51 citations · 113 across the 18 of their papers we have counts for
22 papers
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
Sertac Kilickaya, Mete Ahishali, Cansu Celebioglu +5
The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for ef…
Blind Underwater Image Restoration using Co-Operational Regressor Networks
Ozer Can Devecioglu, Serkan Kiranyaz, Turker Ince +1
The exploration of underwater environments is essential for applications such as biological research, archaeology, and infrastructure maintenanceHowever, underwater imaging is chal…
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
Serkan Kiranyaz, Ozer Can Devecioglu, Amir Alhams +4
Robust and real-time detection of faults on rotating machinery has become an ultimate objective for predictive maintenance in various industries. Vibration-based Deep Learning (DL)…
Sound-to-Vibration Transformation for Sensorless Motor Health Monitoring
Ozer Can Devecioglu, Serkan Kiranyaz, Amer Elhmes +6
Automatic sensor-based detection of motor failures such as bearing faults is crucial for predictive maintenance in various industries. Numerous methodologies have been developed ov…
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Sertac Kilickaya, Mete Ahishali, Fahad Sohrab +2
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification…
Improved Active Fire Detection using Operational U-Nets
Ozer Can Devecioglu, Mete Ahishali, Fahad Sohrab +2
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can c…
Blind Restoration of Real-World Audio by 1D Operational GANs
Turker Ince, Serkan Kiranyaz, Ozer Can Devecioglu +3
Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ig…
Zero-Shot Motor Health Monitoring by Blind Domain Transition
Serkan Kiranyaz, Ozer Can Devecioglu, Amir Alhams +5
Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing fa…
Blind ECG Restoration by Operational Cycle-GANs
Serkan Kiranyaz, Ozer Can Devecioglu, Turker Ince +8
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acq…
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…
Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs
Ozer Can Devecioglu, Junaid Malik, Turker Ince +3
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progr…
Super Neurons
Serkan Kiranyaz, Junaid Malik, Mehmet Yamac +5
Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neuro…
Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network
Muhammad Uzair Zahid, Serkan Kiranyaz, Turker Ince +5
Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic…
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
Mete Ahishali, Serkan Kiranyaz, Turker Ince +1
Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning…
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear…
Self-Organized Operational Neural Networks with Generative Neurons
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as netw…
1D Convolutional Neural Networks and Applications: A Survey
Serkan Kiranyaz, Onur Avci, Osama Abdeljaber +3
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Ar…
Colorectal cancer diagnosis from histology images: A comparative study
Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth +4
Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approach…
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks
Serkan Kiranyaz, Morteza Zabihi, Ali Bahrami Rad +4
The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnosti…
Operational Neural Networks
Serkan Kiranyaz, Turker Ince, Alexandros Iosifidis +1
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning perf…