Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments.

Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes. Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery.

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles.

Hemispheric Asymmetries in Electroencephalogram Oscillations for Long-Term Memory Retrieval in Healthy Individuals.

The hemispherical encoding retrieval asymmetry (HERA) model, established in 1991, suggests that the involvement of the right prefrontal cortex (PFC) in the encoding process is less than that of the left PFC. The HERA model was previously validated for episodic memory in subjects with brain traumas or injuries. In this study, a revised HERA model is used to investigate long-term memory retrieval from newly learned video-based content for healthy individuals using electroencephalography.

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities.

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks.