Mounim A. El Yacoubi

• Professor
• Professor (Full) at Institut Mines-Télécom

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, characterized by a wide range of motor and non-motor symptoms. Among these symptoms, alterations in speech and voice quality stand out as early and prominent indicators of the disease. Recently, the emergence of speech foundation models has revolution...

Eye movement biometrics has received increasing attention thanks to its highly secure identification. Although deep learning (DL) models have succeeded in eye movement recognition, their architectures largely rely on human prior knowledge. Differentiable Neural Architecture Search (DARTS) automates the manual architecture design process with high s...

Eye movement is a new, highly secure biometric behavioral modality that has received increasing attention in recent years. Although deep neural networks, such as convolutional neural networks (CNNs), have recently achieved promising performance (e.g., achieving the highest recognition accuracy on GazeBase database), current solutions fail to captur...

Article The Promise of Applying Machine Learning Techniques to Network Function Virtualization Houda Jmila 1, Mohamed Ibn Khedher 2,*, and Mounim A. El-Yacoubi 3 1 Institute LIST, CEA, Paris-Saclay University, 91190 Palaiseau, France 2 IRT-SystemX, 2 Bd Thomas Gobert, 91120 Palaiseau, France 3 Samovar, Telecom SudParis, Institut Polytechnique de Pa...

In broad terms, accessibility measures opportunities reachable (such as shops, residents, etc.) within a given time frame. Urban Rail Transit (URT) plays a crucial role in providing accessibility, but it is susceptible to disruptions. In city centers with dense public transport (PT) networks, travelers can often find alternative lines. However, in...

Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital. Handwriting, often affected early in AD, offers a non-invasive and cost-effective way to capture subtle motor changes. State-of-the-art research on handwriting, mostly online, based AD detection has predominantly relied on manually extracted features...

Designing a network (e.g., a telecommunication or transport network) is mainly done offline, in a planning phase, prior to the operation of the network. On the other hand, a massive effort has been devoted to characterizing dynamic networks, i.e., those that evolve over time. The novelty of this paper is that we introduce a method for the online de...

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer’s disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer’s disease detection, the widespread adoption of deep learning models raises concerns...

Non-invasive crop analysis through image-based methods holds great promise for applications in plant research, yet accurate and robust trait inference from images remains a critical challenge. Our study investigates the potential of AI model ensembling and hybridization approaches to infer sorghum crop traits from RGB images generated via unmanned...

Eye movement biometrics has received increasing attention thanks to its high secure identification. Although deep learning (DL) models have been recently successfully applied for eye movement recognition, the DL architecture still is determined by human prior knowledge. Differentiable Neural Architecture Search (DARTS) automates the manual process...

Eye movement biometrics is a secure and innovative identification method. Deep learning methods have shown good performance, but their network architecture relies on manual design and combined priori knowledge. To address these issues, we introduce automated network search (NAS) algorithms to the field of eye movement recognition and present Relax...

Deep neural networks have recently achieved promising performance in the vein recognition task and have shown an increasing application trend, however, they are prone to adversarial perturbation attacks by adding imperceptible perturbations to the input, resulting in making incorrect recognition. To address this issue, we propose a novel defense mo...

Due to the advantages such as high security, high privacy, and liveness recognition, vein recognition has been received more and more attention in past years. Recently, deep learning models, e.g., Mamba has shown robust feature representation with linear computational complexity and successfully applied for visual tasks. However, vision Manba can c...

Person re-identification is still an open challenging task in various fields due to numerous factors, including illumination changes, background clutter, pose state variations and cloth changes. Several approaches have been suggested to address this problem in the context of deep learning. Gen-erative models, particularly Variational Autoencoders (...

Palm vein recognition has attracted recently wide attention thanks to its robust feature representation and high accuracy. Despite advancements in the literature, however, existing solutions suffer from the following issues: 1) Insufficient large-scale data for deep learning-based recognition of vein biometrics, resulting in decreased generalizatio...

Biomechanical features describing movements and poses of athletes have been proposed by experts to help study athletic performances, but the traditional way of measuring those features are high-cost, time-consuming and intrusive. In this paper, we propose a deep learning-based method that can estimate athletic biomechanical features from typical br...

Hypomimia, a symptom of Parkinson's disease (PD), is marked by reduced facial movements and loss of face emotional expressions. This study focuses on identifying hypomimia in individuals with early-stage PD using optical-flow-based video vision transformer. Our study included video recordings from 109 PD and 45 healthy control (HC) subjects with an...

For data augmentation (DA), Generative Adversar-ial Networks (GANs) are typically integrated with CNNs or MLPs to generate samples in classification and segmentation tasks. For classification, categorical ground truth is leveraged in conditional GANs to generate samples for each class. For regression, data generation becomes complex as the aim now...

Alzheimer's disease is a neurodegenerative disorder defined by memory loss and primarily affects older individuals. Currently, there is no definitive cure available. Although medications are accessible, they only serve to slow the progression of the disease. In this paper, we propose the use of Vision Transformers and Transfer Learning for Alzheime...

Predicting strokes is essential for improving healthcare outcomes and saving lives. This paper introduces a benchmarking dataset, PredictStr, specifically developed to enhance stroke prediction. This dataset improves upon a previously unique dataset identified in the literature. Our methodology comprises two main steps: firstly, we outline a series...

Language impairment is a key biomarker for neurodegenerative diseases such as Alzheimer's disease (AD). With the rapid growth of Large Language Models, natural language processing (NLP) has become a preferred modality for the early prediction of AD from speech. In this work, we propose a two-stage process for early detection of AD from transcriptio...

Alzheimer's disease (AD) is a chronic and irreversible neurological disorder, making early detection essential for managing its progression. This study investigates the coherence of SHAP values with medical scientific truth. It examines three types of features: clinical, demographic, and FreeSurfer extracted from MRI scans. A set of six ML classifi...

Digital device technologies, such as wearable gait sensors, voice and video recordings, bear potential for monitoring symptoms of chronic and increasingly prevalent diseases, such as Parkinson's Disease. This could facilitate a more personalised and higher quality treatment in the future. As part of the EU-wide project DIGIPD, we confirmed this pot...

Mixup data augmentation approaches have been applied for various tasks of deep learning to improve the generalization ability of deep neural networks. Some existing approaches CutMix, SaliencyMix, etc. randomly replace a patch in one image with patches from another to generate the mixed image. Similarly, the corresponding labels are linearly combin...

Person re-identification is still an open challenging task in various fields due to numerous factors, including illumination changes, background clutter, pose state variations and cloth changes. Several approaches have been suggested to address this problem in the context of deep learning. Generative models, particularly Variational Autoencoders (V...

In recent years, vein biometrics has gained significant attention due to its high security and privacy features. While deep neural networks have become the predominant classification approaches for their ability to automatically extract discriminative vein features, they still face certain drawbacks: 1) Existing transformer-based vein classifiers s...

As a representative of a new generation of biometrics, vein identification technology offers a high level of security and convenience. Convolutional neural networks (CNNs), a prominent class of deep learning architectures, have been extensively utilized for vein identification. Since their performance and robustness are limited by small Effective R...

Digital device technologies, such as wearable gait sensors, voice and video recordings, bear potential for monitoring symptoms of chronic and increasingly prevalent diseases, such as Parkinson's Disease. This could facilitate a more personalized and higher quality treatment in the future. As part of the EU-wide project DIGIPD, we confirmed this pot...

Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup cla...

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer’s disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer’s disease detection, the widespread adoption of deep learning models raises concerns...

SHORT SUMMARY What-to methods for the design of Public Transport (PT) traditionally maximize overall efficiency. They do not generally embed the inequality of the distribution of accessibility into the optimization objective. However, such inequality is crucial, as it contributes to the car-dependency of areas underserved by PT. In fact, while ineq...

Vein recognition has received increasing attention due to its high security and privacy. Recently, deep neural networks such as Convolutional neural networks (CNN) and Transformers have been introduced for vein recognition and achieved state-of-the-art performance. Despite the recent advances , however, existing solutions for finger-vein feature ex...

Eye movement (EM) is a new highly secure bio-metric behavioral modality that has received increasing attention in recent years. Although deep neural networks, such as convolutional neural network (CNN), have recently achieved promising performance, current solutions fail to capture local and global temporal dependencies within eye movement data. To...

Finger-vein biometrics has attracted significant attention in recent years, posing a challenge in extracting robust finger-vein patterns from raw images with limited prior knowledge. While deep learning-based models, particularly convolutional neural network (CNN)-based methods, exhibit substantial capacity for feature representation, they still su...

The water crisis, global warming and climate changes have become recently prominent world issues. Saving and conserving water have become, therefore, an imperative for water resources’ sustainability. In this context, dramatic innovations have come to light, e.g., Precision Agriculture. Numerous research projects on this subject have been conducted...

Finger-vein biometrics has recently gained significant attention due to its robust privacy and high security features. Despite notable advancements, most existing methods focus on extracting features from a 2-dimensional (2D) image projected from 3D vein vessels with a single view. However, recognition based on a single view is prone to errors due...

We propose, in this paper, a comprehensive study on risk assessment related to car insurance, based on claim and telematics data, collected from a dataset of voluntary drivers. Our work addresses experimental settings not covered before in the state of the art, such as the collection of telematic data within a period significantly after the claim r...

Neural networks serve as a crucial role in critical tasks, where erroneous outputs can have severe consequences. Traditionally, the validation of neural networks has focused on evaluating their performance across a large set of input points to ensure desired outputs. However, due to the virtually infinite cardinality of the input space, it becomes...

Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup cla...

Finger-vein recognition has attracted extensive attention due to its exceptional level of security and privacy. Recently, deep neural networks (DNNs), such as convolutional neural networks (CNNs) showing robust capacity for feature representation, have been proposed for vein recognition. The architectures of these DNNs, however, have primarily been...

Palm-vein identification is a highly secure pattern biometrics that has become an active research area in recent years. Despite the recent progress in deep neural networks (DNNs) for vein identification, existing solutions for feature representation continue to lack robustness due to the limited training samples. To address this limitation, data au...

The EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging. One of the core aspects of the system is its human sensing capabilities, allowing for the perception of emotional states to provide a personalized experience. This paper outlines the d...

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literat...

Accessibility measures how well a location is connected to surrounding opportunities. We focus on accessibility provided by Public Transit (PT). There is an evident inequality in the distribution of accessibility between city centers or close to main transportation corridors and suburbs. In the latter, poor PT service leads to a chronic car-depende...

In recent years, finger-vein biometrics has attracted extensive attention due to its potential for accurate and efficient identification. Deep neural networks (DNNs) have proven effective in automatically extracting discriminative features from large collections of finger-vein images, resulting in improving the accuracy and efficiency of finger-vei...

The necessity of implementing new options to improve telemedicine has gained much importance in recent years. Among all the available technologies, photoplethysmography is turning out to be a promising resource. Its cost effectiveness and its usability allow its embedding in several devices without the need of constraining requirements. In this pap...

Background: Hypomimia is a symptom of Parkinson's disease (PD), characterized by a decrease in facial movements and loss of face emotional expressions. This study aims to detect hypomimia in participants with early-stage PD based on facial action units (AUs). Methods: A total of 299 video recordings were included, consisting of 208 PD subjects and...

Retinopathy is one of the most common micro vascular impairments in diabetic subjects. Elevated blood glucose leads to capillary occlusion, provoking the uncontrolled increase in local growth of new vessels in the retina. When left untreated, it can lead to blindness. Traditional approaches for retinopathy detection require expensive devices and hi...

Athlete's pose acquisition and analysis is promising to provide coaches with details of athletes performance and thus help to improve athletes' performances with more detailed supervision from coaches. Compared with traditional ways of acquiring an athlete's gesture, such as using wearable sensors, computer vision technology has advantages of low-c...

Over the last years, sensor-based continuous authentication on mobile devices has achieved great success on personal information protection. These proposed mechanisms, however, require both legal and illegal users' data for authentication model training, which takes time and is impractical. In this paper, we present MAuGANs, a lightweight and pract...

Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variations in the microcirculation. PPG technology is widely used in a variety of wearable sensors to investigate the cardiovascular system. Recent studies have demonstrated the utility of PPG analysis for carrying out large-scale screening...

Deep neural networks have been widely used in several complex tasks such as robotics, self-driving cars, medicine, etc. However, they have recently shown to be vulnerable in uncertain environments where inputs are noisy. As a consequence, the robustness of neural networks has become an essential property for their application in critical systems. R...

Vein biometrics is a high security and privacy preserving identification technology that has received increasing attentions. Although deep neural networks (DNNs), such as convolutional neural network (CNN), have been investigated for vein recognition and achieved a significant improvement in accuracy, they still fail to model long-range pixel depen...

With the enormous technological advances of recent years, the amount of digitized historical documents, both handwritten and printed, has increased. It is well known that digital historical documents are not easily processed in their original form, but they need to be transformed into a readable form in order to be automatically understood by comp...

Vein biometrics is a high security and privacy preserving identification technology that has attracted increasing attention over the last decade. Deep neural networks (DNNs), such as convolutional neural networks (CNN), have shown strong capabilities for robust feature representation, and have achieved, as a result, state-of-the-art performance on...

Building upon the recent advances and successes in the application of deep learning to the medical field, we propose in this work a new approach to detect and classify early-stage Alzheimer patients using online handwriting (HW) loop patterns. To cope with the lack of training data prevalent in the tasks of classification of neuro-degenerative dise...

Multi-view finger-vein recognition technology has attracted increasing attentions in recent years. Despite recent advances in the multi-view finger-vein identification, existing solutions employ multiple monocular cameras from different views to record two-dimensional (2D) projections of 3D vein vessels, which causes the following problems: 1) 2D i...

Palm-vein recognition has been the focus of large research efforts over the last years. However, despite the effectiveness of deep learning models, in particular Convolutional Neural Networks (CNNs), in automatically learning robust feature representations, thereby obtaining good accuracy, such good performance is usually obtained at the expense of...

(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...

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive De...

Recently, Generative Adversarial Networks (GANs) have been widely applied for data augmentation given limited datasets. The state of the art is dominated by measures evaluating the quality of the generated images, that are typically all added to the training dataset. There is however no control of the generated data, in terms of the compromise betw...

This two-volume set constitutes the proceedings of the Third International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022, which took place in Paris, France, in June 2022.The 98 full papers presented were carefully reviewed and selected from 192 submissions. The papers present new advances in the field of pattern recogni...

Detection of Alzheimer's disease from handwritten features using a photonic reservoir computer.

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...

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...

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...

Although it has received increasing researchers’ attention in recent years, palm-vein recognition still 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 chapter proposes an end-to-end genera...

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...

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...

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...

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...

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...

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...

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...