62 citations · 201 across the 21 of their papers we have counts for
23 papers
Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
Luukas Peräkylä, Fahad Sohrab, Ville Hautamäki +3
Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events. Consumer wear…
From Reports to Ontologies: Ontology-Guided Representation Learning for 12-Lead ECG
Lei Xu, Fahad Sohrab, Mehmet Yamac +2
The 12-lead electrocardiogram (ECG) is a quasi-periodic, multi-channel signal with diagnostic content spanning timescales from millisecond waveform morphology to multi-second rhyth…
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…
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…
Deep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data
Faiz Muhammad Chaudhry, Jarno Ralli, Jerome Leudet +4
This research addresses the challenge of camera calibration and distortion parameter prediction from a single image using deep learning models. The main contributions of this work…
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…
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…
Trustworthiness of Users: A One-Class Classification Approach
Tanveer Khan, Fahad Sohrab, Antonis Michalas +1
(formerly Twitter) is a prominent online social media platform that plays an important role in sharing information making the content generated on this platform a valu…
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…
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification
Zaffar Zaffar, Fahad Sohrab, Juho Kanniainen +1
In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial…
One-Class Classification for Intrusion Detection on Vehicular Networks
Jake Guidry, Fahad Sohrab, Raju Gottumukkala +2
Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work…
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…
Newton Method-based Subspace Support Vector Data Description
Fahad Sohrab, Firas Laakom, Moncef Gabbouj
In this paper, we present an adaptation of Newton's method for the optimization of Subspace Support Vector Data Description (S-SVDD). The objective of S-SVDD is to map the original…
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…
A multimodal stress detection dataset with facial expressions and physiological signals
Majid Hosseini, Fahad Sohrab, Raju Gottumukkala +5
Affective computing has garnered the attention and interest of researchers in recent years, as there is a need for AI systems to better understand and react to human emotions. Howe…
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz +1
Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocard…
AnomalyHop: An SSL-based Image Anomaly Localization Method
Kaitai Zhang, Bin Wang, Wei Wang +3
An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1…
Graph-Embedded Subspace Support Vector Data Description
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj +1
In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes…
Ellipsoidal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data…
Boosting rare benthic macroinvertebrates taxa identification with one-class classification
Fahad Sohrab, Jenni Raitoharju
Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be sca…
Multimodal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively tran…
Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj +1
This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspac…