In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …

Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

The application of Machine learning and deep learning techniques for time series
forecasting has gained significant attention in recent years. Numerous endeavors have been …

Object detection using remote sensing data is a key task of the perception systems of self-
driving vehicles. While many generic deep learning architectures have been proposed for …

In this study, an automatic autism diagnostic model based on sMRI is proposed. This
proposed model consists of two basic stages. The first stage is the preprocessing stage …

Featured Application Energy demand forecasting to improve power generation
management. Abstract Modern energy systems collect high volumes of data that can provide …

Deep learning has been widely applied to vision‐based structural damage detection, but its
computational demand is high. To avoid this computational burden, a novel crack detection …

Cities that are adopting innovative and technology‐driven solutions to improve the city's
efficiency are considered smart cities. With the increased attention on smart cities with self …

Deep learning has achieved promising results in pavement distress detection. However, the
training model's effectiveness varies according to the data and scenarios acquired by …