Browsing Academic by Author "Gabbouj, Moncef"
Now showing items 21-40 of 42
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Improved Domain Adaptation Approach for Bearing Fault Diagnosis
( IEEE Computer Society , 2022 , Conference Paper)Application of domain adaptation techniques to predictive maintenance of modern electric rotating machinery (RM) has significant potential with the goal of transferring or adaptation of a fault diagnosis model developed ... -
Joint K-Means quantization for Approximate Nearest Neighbor Search
( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces ... -
Joint learning and optimization for Federated Learning in NOMA-based networks
( Elsevier , 2023 , Article)Over the past decade, the usage of machine learning (ML) techniques have increased substantially in different applications. Federated Learning (FL) refers to collaborative techniques that avoid the exchange of raw data ... -
Learned vs. engineered features for fine-grained classification of aquatic macroinvertebrates
( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various ... -
Learning graph affinities for spectral graph-based salient object detection
( Elsevier Ltd , 2017 , Article)In this paper, we propose a novel method for learning graph affinities for salient object detection. First, we assume that a graph representation of an image is given with a predetermined connectivity rule and representative ... -
Learning to rank salient segments extracted by multispectral Quantum Cuts
( Elsevier B.V. , 2016 , Article)and third, multispectral approach is followed to generate multiple proposals instead of a single proposal as in Quantum Cuts. The proposed learn-to-rank algorithm is then applied to these multiple proposals in order to ... -
Limited random walk algorithm for big graph data clustering
( SpringerOpen , 2016 , Article)Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the ... -
Medical image analysis
( Elsevier , 2022 , Book chapter)This chapter presents deep learning methodologies for medical imaging tasks. The chapter starts with echocardiography for early detection of myocardial infarction (MI) or commonly known as heart attack. Early and fundamental ... -
OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETECTION USING CHEST X-RAY IMAGES
( IEEE , 2022 , Conference Paper)Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets ... -
Outlier edge detection using random graph generation models and applications
( Springer , 2017 , Article)Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or ... -
Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias
( Nature Publishing Group , 2017 , Article)Each year more than 7 million people die from cardiac arrhythmias. Yet no robust solution exists today to detect such heart anomalies right at the moment they occur. The purpose of this study was to design a personalized ... -
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
( Institute of Electrical and Electronics Engineers Inc. , 2016 , Article)Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such ... -
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
( IEEE Computer Society , 2016 , Article)Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently ... -
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
( Academic Press , 2017 , Article)Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has ... -
RELIABLE COVID-19 DETECTION USING CHEST X-RAY IMAGES
( IEEE Computer Society , 2021 , Conference Paper)Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest ... -
Representation based regression for object distance estimation
( Elsevier , 2023 , Article)In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs ... -
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
( Institute of Electrical and Electronics Engineers Inc. , 2022 , Article)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) ... -
Salient object segmentation based on linearly combined affinity graphs
( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is ... -
Self-organizing binary encoding for approximate nearest neighbor search
( European Signal Processing Conference, EUSIPCO , 2016 , Conference Paper)Approximate Nearest Neighbor (ANN) search for indexing and retrieval has become very popular with the recent growth of the databases in both size and dimension. In this paper, we propose a novel method for fast approximate ... -
SRL-SOA: SELF-REPRESENTATION LEARNING WITH SPARSE 1D-OPERATIONAL AUTOENCODER FOR HYPERSPECTRAL IMAGE BAND SELECTION
( IEEE Computer Society , 2022 , Conference Paper)The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy. In this work, we propose a novel framework for the band selection ...
