Lhassane Idoumghar

• Prof. (Exceptional-class)
• Professor (Full) at University of Upper Alsace

This paper investigates the Electric Vehicle Charging Scheduling Problem (EVCSP) to maximize satisfied charging demands while optimizing resource utilization. Specifically, it addresses scenarios involving non-identical chargers with constant and variable output power, as well as introducing a novel configuration featuring chargers with discrete va...

This study presents a novel multi-objective optimization model for electric vehicle (EV) charging scheduling, addressing the trade-off between maximizing the energy delivered to clients and minimizing peak energy consumption. The model incorporates real-world constraints, including limited charging infrastructure, heterogeneous charging power level...

Lithium-ion battery (LIB) health estimation is essential for battery management systems to function properly. In this paper, a technique for co-estimating the state of health (SOH) and the state of charge (SOC) for LIBs through the widely used data-driven approaches is provided, as their dependability and flexibility greatly depend on the selected...

Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely employed to tackle problems with multiple conflicting objectives. Recent research indicates that not all objectives are equally important to the decision-maker (DM). In the context of interactive EMOAs, preference information elicited from the DM during the optimization process...

Automatic analysis of medical images using machine learning techniques has gained significant importance over the years. A large number of approaches have been proposed for solving different medical image analysis tasks using machine learning and deep learning approaches. These approaches are quite effective thanks to their ability to analyze large...

Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information is crucial and might b...

This paper addresses the pressing issue of efficiently scheduling electric vehicle (EV) charging at public stations to alleviate strain on the electrical grid. EV drivers provide their charging needs beforehand, and the scheduler optimizes charger allocation and power distribution to minimize discrepancies in state-of-charge levels at departure. To...

This paper introduces a new Distributed State Variable Estimation Algorithm (DSVEA). This algorithm is developed based on the Kalman filter for estimating the state variables of large-scale systems. This new approach involves decomposing the large-scale state estimation problem into several interconnected local sub-estimation problems. To demonstra...

An essential aspect of pattern recognition pertains to handwriting recognition, particularly in languages with diverse character styles like Arabic. Arabic characters present a challenge due to their varied writing styles, intricate interconnections within words, and shape modifications based on position. The complexity of Arabic calligraphy furthe...

Unmanned Aerial Vehicles (UAVs) are ideally suited for many real-world applications ranging from scientific to commercial, industrial, and military fields. Enhancing the efficiency of UAV-based missions through optimization techniques is of paramount significance. Unmanned Aerial Vehicles (UAVs) are ideally suited for many real-world applications r...

Recent studies have demonstrated that Convolutional Neural Network (CNN) architectures are sensitive to adversarial attacks with imperceptible permutations. Adversarial attacks on medical images may cause manipulated decisions and decrease the performance of the diagnosis system. The robustness of medical systems is crucial, as it assures an improv...

In the last years, the world has witnessed a potential increasing in the patient number resulted from the increasing number of aged persons along with the emergence of new virus and diseases. This imposes a high pressure on hospitals that suffer from a shortage of medical staff, personal equipment and adequate interventions to overcome such a chall...

This paper proposes a distributed joint parameter and state variables estimation algorithm for large-scale state-space interconnected systems. In this distributed estimation scheme, each interconnected sub-system is described by a linear discrete-time state space mathematical model. Each sub-system is supposed to be controlled by an intelligent con...

Nowadays, healthcare takes a great attention from people and governments due to the emergence of new diseases and viruses. Health Information Exchange (HIE) allows doctors, clinicians, and healthcare facilities (HCF) to exchange and share patient records according to patient permission. Unfortunately, the HIE systems suffer from several challenges...

Nowadays, Healthcare Information Exchange (HIE) plays a vital role in healthcare systems; it allows healthcare providers to access and share patient medical data electronically and securely. Subsequently, HIE eliminates redundant or unnecessary testing, and improves public health reporting and monitoring. Security is a very important challenge in t...

Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of att...

Automatic diagnosis of abnormalities and diseases using medical scans consisting of different modalities (X-rays, mammograms, Optical Coherence Tomography (OCT)) is a challenging task due to changing clinical environment and varying noise levels. Manually designing deep learning architectures is a tedious task. However, Neural Architecture Search (...

This paper addresses the electric vehicle charging problem in a charging station with a limited overall power capacity and a fixed number of chargers. Electric vehicle drivers submit their charging demands. Given the limited resources, these charging demands are either accepted or rejected, and an accepted demand must be satisfied. The objective of...

This study focuses on a 3D multi-objective collision-free offline UAV path planning problem by considering the variability in flying altitude over an urban environment that is replete with static obstacles. The environment is decomposed into several equal-sized ground cells with an infinite flying altitude. The UAV can adjust the altitude to fly ab...

Developing healthy diets in early childhood may help determine future healthy foods. Many kids pass time in childcare, but few studies evaluated the nutritional quality of menus and snacks in childcare homes. Therefore, serving healthier meals is the main phase to attaining that objective. In spite of this, planning a healthful and balanced menu ma...

This study presents a comprehensive comparative analysis of several meta-heuristic optimization algorithms for solving the damage detection problem in concrete plate structures. The problem is formulated as a bounded single objective optimization problem. The performance and efficiency of the algorithms are compared under various scenarios using no...

Nowadays, health care has become a constant concern of all countries around the world, especially after the emergence of the Coronavirus (COVID-19) and all its variants. Billions of dollars are being paid through the World Health Organization to improve health care. Scientific research laboratories play a pioneering role in this area as well. Due t...

Land use and land cover and Urban Fabric (UF) mapping are very useful for urban modeling and simulation (growth, pollution, noise, micro-climate, mobility) in a context of global change. In recent years, due to the increase of Earth Observation data researchers built and shared datasets to the machine learning scientific community to apply and test...

Adopting electric vehicles is essential to reducing greenhouse gas emissions and achieving climate goals. However, the existing electrical grid can easily be overloaded with more electric vehicles on the road. For this reason, many researchers are working on developing relevant charging infrastructures that include charging scheduling strategies. T...

This paper proposes an improved variant of the harmony search (HS) algorithm called HSGS for solving clustering problems. The HSGS generates two new harmonies at each iteration. The first is produced by the harmony improvisation procedure of the canonical HS and promotes exploration ability. The second is generated by one of two new phases added to...

Nowadays, hospitals and government departments are struggling to reduce the health costs and improve the service quality. Hospitals rely mainly on nurses who have many duties including caring of patients, communicating with doctors , administering medicine and checking vital signs. Wireless body sensor network (WBSN) is one of the main applications...

Deep Learning models for time series classification are benchmarked on the UCR Archive. This archive contains 128 datasets. Unfortunately only 5 datasets contain more than 1000 training samples. For most deep learning models, this lead to over-fitting. One way to address this issue and improve the generalization of the models is data augmentation....

In the context of global change, up-to-date land use land cover (LULC) maps is a major challenge to assess pressures on natural areas. These maps also allow us to assess the evolution of land cover and to quantify changes over time (such as urban sprawl), which is essential for having a precise understanding of a given territory. Few studies have c...

Blockchain is nowadays a flourishing technology that served many domains (finance, supply chain, smart cities, healthcare, etc.) thanks to the immutability of data stored on it. In fact, before validating a block and appending it to the blockchain, most of the system nodes should agree on it and reach consensus. One of the most used consensus proto...

This paper presents a multi-objective mixed-integer non-linear programming model for a congested multiple-server discrete facility location problem with uniformly distributed demands along the network edges. Regarding the capacity of each facility and the maximum waiting time threshold, the developed model aims to determine the number and locations...

As one of the most prominent variants of the facility location problem, the p-median problem aims to determine the best locations for establishing p number of facilities such that the aggregate customers' transportation cost is minimized. Since the p-median problem is classified as NP-hard, the application of metaheuristics to solve it is inevitabl...

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper present...

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper present...

Healthcare requires the cooperation of many administrative units and medical specialties. The Internet of Things (IoT) is involved into healthcare field and plays an extremely important role by providing healthcare services. In the Internet of Healthcare Things (IoHT) several challenges appeared in terms of limited battery life, long processing tim...

This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 × 256 pixels for the Sentinel-2 L2A , Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Us...

Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of att...

Urban areas are increasing since several years as a result of development of built-up areas, network infrastructure, industrial areas or other built-up areas. This urban sprawl has a considerable impact on natural areas by changing the functioning of ecosystems. Mapping and monitoring Urban Fabrics (UF) is therefore relevant for urban planning and...

This paper proposes two ensemble strategies for the backtracking search algorithm (BSA). The first one is an ensemble of two sets of evolutionary operators that balances exploration and exploitation abilities. The second one is an ensemble of values for each parameter associated with the evolutionary operators. The second strategy provides diverse...

Optimization algorithms often have several critical setting parameters and the improvement of the empirical performance of these algorithms depends on tuning them. Manually configuration of such parameters is a tedious task that results in unsatisfactory outputs. Therefore, several automatic algorithm configuration frameworks have been proposed to...

Recent advances in hardware and communication technologies have accelerated the deployment of billions of wireless sensors. This transformation has created a wide range of applications adapted to the evolving trends of our daily life requirements. Wireless sensor networks (WSNs) could be deployed in several target areas including buildings, forests...

This paper addresses the proposition of an electrical multiport propulsion system. This traction system contains an electric motor to which a magnetic gear is attached; it is called an integrated motor-magnetic gear (IMMG) propulsion unit. This IMMG has two shafts and two levels of torque and speed. Thus, it is suited for the propulsion of an elect...

Coverage area maximization is a crucial issue that must be considered in Wireless sensor network (WSN) deployment as long as it impacts the sensor network efficiency. In this paper, a novel approach based on particle swarm optimization (PSO) and voronoi diagram is developed to solve WSN deployment problem. The objective of the proposed solution is...

In this study, we present a novel discrete bi-objective energy-efficient maximal coverage UAV path planning problem. Here, the ground space is decomposed into several equal-size cells. The flying altitude of each cell should be greater than the height of existing obstacles, if any. Considering both the possibilities of bypassing the obstacles or ad...

Technical constraints imposed by low-power and lossy networks (LLNs) require to defer complexity to routing protocols in order to efficiently and reliably transmit packets. However, despite these constraints, the deployment of this type of network has increased considerably over the last years, particularly in smart cities area with focus on sensin...

Coverage area maximization is a crucial issue that must be considered in Wireless sensor network (WSN) deployment as long as it impacts the sensor network efficiency. In this paper, a novel approach based on particle swarm optimization (PSO) and voronoi diagram is developed to solve WSN deployment problem. The objective of the proposed solution is...

The NP-hard minimum set cover problem (SCP) is a very typical model to use when attempting to formalise optimal camera placement (OCP) applications. In a generic form, the OCP problem relates to the positioning of individual cameras such that the overall network is able to cover a given area while meeting a set of application-specific requirements...

Deep neural networks have recently drawn considerable attention to build and evaluate artificial learning models for perceptual tasks. On the other hand, optimization is the problem of selecting a set of element to find an optimal/near optimal criterion. Here, we present a study on the performance of the deep learning models to deal with global opt...

The use of fossil energy has a negative impact on the climate and on the environment. For this reasons, the electric vehicles present an attractive and interesting solution to protect the Earth. However, the high cost of manufacture and the limited autonomy are the major problems of electric vehicles today. In order to solve these problems, the aut...

The electric vehicle (EV) charging scheduling problem has become a research focus to mitigate the impact of large-scale deployment of EV in the near future. One of the main assumptions in literature is that there are enough charging points (CP) in the charging station to meet all charging demands. However, with the deployment of EVs, this assumptio...

Optimization algorithms often have several critical setting parameters and the improvement of the empirical performance of these algorithms depends on tuning them. Manually configuration of such parameters is a tedious task that results in unsatisfactory outputs. Therefore, several automatic algorithm configuration frameworks have been proposed to...

When overpopulated cities face frequent crowded events like strikes, demonstrations, parades or other sorts of people gatherings, they are confronted to multiple security issues. To mitigate these issues, security forces are often involved to monitor the gatherings and to ensure the security of their participants. However, when access to technology...

Deep neural networks have recently drawn considerable attention to build and evaluate artificial learning models for perceptual tasks. Here, we present a study on the performance of the deep learning models to deal with global optimization problems. The proposed approach adopts the idea of the neural architecture search (NAS) to generate efficient...

This paper brings deep learning at the forefront of research into time series classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by...

This paper studies the electric vehicle (EV) charging scheduling problem where EV arrive at random unknown instants during the day with different charging demands and departure times. We consider single-phase charging EV in a three-phase charging station designed such that each EV has its own parking space. The objective is to build a real-time sch...

This study proposes a novel multi-objective integer programming model for a collision-free discrete drone path planning problem. Considering the possibility of bypassing obstacles or flying above them, this study aims to minimize the path length, energy consumption, and the accumulated maximum path risk simultaneously. The static environment is rep...

Today, diseases and illnesses are becoming the most dangerous enemy to humans. The number of patients is increasing day after day accompanied with the emergence of new types of viruses and diseases. Indeed, most hospitals suffer from the deficiency of qualified staff needed to continuously monitor patients and act when an urgent situation is detect...

The Unicost Set Covering Problem (USCP) is a well-known \(\mathcal {NP}\)-hard combinatorial optimization problem. This paper presents a memetic algorithm that combines and adapts the Hybrid Evolutionary Algorithm in Duet (HEAD) and the Row Weighting Local Search (RWLS) to solve the USCP. The former is a memetic approach with a population of only t...