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Image denoising by Super Neurons: Why go deep?
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep…
ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
Muhammad Asif Khan, Ridha Hamila, Adel Gastli +2
Device mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the…
Early Myocardial Infarction Detection over Multi-view Echocardiography
Aysen Degerli, Serkan Kiranyaz, Tahir Hamid +2
Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI an…
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
Moncef Gabbouj, Serkan Kiranyaz, Junaid Malik +5
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acqu…
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz +2
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high ac…
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks
Turker Ince, Junaid Malik, Ozer Can Devecioglu +4
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recent…