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  • Mounim A. El Yacoubi
Mounim A. El Yacoubi

Mounim A. El Yacoubi
Institut Mines-Télécom | telecom-sudparis.eu · SAMOVAR UMR 5157: Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux

Professor

About

139
Publications
29,848
Reads
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1,747
Citations
Additional affiliations
June 2019 - present
Institu Polytechnique de Paris
Position
  • Professor
January 2015 - May 2019
Université Paris-Saclay
Position
  • Professor (Full)
June 2008 - February 2021
Institut Mines-Télécom
Position
  • Professor (Full)

Publications

Publications (139)
Article
Full-text available
(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects...
Conference Paper
Detection of Alzheimer's disease from handwritten features using a photonic reservoir computer.
Article
Full-text available
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside...
Article
Artificial Neural Networks (ANNs) have amassed unprecedented success in information processing ranging from image recognition to time series prediction. The success can largely be attributed to the availability of large datasets for training and the increased complexity of the models. Unfortunately, for some applications only a limited amount of sa...
Conference Paper
Recently, Deep Learning models, such as Deep Convolutional Neural Networks (CNNs), have shown remarkable performance on various Computer Vision tasks. Unfortunately, many application domains, such as agriculture image analysis, do not have access to large datasets. In this study, we are interested in the prediction of soil moisture dissipation rate...
Article
Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-base...
Conference Paper
Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variation inside the micro-circulation. PPG technology is widely used in a variety of clinical and non-clinical devices in order to investigate the cardiovascular system. One example of clinical PPG device is the pulse oxymeter, while non-c...
Article
The adoption of deep learning in healthcare is hindered by their “black box” nature. In this paper, we explore the RETAIN architecture for the task of glucose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB a...
Article
The standard way to train neural-network-based solutions in healthcare does not consider clinical criteria, leading to models that are not necessarily clinically acceptable. In this study, we look at this problem from the perspective of the forecasting of future glucose values of people with diabetes. We propose a new training methodology that achi...
Article
Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples. These solutions, therefore, still lack the capability to extract robust and discriminative hand-vein features from a single training image sample. To overcome this problem, we...
Chapter
We present a vision-based activity recognition system for centrally connected humanoid robots. The robots interact with several human participants who have varying behavioral styles and inter-activity-variability. A cloud server provides and updates the recognition model in all robots. The server continuously fetches the new activity videos recorde...
Article
Background and objectives: Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on...
Chapter
Progress in the biomedical field through the use of deep learning is hindered by the lack of interpretability of the models. In this paper, we study the RETAIN architecture for the forecasting of future glucose values for diabetic people. Thanks to its two-level attention mechanism, the RETAIN model is interpretable while remaining as efficient as...
Chapter
Palm-vein recognition has received increasing researchers’ attention in recent years. However, palm-vein recognition faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this paper proposes an end-to...
Poster
Full-text available
Intrusion detection is an important Cyber Security task. Machine learning (ML) algorithms are often used to calssify the network traffic and detect abnormal behavior. The performance of such algorithms heavily depend on the representation of the network traffic data. Representation learning (RL) consists in learning better features representation b...
Poster
Full-text available
We present in this poster a theoretical formalization of the abstract operator of the Leaky ReLU function as an activation function in a deep neural network. This work is part of the verification of the robustness of neural networks by interpretation. To validate our formulation, we present our implementation of this function within the most powerf...
Preprint
Standard objective functions used during the training of neural-network-based predictive models do not consider clinical criteria, leading to models that are not necessarily clinically acceptable. In this study, we look at this problem from the perspective of the forecasting of future glucose values for diabetic people. In this study, we propose th...
Thesis
Full-text available
This report paper presents a study on detecting intrusion based on machine learning. The study was performed on the Secure Water Treatment (SWaT) testbed, a scaled-down version of a real-world industrial water treatment plant. We compare two methods : the first one using only a long short-term memory (LSTM) network adapted to time series, and the s...
Preprint
Full-text available
The adoption of deep learning in healthcare is hindered by their "black box" nature. In this paper, we explore the RETAIN architecture for the task of glusose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB a...
Preprint
Full-text available
In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error. We apply this idea to the pr...
Preprint
Full-text available
Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neur...
Preprint
Full-text available
This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starti...
Preprint
Full-text available
Progress in the biomedical field through the use of deep learning is hindered by the lack of interpretability of the models. In this paper, we study the RETAIN architecture for the forecasting of future glucose values for diabetic people. Thanks to its two-level attention mechanism, the RETAIN model is interpretable while remaining as efficient as...
Thesis
Full-text available
We present in this report a theoretical formalization of the abstract operator of the Leaky ReLU function as an activation function in a deep neural network. This work is part of the verification of the robustness of neural networks by interpretation. To validate our formulation, we present our implementation of this function within the most powerf...
Preprint
Full-text available
Due to the sensitive nature of diabetes-related data, preventing them from being shared between studies, progress in the field of glucose prediction is hard to assess. To address this issue, we present GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine-learning-based glucose-predictive models. To ensure the reproducibility of the resul...
Preprint
Full-text available
Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity throug...
Conference Paper
Word Spotting of Historical Arabic Documents is a challenging task due to the complexity of document layouts. This paper proposes a novel word spotting approach that consists of learning feature representation to describe word images. The objective is to investigate optimal embedding spaces to extract a discriminative word image representation. The...
Chapter
In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error.
Chapter
Full-text available
Intrusion detection is a critical Cyber Security subject. Different Machine Learning (ML) approaches have been proposed for Intrusion Detection Systems (IDS). However, their application to real-life scenarios remains challenging due to high data dimensionality. Representation learning (RL) allows discriminative feature representation in a low dimen...
Conference Paper
This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starti...
Article
Full-text available
Hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. A variety of hashing methods have been developed for learning an efficient binary data representation, mainly by relaxing some imposed constraints during hash function learning. Although they have achieved good accuracy-speed trade-off, the...
Article
Hand-vein biometrics as a high security pattern has received more and more attention. One of the open issues in hand-vein verification is the lack of robustness against image quality degradation, which may comprise the verification accuracy. To achieve robust verification, vein feature extraction approach, especially vein texture segmentation, has...
Article
Full-text available
Finger-vein verification is a highly secure biometric authentication that has been widely investigated over the last years. One of its challenges, however, is the possible degradation of image quality, that results in spurious and missing vein patterns, which increases the verification error. Despite recent advances in finger-vein quality assessmen...
Article
Full-text available
Fast urbanization generates increasing amounts of travel flows, urging the need for efficient transport planning policies. In parallel, mobile phone data have emerged as the largest mobility data source, but are not yet integrated to transport planning models. Currently, transport authorities are lacking a global picture of daily passenger flows on...
Chapter
Large-scale and real-time transport mode detection is an open challenge for smart transport research. Although massive mobility data is collected from smartphones, mining mobile network geolocation is non-trivial as it is a sparse, coarse and noisy data for which real transport labels are unknown. In this study, we process billions of Call Detail R...
Article
Communication-enabled devices routinely carried by individuals have become pervasive, opening unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by...
Article
Full-text available
Background: Because of the rich set of spatiotemporal features it allows to extract, online handwriting is being increasingly investigated for characterizing neurodegenerative diseases like Parkinson and Alzheimer. The state of the art on the latter is dominated by methods that extract global (average) kinematic parameters, and then apply basic cla...
Article
Background: The goal of this work is to develop a non-invasive method in order to help detecting Alzheimer's disease in its early stages, by implementing voice analysis techniques based on machine learning algorithms. Methods: We extract temporal and acoustical voice features (e.g. Jitter and Harmonics-to-Noise Ratio) from read speech of patients i...
Conference Paper
Large-scale and real-time transport mode detection is an open challenge for smart transport research. Although massive mobility data is collected from smartphones, mining mobile network geolocation is non-trivial as it is a sparse, coarse and noisy data for which real transport labels are unknown. In this study, we process billions of Call Detail R...
Article
We present, in this paper, a novel paradigm for assessing Alzheimer's disease and aging by analyzing impairment of handwriting (HW) on tablets, a challenging problem that is still in its infancy. The state of the art is dominated by methods that assume a unique behavioral trend for each cognitive profile or age group, and that extract global kinema...
Conference Paper
The paper proposes a novel video-based person re-identification system that consists of describing a person using both Interest Points (IP) and Image-based features. The Image-based descriptor extracts global image representation that includes the silhouette but also possibly extra objects (i.e animal, stroller, etc) while the IP-based descriptor e...
Conference Paper
Full-text available
The paper proposes a novel video-based person re-identification system that consists of describing a person using both Interest Points (IP) and Image-based features. The Image-based descriptor extracts global image representation that includes the silhouette but also possibly extra objects (i.e animal, stroller, etc) while the IP-based descriptor e...
Article
Full-text available
Introduction L’écriture est un processus cognitif qui résulte de l’interaction entre plusieurs facteurs du développement cognitifs, linguistiques, et psychomoteurs. De ce fait, la détérioration de celle-ci est un signe de dysfonctionnement dans l’une des sphères cognitives. Objectifs Notre objectif est de développer une technique innovante, permet...
Article
Communication-enabled devices that are physically carried by individuals are today pervasive, which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata c...
Article
Full-text available
Handwriting recognition is a mature technology with numerous successful applications. By contrast, handwriting analysis for health assessment is more recent and has attracted far less research efforts. In this work, we propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike...
Conference Paper
Full-text available
The recognition of human actions based on three-dimensional depth data has become a very active research field in computer vision. In this paper, we study the fusion at the feature and decision levels for depth data captured by a Kinect camera to improve action recognition. More precisely, from each depth video sequence, we compute Depth Motion Map...
Conference Paper
Full-text available
This paper provides an extended comparison of two temporal models for gesture recognition, namely Hybrid Neural Network-Hidden Markov Models (NN-HMM) and Recurrent Neural Networks (RNN) which have lately claimed the state-the-art performances. Experiments were conducted on both models in the same body of work, with similar representation learning c...
Conference Paper
Full-text available
Resource Management in the network function virtualization (NFV) environment is a challenging task. The continuously varying demands of virtual network functions (VNF) call for dynamic algorithms to efficiently scale the allocated resources and meet fluctuating needs. In this context, studying the behavior of a VNF as a function of its environment...
Patent
La présente invention concerne un procédé d'estimation de la trajectoire d'un utilisateur se déplaçant sur des axes de transport au sein d'une zone géographique, notamment des axes de transport routiers et/ou publics, le procédé utilisant un appareil mobile (2) porté par l'utilisateur et relié à un réseau de télécommunications mobiles, et des donné...
Conference Paper
We intend to develop a non-invasive method in order to diagnose Alzheimer’s disease in its early stages by implementing voice analysis techniques. For this purpose, we extract voice features from read speech of patients diagnosed as having Alzheimer’s disease and mild cognitive impairment versus healthy control group at the AP-HP Broca Hospital. Tw...
Conference Paper
Full-text available
Dans cette étude, nous analysons l’écriture manuscrite acquise sur tablette graphique de patients touchés par la maladie d’Alzheimer (MA) à un stade précoce, et de Troubles Cognitifs Légers (TCL), suivis à l’hôpital Broca. L’objectif est de caractériser par des paramètres spatio-temporels de l’écriture la maladie d’Alzheimer à ses débuts. Nous prop...
Conference Paper
The aim of active and assisted living (AAL) is to develop tools to assist the elderly people in the ageing status. Human posture recognition algorithms can help monitor aged people in home environments. Different types of sensors can be used for such a task. A case in point is the RGBD sensors, which are cost-effective and provide rich information...
Article
Full-text available
Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in fingervein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features...
Conference Paper
Full-text available
As elderly care is getting more and more important, monitoring of activity of daily living (ADL) has become an active research topic. Both robotic and pervasive computing domains, through smart homes, are creating opportunities to move forward in ADL field. Multiple techniques were proposed to identify activities, each with their features, advantag...
Article
Full-text available
Finger-vein biometrics has been extensively investigated for personal authentication. One of the open issues in finger-vein verification is the lack of robustness against image quality degradation. Spurious and missing features in poor quality images may degrade the system performance. Despite recent advances in finger-vein quality assessment, curr...
Article
We present in this paper a multi-shot human re-identification system from video sequences based on interest points (IPs) matching. Our contribution is to take advantage of the complementary of person’s appearance and style of its movement that leads to a more robust description with respect to various complexity factors. The proposed contributions...