1.4k citations · 2.1k across the 85 of their papers we have counts for
28 papers · 1 filter
Multi-Pass, Multi-View Blended Learning for High-Fidelity Volumetric CT Synthesis from Chest X-Rays
Ozer Can Devecioglu, Serkan Kiranyaz, Rashid Mazhar +3
Reconstructing volumetric Computed Tomography (CT) from a single 2D chest radiograph (CXR) is an ill-posed inverse problem, further complicated by the scarcity of paired CXR-CT tra…
Axiomatizing Neural Networks via Pursuit of Subspaces
Mehmet Yamac, Mert Duman, Ugur Akpinar +4
While deep neural networks have achieved remarkable success across a wide range of domains, their underlying mechanisms remain poorly understood, and they are often regarded as bla…
Randomized PCA Forest for Unsupervised Outlier Detection
Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj +2
We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in app…
Multiscale Tensor Summation Factorization as a New Neural Network Layer (MTS Layer) for Multidimensional Data Processing
Mehmet Yamaç, Muhammad Numan Yousaf, Serkan Kiranyaz +1
Multilayer perceptrons (MLP), or fully connected artificial neural networks, are known for performing vector-matrix multiplications using learnable weight matrices; however, their…
To Vaccinate or not to Vaccinate? Analyzing Power over the Pandemic
Tanveer Khan, Fahad Sohrab, Antonis Michalas +1
The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creation of vaccines. To halt the COVID…
Dual-Domain Fusion for Semi-Supervised Learning
Tuomas Jalonen, Mohammad Al-Sa'd, Serkan Kiranyaz +1
Labeled time-series data is often expensive and difficult to obtain, making it challenging to train accurate machine learning models for real-world applications such as anomaly det…
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach
Thomas Debelle, Fahad Sohrab, Pekka Abrahamsson +1
Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification me…
CoRe-Net: Co-Operational Regressor Network with Progressive Transfer Learning for Blind Radar Signal Restoration
Muhammad Uzair Zahid, Serkan Kiranyaz, Alper Yildirim +1
Real-world radar signals are frequently corrupted by various artifacts, including sensor noise, echoes, interference, and intentional jamming, differing in type, severity, and dura…
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems
Aoming Liang, Qi Liu, Lei Xu +4
Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantit…
High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs
Ozer Can Devecioglu, Serkan Kiranyaz, Zafer Yilmaz +3
Vibration sensors are essential in acquiring seismic activity for an accurate earthquake assessment. The state-of-the-art sensors can provide the best signal quality and the highes…
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network
Muhammad Uzair Zahid, Serkan Kiranyaz, Alper Yildirim +1
Objective: Many studies on radar signal restoration in the literature focus on isolated restoration problems, such as denoising over a certain type of noise, while ignoring other t…
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Muhammad Uzair Zahid, Aysen Degerli, Fahad Sohrab +4
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage. This study introduces…
Class-wise Generalization Error: an Information-Theoretic Analysis
Firas Laakom, Yuheng Bu, Moncef Gabbouj
Existing generalization theories of supervised learning typically take a holistic approach and provide bounds for the expected generalization over the whole data distribution, whic…
Fusion of Quadratic Time-Frequency Analysis and Convolutional Neural Networks to Diagnose Bearing Faults Under Time-Varying Speeds
Mohammad Al-Sa'd, Tuomas Jalonen, Serkan Kiranyaz +1
Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing thei…
Improving Unimodal Inference with Multimodal Transformers
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
This paper proposes an approach for improving performance of unimodal models with multimodal training. Our approach involves a multi-branch architecture that incorporates unimodal…
Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions
Tuomas Jalonen, Mohammad Al-Sa'd, Serkan Kiranyaz +1
Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected…
Cryptocurrency Portfolio Optimization by Neural Networks
Quoc Minh Nguyen, Dat Thanh Tran, Juho Kanniainen +2
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on…
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography
Ilke Adalioglu, Mete Ahishali, Aysen Degerli +2
Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium. In this s…
Convolutional autoencoder-based multimodal one-class classification
Firas Laakom, Fahad Sohrab, Jenni Raitoharju +2
One-class classification refers to approaches of learning using data from a single class only. In this paper, we propose a deep learning one-class classification method suitable fo…
On Feature Diversity in Energy-based Models
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches. An energy-based model (EBM) is typically formed of inner-m…
Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting
Adamantios Ntakaris, Moncef Gabbouj, Juho Kanniainen
High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vecto…
WLD-Reg: A Data-dependent Within-layer Diversity Regularizer
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from t…
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…
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
Mehmet Yamaç, Mert Duman, İlke Adalıoğlu +2
This paper proposes a low-cost and highly accurate ECG-monitoring system intended for personalized early arrhythmia detection for wearable mobile sensors. Earlier supervised approa…