An overview of attention detection using EEG signals, which includes six steps: an experimental paradigm design, in which the task and the stimuli are defined and presented to the subjects; EEG data ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

AI fault detection uses waveform analytics and machine learning to identify early electrical failure signatures in distribution systems. Utilities gain predictive insight into incipient faults, asset ...

Brain-Computer Interfaces (BCIs) are emerging as transformative tools that enable direct communication between the human brain and external devices. With recent advancements in Electroencephalography ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

AI transforms digital wallets from transaction processors into intelligent systems. Instead of enforcing fixed rules, machine learning models evaluate context like user behavior, device ...

Incipient fault detection using AI classification represents a fundamental advancement in distribution system reliability engineering. By continuously analyzing waveform behavior and classifying ...

Phishing websites remain a persistent cybersecurity threat, exploiting users by imitating trusted online services. New machine-learning tools could help organisations flag more phishing sites before ...