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Found 71 papers, 19 papers with code
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage.
Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults
Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns.
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
One major reason is the lack of a benchmark dataset providing a large volume of both vibration and sound data over several working conditions for different machines and sensor locations.
Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions
Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures.
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography
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.
A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN
It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0. 25% and 0. 27%, respectively.
Operational Support Estimator Networks
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task.
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
This work focuses on extending the convolutional filters of a popular super-resolution model to more powerful operational filters to enhance the model performance on hyperspectral images.
Real-Time Damage Detection in Fiber Lifting Ropes Using Convolutional Neural Networks
The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage.
Blind Restoration of Real-World Audio by 1D Operational GANs
In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it.
Zero-Shot Motor Health Monitoring by Blind Domain Transition
To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics.
Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping
The DA module transfers the accumulated knowledge in contrasting no-damage and damage cases in the source domain to the target domain, given only the target's no-damage case.
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification
The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model.
2D Self-Organized ONN Model For Handwritten Text Recognition
With their heterogeneous network structure incorporating non-linear neurons, Operational Neural Networks (ONNs) have recently been proposed to address this drawback.
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
An extensive set of experiments performed on the benchmark MIT-BIH ECG dataset shows that when this domain adaptation-based training data generator is used with a simple 1-D CNN classifier, the method outperforms the prior work by a significant margin.
Ranked #2 on
Arrhythmia Detection
on MIT-BIH Arrhythmia Database
Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait
In the GRF analysis, the model showed 94. 78% accuracy by using the Top 15 features for the feature combinations extracted from GRFx, GRFy and GRFz signals.
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques.
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis
The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16. 11 dB and 26. 40%, respectively for all the fNIRS signals using fk4 wavelet packet.
Global ECG Classification by Self-Operational Neural Networks with Feature Injection
The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification.
OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121, 378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community.
SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection
The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy.
Blind ECG Restoration by Operational Cycle-GANs
Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult.
RamanNet: A generalized neural network architecture for Raman Spectrum Analysis
Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials.
Image denoising by Super Neurons: Why go deep?
As the integration of non-local information is known to benefit denoising, in this work we investigate the use of super neurons for both synthetic and real-world image denoising.
ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
Two well-known problems related to device mobility are handover prediction and access point selection.
Early Myocardial Infarction Detection over Multi-view Echocardiography
Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in.
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
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 accuracy for classifying patient-specific ECG are still few.
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
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 acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors.
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs.
Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain.
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation
For a special case where the CS operation is set as a single tensor multiplication, the model is reduced to the learning-based separable CS; while a dense CS matrix can be approximated and learned as the summation of multiple tensors.
Super Neurons
In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection.
A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images
Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU.
Representation Based Regression for Object Distance Estimation
To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR).
Self-Organized Residual Blocks for Image Super-Resolution
It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR).
Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression
In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space.
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography
Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI.
COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images.
BM3D vs 2-Layer ONN
Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs.
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising
Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution.
EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system.
Detection and severity classification of COVID-19 in CT images using deep learning
Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94. 13% and IoU of 91. 85% using the FPN model with the DenseNet201 encoder.
Reliable COVID-19 Detection Using Chest X-ray Images
To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects.
Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network
Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99. 83% F1-score, 99. 85% recall, and 99. 82% precision.
Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96. 29%, 96. 28%, 96. 29%, 96. 28% and 96. 27% respectively.
Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer
Automatic classification of speech commands has revolutionized human computer interactions in robotic applications.
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach.
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32. 4 million people suffer each year.
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.
Operational vs Convolutional Neural Networks for Image Denoising
Convolutional Neural Networks (CNNs) have recently become a favored technique for image denoising due to its adaptive learning ability, especially with a deep configuration.
Self-Organized Operational Neural Networks for Severe Image Restoration Problems
Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs.
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF).
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.
FastONN -- Python based open-source GPU implementation for Operational Neural Networks
Operational Neural Networks (ONNs) have recently been proposed as a special class of artificial neural networks for grid structured data.
Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images
All networks showed high COVID-19 detection sensitivity (>96%) with the segmented lung images.
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance.
An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control
In the 34 developed and 156 developing countries, there are about 132 million disabled people who need a wheelchair constituting 1. 86% of the world population.
Self-Organized Operational Neural Networks with Generative Neurons
However, Greedy Iterative Search (GIS) method, which is the search method used to find optimal operators in ONNs takes many training sessions to find a single operator set per layer.
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications
Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures.
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e. g., Compressively Sensed Measurements).
1D Convolutional Neural Networks and Applications: A Survey
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations.
3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales.
Colorectal cancer diagnosis from histology images: A comparative study
Moreover, we conduct quantitative comparative evaluations among the traditional methods, transfer learning-based methods and the proposed adaptive approach for the particular task of cancer detection and identification from scarce and low-resolution histology images.
1D Convolutional Neural Network Models for Sleep Arousal Detection
Sleep arousals transition the depth of sleep to a more superficial stage.
Operational Neural Networks
In order to address this drawback and also to accomplish a more generalized model over the convolutional neurons, this study proposes a novel network model, called Operational Neural Networks (ONNs), which can be heterogeneous and encapsulate neurons with any set of operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution
In this paper, we propose an evolutionary strategy to find better topologies for deep CNNs.
Progressive Operational Perceptron with Memory
Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model in the traditional Multilayer Perceptron (MLP) and this model can mimic the synaptic connections of the biological neurons that have nonlinear neurochemical behaviours.
Heterogeneous Multilayer Generalized Operational Perceptron
Previously, Generalized Operational Perceptron (GOP) was proposed to extend conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons.
Human experts vs. machines in taxa recognition
Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts ($\overline{CE}=13. 8\%$).
Limited Random Walk Algorithm for Big Graph Data Clustering
Graph clustering is an important technique to understand the relationships between the vertices in a big graph.
Social and Information Networks Physics and Society
