Abstract: Accurate power load forecasting is a cornerstone for the reliable operation and economic dispatch of modern power grids, particularly as the integration of Variable Renewable Energy ...
Abstract: ‘Photoplethysmography’ (PPG) is an approach that uses light to monitor changes in blood mass within tissues due to systole and diastole. The resulting blood volume signal can be utilized to ...
Abstract: Deepfake technology, using deep learning, creates highly realistic yet artificial media, creating challenges for security and privacy. Convolutional Neural Networks (CNNs) play a crucial ...
Abstract: This work investigates ECG arrhythmia classification using two-dimensional convolutional neural networks (2D CNNs) applied to wavelet-based time–frequency representations. Three CNN ...
Abstract: Early detection of lung cancer is highly beneficial for patient survival. This paper proposes a hybrid deep learning diagnostic pipeline for pulmonary nodules in chest CT. We constructed a ...
Abstract: Modulation identification of radio frequency (RF) signals is a fundamental task in contemporary wireless communications, electronic warfare, and spectrum surveillance. While deep ...
Abstract: Fiber Bragg grating (FBG) sensing systems have demonstrated strong potential for distributed vibration monitoring, yet recognizing mixed intrusion events remains challenging due to the ...
Abstract: Stroke is a major cause of long-term neurological impairment, and continuous monitoring of post-stroke patients is essential for rehabilitation and relapse prevention. Electroencephalogram ...
Abstract: Structural health monitoring is crucial for safeguarding critical infrastructure and requires the use of traceable methods. Hence, using explainable machine learning (ML) becomes ...
Replit's new feature allows users to create publishable and monetizable mobile apps using only natural language prompts. As more vibe-coding products come online, some software companies could see one ...