Souham Meshoul

• PhD
• Full Prodessor at Université Constantine 2

Translation education (TE) demands significant effort from educators due to its labor-intensive nature. Developing computational tools powered by artificial intelligence (AI) can alleviate this burden by automating repetitive tasks, allowing instructors to focus on higher-level pedagogical aspects of translation. This integration of AI has the pote...

The popularity of quadrotor Unmanned Aerial Vehicles (UAVs) stems from their simple propulsion systems and structural design. However, their complex and nonlinear dynamic behavior presents a significant challenge for control, necessitating sophisticated algorithms to ensure stability and accuracy in flight. Various strategies have been explored by...

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable chall...

The past decade has witnessed a growing demand for drone-based fire detection systems, driven by escalating concerns about wildfires exacerbated by climate change, as corroborated by environmental studies. However, deploying existing drone-based fire detection systems in real-world operational conditions poses practical challenges, notably the intr...

Problem solving applications require users to exercise caution in their data usage practices. Prior to installing these applications, users are encouraged to read and comprehend the terms of service, which address important aspects such as data privacy, processes, and policies (referred to as information elements). However, these terms are often le...

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher pe...

Cancer Research has advanced during the past few years. Using high throughput technology and advances in artificial intelligence, it is now possible to improve cancer diagnosis and targeted therapy, by integrating the investigation and analysis of clinical and omics profiles. The high dimensionality and class imbalance of the majority of available...

Access to corporate information systems by consumers via the Internet has increased dramatically over the past several decades. In a separate organization, extensive research has been conducted on the free flow of information generated by both external and internal keywords. Research on transparency should aid the audience in making informed decisi...

Information visualization refers to the practice of representing data in a meaningful, visual way that users can interpret and easily comprehend. Geometric or visual encoding shapes such as circles, rectangles, and bars have grown in popularity in data visualization research over time. Circles are a common shape used by domain experts to solve real...

The recent large outbreak of infectious diseases, such as influenza-like illnesses and COVID-19, has resulted in a flood of health-related posts on the Internet in general and on social media in particular, in a wide range of languages and dialects around the world. The obvious relationship between the number of infectious disease cases and the num...

With the help of machine learning, many of the problems that have plagued mammography in the past have been solved. Effective prediction models need many normal and tumor samples. For medical applications such as breast cancer diagnosis framework, it is difficult to gather labeled training data and construct effective learning frameworks. Transfer...

Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are...

In recent years, human–drone interaction has received increasing interest from the scientific community. When interacting with a drone, humans assume a variety of roles, the nature of which are determined by the drone’s application and degree of autonomy. Common methods of controlling drone movements include by RF remote control and ground control...

Solving dynamic optimization problems is more challenging than static ones. When a change in the objective landscape occurs, the search process may not be powerful enough to track new optima. For population based algorithms this is referred to as diversity loss problem. Furthermore, the memory of old optima becomes outdated and if not correctly dea...

Whithin the advances in highthrouput technologies, handling vast and various cancer omic data requires more accurate and felexible models to either achieve a precise clinical decision or to discover new and relevant diagnostic, prognostic and theurapeutic genes. Reverse protein phase array (RPPA) data are considered to be more stable than gene expr...

Gene regulatory network (GRN) inference is a challenging problem that lends itself to a learning task. Both positive and negative examples are needed to perform supervised and semi-supervised learning. However, GRN datasets include only positive examples and/or unlabeled ones. Recently a growing interest is being devoted to the generation of negati...

Many real-world applications require optimization in dynamic environments where the challenge is to find optima of a time-dependent objective function while tracking them over time. Many evolutionary approaches have been developed to solve Dynamic Optimization Problems (DOPs). However, there is still a need for more efficient methods. Recently, a n...

The identification of drug-target interactions (DTIs) is an important process in drug repositioning and drug discovery. However, it is very expensive and time-consuming to determine all possible DTIs with experimental approaches. Most existing machine learning-based methods formulate the DTIs prediction problem as a binary classification problem. N...

Online harassment is a major threat to users of social media platforms, especially young adults and women. It can cause mental illnesses and impacts deeply and negatively economic institutions experiencing cyberbully attacks by losing their credibility and business. This makes automatic detection of online harassment extremely important. Most of cu...

Cluster validation aims to both evaluate the results of clustering algorithms and predict the number of clusters. It is usually achieved using several indexes. Traditional internal clustering validation indexes (CVIs) are mainly based in computing pairwise distances which results in a quadratic complexity of the related algorithms. The existing CVI...

Nowadays, the Internet is becoming a platform of choice where the number of users and items grows dramatically making recommender systems (RS) the most required and widespread technology. This paper deals with context aware collaborative RS and presents a double contribution that consists of a two Dimensions Contextual Collaborative Recommender Sys...

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population t...

ABSTRACT The intensive explosion in the generation of large scale cancer gene expression data brought several computational challenges, yet opened great opportunities in exploring different pathways in order to improve cancer prognosis, diagnosis and treatment. In this paper, we propose a targeted unsupervised learning model, based on deep autoenc...

The recent progress in cancer diagnosis is genomic data analysis oriented. miRNA is playing an important role as cancer biomarkers to move with cancer diagnosis and therapy towards personalized medicine with the ultimate goal to augment survival rate and disease prevention. The recent explosion in genomic data generation has motivated the use of mi...

Social Network Analysis (SNA) is an active research topic. It arises in a broad range of fields. One important issue in SNA is the discovery of key players who are the most influential actors in a social network. Negative Key Player Problem (KPP-NEG) aims at finding the set of actors whose removal will break the social network into fragments. By an...

In this work, we developed and experimentally validated a novel model for external clustering validation to deal with huge data sets using Conditional Entropy index. The model allows clustering validation in a parallel and a distributed manner using Map-Reduce framework, it is termed MR-Centropy. The aim is to be able to scale with increasing datas...

Transcription factors are key elements in the regulation of genetic expressions. Understanding the behavior of the system is the ultimate goal behind modeling biology networks including gene regulatory networks. Prediction of regulation relationship between a transcription factor and a target gene can be viewed as a machine learning problem. Within...

Procedures that evaluate the results of clustering algorithms are known as cluster validation (CV) indexes. There exist several CV indexes usually classified into two broad classes namely external and internal clustering validation indexes depending on whether ground truth or optimal clustering solutions are known in advance or not respectively. Tr...

Inferring gene regulatory network from gene expression data is a challenging task in system biology. Elucidating the structure of these networks is a machine-learning problem. Several approaches have been proposed to address this challenge using unsupervised semi-supervised and supervised methods. Semi-supervised and supervised methods use primordi...

The accurate annotation of a protein function is important for understanding life at molecular level. Nowadays, powerful high throughput proteomics technologies provide an unprecedented understanding of the human biology and disease. These technologies are generating a deluge of protein sequences available in public databases. However, a critical c...

Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, it becomes a challenging issue due to the huge amount of data recently collected making conventional clustering algorithms inappropriate. The use of swarm intelligence algorithms has shown promising results when applied to data clustering of...

Analysis of large gene expression datasets for cancer classification is a crucial task in bioinformatics and a very challenging one as well. In this paper, we explore the potential of using advanced models in machine learning namely those based on deep learning to handle such task. For this purpose we propose a deep feed forward neural network arch...

Feature selection is a key issue in machine learning and data mining. A great deal of effort has been devoted to static feature selection. However, with the assumption that features occur over time, methods developed so far are difficult to use if not applicable. Therefore, there is a need to design new methods to deal with streaming feature select...

Recommender systems are very useful to help access to relevant information on the web and to customize search. Content based filtering (CBF) is an alternative among others used to design recommender systems by exploiting items’ contents. Basically, they recommend items based on a comparison between the content of items and user profile. Usually, th...

Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, it becomes a challenging issue due to the huge amount of data recently collected making conventional clustering algorithms inappropriate. The use of swarm intelligence algorithms has shown promising results when applied to data clustering of...

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population t...

Wireless sensor networking is a promising technology that can lead to automatic, intelligent, easier and more secure systems. A wireless sensor network (WSN) consists of small battery powered devices with limited energy resources. One of the major challenges in WSN lies in the energy constraint and computation resources available at the sensor node...

Dynamic optimization holds promise to solve real world problems that require adaptation to dynamic environments. The challenge is to track optima in an ever changing landscape. This paper describes a new computational intelligence approach to dynamic optimization termed as wind driven dynamic optimization (WD2O). Basically, it relies on an enhanced...

With the deluge of data published on the web, it becomes even more difficult for a user to get access to the relevant information based on his preferences. In order to accurately predict the preference a user would give to an item, recommender systems should use an effective information filtering engine. This task can be achieved using content base...

Inferring gene regulatory network (GRN) is one of the major challenges in bioinformatics. A great amount of gene expression data is being produced raising the issue of GRN reconstruction. This later becomes an even more difficult task to perform when the biological dataset is very large. GRN reconstruction can be achieved through clustering. In thi...

Clustering1is an essential task in many areas such as machine learning, data mining and computer vision among others. Cluster validation aims to assess the quality of partitions obtained by clustering algorithms. Several indexes have been developed for cluster validation purpose. They can be external or internal depending on the availability of gro...

One of the remarkable results of the rapid advances in information technology is the production of tremendous amounts of data sets, so large or complex that available processing methods are inadequate, among these methods cluster analysis. Clustering becomes more challenging and complex. In this paper, the authors describe a highly scalable Differe...

Cuckoo Search (CS) is a recent addition to the field of swarm-based metaheuristics. It has been shown to be an efficient approach for global optimization. Moreover, its application for solving Multi-objective Optimization (MOO) shows very promising results as well. In multi-objective context, a bounded archive is required to store the set of nondom...

Projet de fin d’études pour l’obtention du diplôme de Master Académique en Informatique

Metaheuristics have been proposed as an alternative to mathematical optimization methods to address non convex problems involving large search spaces. Within this context a new promising metaheuristic inspired from earth atmosphere phenomena and termed as Wind Driven Optimization (WDO) has been developed by Bayraktar. WDO has been successfully appl...

The Communities of Practice of E-learning (CoPEs) are virtual spaces that facilitate learning and acquisition of new knowledge for its members. To achieve these objectives CoPE members exchange and share learning resources that can be (online courses, URLs, articles, theses, etc ...). The growing number of adherents to the CoPE increases the number...

Computer aided drug discovery is a field where recent advances in technology are expected to impact in a significant way the traditional process of drug development. In this paper, an integer differential evolution (IDE) algorithm for De novo drug design is proposed. The aim is to find small molecules that are complementary in shape and charge to a...

Biomarker discovery becomes the bottle-neck of personalized medicine and has gained increasing interest from various research fields recently. Nevertheless, producing robust and accurate signatures is a crucial problem in biomarker discovery and relies heavily on the used feature selection algorithms. Feature selection is a preprocessing step which...

Clustering is an important tool in many fields such as exploratory data mining and pattern recognition. It consists in organizing a large data set into groups of objects that are more similar to each other than to those in other groups. Despite its use for over three decades, it is still subject to a lot of controversy. In this paper, we cast clust...

Data-clustering has been identified as a major problem in many areas. It aims to identify and extract meaningful groups from a very large set of data. It is a combinatorial problem, because the number of partitions that can be obtained grows exponentially with the volume of data to be classified and the number of clusters. In this paper, we deal wi...

In the last few years, researchers have dedicated growing attention to biomarker identification given due to its extreme importance in genomics and personalised medicine. In this paper, biomarker discovery is handled by means of a cooperative parallel and distributed approach based on metaheuristics. More specifically, metaheuristics are employed a...

In this paper, we propose a multi-objective algorithm to deal with motif discovery problem. This latter is a critical issue in bioinformatics, and its complexity arises from the fact that we do not know a priori what needs to be extracted and the motifs are not exact copies due to biological reasons. The key features of our algorithm are that it is...

Clustering of gene expression profiles is a mandatory task in cancer classification. Querying the expression of thousands of genes simultaneously imposes the use of powerful clustering techniques. Swarm based methods have shown their ability to perform data clustering. However, they may be faced to premature convergence problem and may be time cons...

This paper describes a new approach to deal with dynamic optimization that uses a multi-population. Its main features include the use of a modified wind driven optimization algorithm that aims to foster impact of pressure on velocities of particles. Moreover, a concept of multi-region inspired from meteorology has been introduced along with a new c...

Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however...

Cuckoo Search has been recently added to the pool of nature inspired metaheuristics. Its promising results in solving single objective optimization motivate its use in multiobjetive context. In this paper we describe a Pareto based multiobjective Cuckoo search algorithm. Like swarm based metaheuristics, the basic algorithm needs to specify the best...

Bi-level image thresholding methods can be easily extended to multilevel cases. However, extended versions are computationally expensive. In this paper, we propose first a differential evolution (DE) algorithm using Tsallis entropy as objective function. Second, we conduct a comprehensive comparative study by investigating the potential of the prop...

High level tasks in image analysis and understanding are based on accurate image segmentation which can be accomplished through multilevel thresholding. In this paper, we propose a new method that aims to determine the number of thresholds as well as their values to achieve multilevel thresholding. The method is adaptive as the number of thresholds...

The possibility to get a set of Pareto optimal solutions in a single run is one of the attracting and motivating features of using population based algorithms to solve optimization problems with multiple objectives. In this paper, constrained multi-objective problems are tackled using an extended quantum behaved particle swarm optimization. Two str...

Image segmentation can be cast as a clustering task where the image is partitioned into clusters. Pixels within the same cluster are as homogenous as possible whereas pixels belonging to different clusters are not similar in terms of an appropriate similarity measure. Several clustering methods have been proposed for image segmentation purpose amon...

Quantum behaved particle swarm optimization (QPSO) is a recently proposed metaheuristic, which describes bird flocking trajectories by a quantum behavior. It uses only one tunable parameter and suggests a new and interesting philosophy for moving in the search space. It has been successfully applied to several problems. In this paper, we investigat...

In this paper, we tackle the problem of identifying the relevant set of features that helps achieving accurate on-line signature based authentication. There exists a large set of features that can be acquired from the original signal or derived from it. Taking into account the whole set of features in the authentication process is time consuming. F...

Rapid advances in technology, that made almost everything goes digital have entailed a persistent need for a stronger means of information security. Furthermore, new advanced devices are now available to capture the dynamic of a person's signature. Therefore, the reliance on the dynamic signature for authenticating entities in secure system became...

In an attempt to improve existing evolutionary metaheuristics quantum computing principles have been used. While some of them focus on the representation scheme adopted others deal with the behavior of the underlying algorithm. In this paper, we propose a search strategy that combines the ideas of use of a chaotic search with a selection operation...

The advent of new technologies enables capturing the dynamic of a signature. This has opened a new perspective for the possible use of signatures as a basis for an authentication system that is accurate and trustworthy enough to be integrated in practical applications. Automatic online signature recognition and verification is one of the biometric...

In this paper we investigate the use of Artificial Immune Systems’ principles to cope with the satisfiability problem. We describe ClonSAT, a new iterative approach for solving the well known Maximum Satisfiability (Max-SAT) problem. This latter has been shown to be NP-hard if the number of variables per clause is greater than 3. The underlying ide...

The rapid advancements in communication, networking and mobility have entailed an urgency to further develop basic biometric capabilities to face security challenges. Online signature authentication is increasingly gaining interest thanks to the advent of high quality signature devices. In this paper, we propose a new approach for automatic authent...

In this paper, a quantum!inspired differential evol ution algorithm for solving the N!queens problem is presented. The N!queens problem aims at placing N queens on an NxN chessboard, in such a way that no queen could capture any of the others. The proposed algorithm is a novel hybridization between differential evolution algorithms and quantum comp...

The emerging field of quantum computing has recently created much interest in the computer science community due to the new concepts it suggests to store and process data. In this paper, we explore some of these concepts to cope with the data clustering problem. Data clustering is a key task for most fields like data mining and pattern recognition....

Cellular automata (CA) have been shown to be suitable for modelling and simulating complex adaptive systems. To achieve adaptation to the exterior environment, Learning Cellular Automata (LCA) have been proposed as an extension of traditional CA, which exhibit only local adaptation, in order to allow a global adaptation of the automaton to its envi...

Particle Swarm Optimization (PSO) has been successfully applied to a wide range of fields. The recent introduction of quantum mechanics principles into PSO has given rise to a Quantum behaviour PSO (QPSO) algorithm. This paper investigates its application into motif discovery, a challenging task in bioinformatics and molecular biology. Given a set...

Progressive methods for multiple sequence alignment are popular for their simplicity and cost effectiveness. However, it has been shown that they fail in locating the flanking core blocks. To cope with this issue, we describe in this paper a hybrid algorithm that aims to improve the accuracy of progressive global alignments especially in the case o...

Purpose – The purpose of this paper is to describe a work that aims to solve contour detection problem using a planar deformable model and a swarm‐based optimization technique. Contour detection is an important task in image processing as it allows depicting boundaries of objects in an image. The proposed approach uses snakes as active contour mode...

In this paper, the authors present a new approach for image processing based on reverse emergence and quantum computing. The key idea is to use cellular automata as a complex system and quantum inspired algorithms as a search strategy. Cellular automata system is a collection of many simple units that operate in parallel and interact locally with e...

Most high level interpretation tasks in image analysis rely on image registration (alignment) process. Basically, image registration consists in finding the geometric transformation that best aligns two or several images. In this paper, we focus on mono-modality image alignment. The core task to do in this case is to put into correspondence two set...

RNA structural alignment is one of key issues in bioinformatics. It aims to elucidate conserved structural regions among a set of sequences. Finding an accurate conserved structure is still difficult and a time consuming task that involves structural alignment as a prerequisite. In this work, structural alignment is viewed as an optimization proces...

Emergence is the process of deriving some new and coherent structures, patterns and properties in a complex system. Emergent phenomena occur due to interactions (non-linear and distributed) between the elements of a system over time. An important aspect concerning the emergent phenomena is that they are observable on a macroscopic level, whereas th...

The work described in this paper covers mainly the exploration of an important paradigm called amorphous computing. With the current smart systems composed of a great number of cognitive entities, amorphous computing offers useful tools and languages to emerge a coherent behavior relying on local communications and limited capabilities. In order to...

We describe a new approach for the well known problem in bioinformatics: multiple sequence alignment (MSA). MSA is fundamental task as it represents an essential platform to conduct other tasks in bioinformatics such as the construction of phylogenetic trees, the structural and functional prediction of new protein sequences. Our approach merges bet...

This paper provides a new proposal that aims to solve multi-objective optimization problems (MOP s ) using quantum evolutionary paradigm. Three main features characterize the proposed framework. In one hand, it exploits the states superposition quantum concept to derive a probabilistic representation encoding the vector of the decision variables fo...

This paper describes a novel approach to deal with multiple sequence alignment (MSA). MSA is an essential task in bioinformatics which is at the heart of denser and more complex tasks in biological sequence analysis. MSA problem still attracts researcher’s attention despite the significant research effort spent to solve it. We propose in this paper...

Résumé—Dans ce papier, nous nous intéressons à la reconnaissance de formes en utilisant un système immunitaire artificiel. Nous proposons une approche hybride qui améliore les performances de CLONCLAS précédemment développé par White et al. comme une alternative pour la reconnaissance de caractère binaires. Dans CLONCLAS un modèle unique est trouvé...

Feature point matching is a key step for most problems in computer vision. It is an ill-posed problem and suffers from combinatorial complexity which becomes even more critical with the increase in data and the presence of outliers. The work covered in this paper describes a new framework to solve this problem in order to achieve robust registratio...

Les techniques de suivi (tracking) basées modèles sont très prometteuses pour les applications de réalité augmentée. Dans ce papier, nous décrivons une méthode basée sur le suivi des primitives coins caractérisant un pattern 2D le long des séquences vidéo en vue de leur augmentation par des objets 2D. L'objectif étant de développer un outil pour as...

Alignment of multimodality images is the process that attempts to find the geometric transformation overlapping at best the common part of two images. The process requires the definition of a similarity measure and a search strategy. In the literature, several studies have shown the ability and effectiveness of entropy-based similarity measures to...