consuming, expensive, and requires experienced experts who are often …

Featured Application The proposed intelligent medical system is applicable for a medical
diagnostic system, especially for the diagnosis of diabetic foot ulcer. Abstract One of the …