M. BOURAHLA Mustapha

Prof

Directory of teachers

Department

Informatics Department

Research Interests

Méthodes formelles Web sémantique et ontologies Intelligence artificielle

Contact Info

University of M'Sila, Algeria

Email : mustapha.bourahla@univ-msila.dz

On the Web:

  • Google Scholar N/A
  • ResearchGate
    ResearchGate N/A
  • ORCID N/A
  • SC
    Scopus N/A

Recent Publications

2025-01-01

A Simulation-Based Behavioral Clustering Method for Crowd Evacuation Analysis

Traffic management, urban planning, and emergency management cannot be efficiently done without crowd simulation. This paper proposes a Behavioral Clustering Method (BCM), which tackles the problem of forming crowds in clusters or subgroups based on fundamental behaviors so that congestion is minimized during effective evacuation processes. We designed BCM based on synthetic data obtained from the simulation of the evacuation of a crowd in high-risk situations. Our method regards pedestrians as intelligent agents and predicts key behavioral aspects of future crowd evacuations before they occur. We use cluster analysis on those movement and behavioral data for building as well as evacuation-friendly control strategies by clustering people into subgroups of behavioral similarity. The credibility of the model is validated through Python-based animations to detect and rectify errors. Results from simulation performance evaluations indicate that BCM is successful in modeling the evolution of crowd behavior at the time of evacuation.
Citation

M. BOURAHLA Mustapha, (2025-01-01), "A Simulation-Based Behavioral Clustering Method for Crowd Evacuation Analysis", [national] YMER , University of Stockholm

Read Full Paper
2024-06-01

Quantum Convolution for Convolutional Neural Networks

Quantum machine learning has garnered a lot of attention recently due to the quick advancement of quantum technologies. For enhancing the performance of classical neural networks, a family of hybrid quantum-classical neural networks made up of both classical and quantum components has received extensive study. A new design of quantum convolutional neural networks (QCNNs), is what we propose in this research. With the help of our technique, the idea of convolution, which is frequently used in contemporary deep learning algorithms, is developed with quantum operations to be used in designing the quantum convolutional neural networks (QCNNs). While lowering the computational cost, the suggested QCNNs are able to capture more context throughout the quantum convolution process. We conduct practical studies on the keras digits dataset to perform image recognition and show that QCNN models generally outperform existing quantum convolutional neural networks (QCNNs) in terms of accuracy and loss computation.
Citation

M. BOURAHLA Mustapha, (2024-06-01), "Quantum Convolution for Convolutional Neural Networks", [national] ISEM, Springer Nature , Springer

Read Full Paper
2024-02-01

Agent-Based Simulation of Crowd Evacuation Through Complex Spaces

In this paper, we have developed a description of an agent-based model for simulating the evacuation of crowds from complex physical spaces to escape dangerous situations. This model describes a physical space that contains a set of differently shaped fences and obstacles, and an exit door. The pedestrians composing the crowd and moving in this space in order to be evacuated are described as intelligent agents with a supervised machine
learning using perception-based data to perceive particular environment differently. The description of this model is developed with the Python language where its execution represents its simulation. Before the simulation, the model can be validated using an animation written with the Python language and this to fix possible problems of model description. A model performance evaluation is presented using an analysis of simulation
results and this evaluation shows that these results are very encouraging.
Citation

M. BOURAHLA Mustapha, (2024-02-01), "Agent-Based Simulation of Crowd Evacuation Through Complex Spaces", [national] Ingénierie des Systèmes d’Information , International Information and Engineering Technology Association (IIETA)

Read Full Paper
2023-09-24

Quantum convolution for convolutional neural network

Quantum machine learning has garnered a lot of attention recently due to the quick advancement of quantum technologies. For enhancing the performance of classical neural networks, a family of hybrid quantum-classical neural networks made up of both classical and quantum components has received extensive study. A new design of quantum convolutional neural networks (QCNNs), is what we propose in this research. With the help of our technique, the idea of convolution, which is frequently used in contemporary deep learning algorithms, is developed with quantum operations to be used in designing the quantum convolutional neural networks (QCNNs). While lowering the computational cost, the suggested QCNNs are able to capture more context throughout the quantum convolution process. We conduct practical studies on the keras digits dataset to perform image recognition and show that QCNN models generally outperform existing quantum convolutional neural networks (QCNNs) in terms of accuracy and loss computation.
Citation

M. BOURAHLA Mustapha, (2023-09-24), "Quantum convolution for convolutional neural network", [international] International Symposium on Quantum Sciences: Applications and Challenges QSAC'2023 , Alger

Read Full Paper
2023-07-15

Sewer Systems Control using Internet of Things and eXplainable Artificial Intelligence

This paper presents a real-time control on sewer systems to prevent overflow using the Internet of Things and eXplainable Artificial Intelligence. The Internet of Things is used to offer continuous data to be used in two steps. In the first step, we use historical data to construct Artificial Intelligence-based prediction models to forecast future system states and then, in the second step, we use the real-time data for monitoring and control of sewer systems using the prediction models combined with eXplainable Artificial Intelligence (XAI) technique. The prediction model is used to predict the labels outputs of real-time inputs observed by sensors installed in different locations of the sewer system. These predictions will be analyzed by a technique of explainable artificial intelligence to diagnose the sewer system if an abnormal behavior is observed.
Citation

M. BOURAHLA Mustapha, (2023-07-15), "Sewer Systems Control using Internet of Things and eXplainable Artificial Intelligence", [international] Communications in Computer and Information Science , Springer , Alger

Read Full Paper
2022

Using Rough Set Theory for Reasoning on Vague Ontologies

Using Rough Set Theory for Reasoning on Vague Ontologies
Citation

M. BOURAHLA Mustapha, (2022), "Using Rough Set Theory for Reasoning on Vague Ontologies", [national] IJISA , IJISA

Read Full Paper

Monitor city-wide sewage systems using the Internet of Things and eXplainable Artificial Intelligence

Monitor city-wide sewage systems using the Internet of Things and eXplainable Artificial Intelligence
Citation

M. BOURAHLA Mustapha, (2022), "Monitor city-wide sewage systems using the Internet of Things and eXplainable Artificial Intelligence", [international] WIIS , Moldova

Read Full Paper

Formalization of Ontology Conceptualizations Using Model Transformation

Formalization of Ontology Conceptualizations Using Model Transformation
Citation

M. BOURAHLA Mustapha, (2022), "Formalization of Ontology Conceptualizations Using Model Transformation", [national] Int. J. Inf. Syst. Model. Des. , IGI Global

Read Full Paper

Approach for the development of mobile applications based on migrant objects

Approach for the development of mobile applications based on migrant objects
Citation

M. BOURAHLA Mustapha, (2022), "Approach for the development of mobile applications based on migrant objects", [national] Comput. Sci. J. Moldova , Comput. Sci. J. Moldova

Read Full Paper
2019

Classifying Non-elementary Movements in Vietnamese Mõ Dances

This paper proposes a method to classify non-elementary movements in Vietnamese dances. This classification method uses an OWL ontology called VDM (Vietnamese Dance Movements) recently developed by the authors. The VDM defines a taxonomy of dance movement classes and their relationships for the traditional Vietnamese dances taking into account the semantics of its art and its cultural anthropologists. The VDM terminology describes elementary movements (poses) as a dataset ontology importing the ontology VDM. These poses are results of dance sequences segmentation (using segmentation techniques). In this paper, we support the initial ontology VDM by complex classification rules written with SWRL (Semantic Web Rule Language, which is the OWL complementary language) to classify non-elementary movements. The objective is to entail classes of movement phrases, which are non-elementary basic movements with complete meaning and illustrated using Mõ dances. The classification result is the initial dataset VDM ontology augmented with class descriptions of non-elementary movements, which can be queried using the query language SQWRL (Semantic Query Web-enhanced Rule Language).
Citation

M. BOURAHLA Mustapha, (2019), "Classifying Non-elementary Movements in Vietnamese Mõ Dances", [international] HCI , Florida USA

Read Full Paper

Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method

The agent-based modelling is used for modelling many complex dynamic systems, especially those including autonomous individuals such as human beings' societies, animals' societies, robots, insects' societies, etc. Evacuation systems such as those needed for supermarket buildings are considered as complex dynamic systems. In these systems, we have to deal with the problem of rescuing a high number of people of different ages, sex, physical characteristics, etc. Furthermore, this process mostly runs in buildings with different constraints like locations of the rows of shelves, exit gates, etc. On one hand, in order to deal with disasters such as fire propagation, studying this kind of system using a dynamic model has a great importance in order to avoid the maximum of casualties. On the other hand, the model that represents this kind of system must take into account several factors such as time, the building's characteristics and people's characteristics. In this study, an agent-based model has been designed to visualise the dynamic system behaviour via these internal entities that often interact. Additionally, we use some dynamic data mining methods such as Monte Carlo method to calculate the stable characteristics of this model via probabilistic approach.
Citation

M. BOURAHLA Mustapha, (2019), "Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method", [national] IJSPM , Inderscience

Read Full Paper

Stability-based Dynamic Bayesian Network method for dynamic data mining

In this article we introduce a new stability-based dynamic Bayesian network method for dynamic systems represented by their time series. Based on the Grow Shrink algorithm and the stability of the network through time, new variables and arcs could be added to the network in order to generate missing data or predict future values. The concept of stability in the network is maintained through a stability matrix which contains learned values that indicate the strength of dependencies between variables along the time. Moreover, we present the application of the proposed method to deal with the problem of prediction in a real-life air quality case study, in which we try to predict the level of Carbon monoxide in the air, comparing between the results obtained using the proposed method and those obtained using the Vector Autoregression model.
Citation

M. BOURAHLA Mustapha, (2019), "Stability-based Dynamic Bayesian Network method for dynamic data mining", [national] Eng. Appl. of AI , Elsevier

Read Full Paper
2018

Description and reasoning for vague ontologies using logic programming

The Semantic Web ontologies can contain vague axioms, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption if the necessary information is incomplete (there is an ignorance about information). An axiom description can be very exact (crisp axiom) or exact (fuzzy axiom) if its knowledge is complete, otherwise it is inexact (vague axiom) if its knowledge is incomplete. Here, the author proposes vagueness description with meta-level logic programming to describe vague ontologies. These vagueness descriptions are inputs to vagueness reasoning procedure implemented at meta-level, which is based on extended tableau algorithm. The extended tableau algorithm is intended to answer queries even with the presence of imprecise information.
Citation

M. BOURAHLA Mustapha, (2018), "Description and reasoning for vague ontologies using logic programming", [national] IET Software , IET

Read Full Paper
2017

Fuzzy Reasoning in Description Logic

Fuzzy description logics are intended to represent and reason not only crisp notions but also fuzzy ones. They have a key role in various domains, and in particular in the semantic web domain. They are now a promising research orientation, on which we positioned our works, assuming that the reasoning over vague concept is complex, and the integration of new expansion rules is essential. This is the subject of this paper. The approach which we propose for this purpose is to extend Description Logics by concrete domains which allows us to describe the precise parts of the concepts, the thing that allows us at the reasoning phase by applying expansion rules proposed to extract the vague part of the concept by assigning a degree of certainty, and also execute an inference according to these degrees.
Citation

M. BOURAHLA Mustapha, (2017), "Fuzzy Reasoning in Description Logic", [national] International Journal of Computer Science and Network Security , International Journal of Computer Science and Network Security

Read Full Paper

Polynomial Algorithms for Computing a Single Preferred Assertional-Based Repair

This paper investigates different approaches for handling inconsistent DL-Lite knowledge bases in the case where the assertional base is prioritized and inconsistent with the terminological base. The inconsistency problem often happens when the assertions are provided by multiple conflicting sources having different reliability levels. We propose different inference strategies based on the selection of one consistent assertional base, called a preferred repair. For each strategy, a polynomial algorithm for computing the associated single preferred repair is proposed. Selecting a unique repair is important since it allows an efficient handling of queries. We provide experimental studies showing (from a computational point of view) the benefits of selecting one repair when reasoning under inconsistency in lightweight knowledge bases.
Citation

M. BOURAHLA Mustapha, (2017), "Polynomial Algorithms for Computing a Single Preferred Assertional-Based Repair", [national] KI - Künstliche Intelligenz, German Journal on Artificial Intelligence , Springer

Read Full Paper

Reasoning with Vague Concepts in Description Logics

The open world assumption in ontologies representing knowledge may assign deficient (imprecise) meaning for ontology concepts which are language adjectives referring the meaning of classes of objects (individuals). The interpretation of an imprecise (vague) concept is by three subsets of individuals. The first subset of individuals surely belongs to the vague concept, the second subset of individuals surely doesn't belong the vague concept and the third subset is in the borderline. In this paper, the authors will show that is possible to describe ontology vague concepts using well-defined formal languages. The authors will propose also an extension of the Tableau algorithm for reasoning over vague ontologies.
Citation

M. BOURAHLA Mustapha, (2017), "Reasoning with Vague Concepts in Description Logics", [national] International Journal of Fuzzy System Applications , IGI Global

Read Full Paper

A model transformation approach for specifying real-time systems and its verification using RT-maude

Real-time systems must be properly validated and verified before their manufacturing and deployment in order to increase their reliability and reduce their maintenance cost. Models have been used for a long time to build complex systems, in virtually every engineering field. This is because they provide invaluable help in making important design decisions before the system is implemented. In this paper, the authors propose an approach based on model transformation to apply formal verification techniques to demonstrate the correctness of system designs. At the first step, they describe real-time systems by state chart (machine) diagrams, as source models to generate RT-Maude models (target models). The second step is to use the result models to verify the real-time systems against specified LTL properties using Maude LTL Model-Checker. This approach is illustrated through an example.
Citation

M. BOURAHLA Mustapha, (2017), "A model transformation approach for specifying real-time systems and its verification using RT-maude", [national] International Journal of Information Technology and Web Engineering , IGI Global

Read Full Paper

Repairing errors in probabilistic databases models using probabilistic abduction reasoning

This paper presents a technique to diagnose probabilistic counter examples that are generated when model checking probabilistic databases models against probabilistic properties formulating queries on probabilistic databases. In probabilistic model checking (PMC), a counterexample is a set of paths that satisfies a path formula, whose cumulative probability mass violates the probability bound. The diagnosis is to repair errors in probabilistic PRISM programs of probabilistic databases models using the probabilistic abduction reasoning on independent choice logic (ICL) programs describing the generated probabilistic counterexamples.
Citation

M. BOURAHLA Mustapha, (2017), "Repairing errors in probabilistic databases models using probabilistic abduction reasoning", [national] International Journal of Intelligent Information and Database Systems , Inderscience

Read Full Paper

LTL Transformation Modulo Positive Transitions

In this study, the author presents a new efficient algorithm for translating linear temporal logic (LTL) formulas to Büchi automata, which are used by LTL model checkers. The general idea of this algorithm is to generate Büchi automata from LTL formulas, using the principle of alternating automata and keeping only the positive transitions without generating the intermediate generalised automata. The LTL translation is the heart of any LTL model checker, which affects its performance. The translation performance is measured in addition to its speed and the size of the produced Büchi automaton (number of states and number of transitions), by correctness of produced Büchi automaton and its level of determinism. The author will show that this method is different from the others and it is very competitive with the most efficient translators to date.
Citation

M. BOURAHLA Mustapha, (2017), "LTL Transformation Modulo Positive Transitions", [national] IET Computers & Digital Techniques , IET

Read Full Paper
2016

Reasoning over decomposing fuzzy description logic

A DF-ALC (Decomposing fuzzy ALC) is proposed in this paper to satisfy the need for representing and reasoning with fuzzy ontologies in the context of semantic Web. A DF-ALC is also proposed to satisfy the need for seeing the necessity of decomposing ontology into several sub-ontologies in order to optimize the fuzzy reasoning process.

The main contribution of this work is to decompose the axioms of the ontology into sub-axioms according to a degree of certainty which is assigned to the fuzzy concepts and roles. It is also to define the syntax and semantics and to propose a local reasoning algorithm and a way of using gateways to infer between local TBo
Citation

M. BOURAHLA Mustapha, (2016), "Reasoning over decomposing fuzzy description logic", [national] Journal of Innovation in Digital Ecosystems , Elsevier

Read Full Paper

The Reasoning in the Description Logic with Vague Concepts

The Description Logic languages are considered the core of the knowledge representation systems, given both the structure of a DL knowledge base and its associated reasoning services. “concept” is used to refer to the expressions of a DL language, denoting sets of individuals; however DL becomes les suitable in domains in which the concepts to be represented have not precise definition. we will face the problem of vague concepts. This paper discusses a vagueness theory to express the vague concepts in OWL2 and propose reasoning technique for reasoning tasks of extended Vagueness DL by introduce new expansion rules in Tableau algorithm for reasoning over vague DL..
Citation

M. BOURAHLA Mustapha, (2016), "The Reasoning in the Description Logic with Vague Concepts", [national] International Journal of Computer Science and Information Security , International Journal of Computer Science and Information Security

Read Full Paper

Debugging of probabilistic systems using structural equation modelling

The counterexample in probabilistic model checking (PMC) is a set of paths in which a path formula holds, and their accumulated probability violates the probability bound. However, understanding the counterexample is not an easy task. In this paper we address the complementary task of counterexample generation, which is the counterexample analysis. We propose an aided-diagnostic method for probabilistic counterexamples based on the notions of causality and regression analysis. Given a counterexample for a probabilistic CTL (PCTL)/continuous stochastic logic (CSL) formula that does not hold over probabilistic models [discrete time Markov chain (DTMC), Markov decision process (MDP) and continuous time Markov Chain (CTMC)], this method generates the causes of the violation, and describes their contribution to the error in the form of a regression model using structural equation modelling (SEM). The interpretation of the regression model generated helps the designer to get better insight on the behaviour of the model, and thus helps him to understand how the error has emerged.
Citation

M. BOURAHLA Mustapha, (2016), "Debugging of probabilistic systems using structural equation modelling", [national] Int. J. Critical Computer-Based Systems , Inderscience

Read Full Paper

Verification of Pipelined Microprocessors using Maude LTL Model Checker

https://www.academia.edu/28181189/Verification_of_Pipelined_Microprocessors_using_Maude_LTL_Model_Checker
Citation

M. BOURAHLA Mustapha, (2016), "Verification of Pipelined Microprocessors using Maude LTL Model Checker", [national] International Journal of Computer Science and Information Security , International Journal of Computer Science and Information Security

Read Full Paper
2014

Towards an Ontology for UML State Machines

Ontology is a conceptual model that is used to represent the concepts in a domain and relationship between the concepts. It can be used for sharing and reuse of knowledge that allows humans and machines to exchange diverse information. UML state machines used to describe the behavior of a software systems. This article aims at to provide a solution for representing UML state machine model as an ontology expressed in OWL. a method proposed is a conceptualization of UML state machine and its operation, in order to check its consistency and its conformity. we chose OWL to represent formally our state machine augmented with SWRL rules to represent the dynamic aspect of the operation system and SPARQL to query our ontology.
Citation

M. BOURAHLA Mustapha, (2014), "Towards an Ontology for UML State Machines", [national] Lecture Notes on Software Engineering , LNSE

Read Full Paper
2013

Medical treatment analysis using probabilistic model checking

Physicians and patients are always facing critical situations where they have alternative actions, and they have to choose the appropriate one in order to get the best outcome. Medical treatment decision is a highly complex process that involves health states, preferences, the offered options (actions) and the corresponding cost. The complexity of this process is due to the multiple treatment decision and the accompanying risk factors, as well as that these decisions are made under uncertainty. Markov Decision Processes (MDPs) are a mathematical framework for modelling dynamic systems under uncertainty. Its ability to define an optimal policy makes the MDPs as one of the most successful methods analysis in health care and medical treatment. With this growing importance, delivering frameworks for solving, visualising and cost-effective analysing of MDPs is highly required. In this paper, we address the use of probabilistic model checking as a comprehensive technique for modelling and analysing medical treatment problems.
Citation

M. BOURAHLA Mustapha, (2013), "Medical treatment analysis using probabilistic model checking", [national] International Journal of Biomedical Engineering and Technology , Inderscience

Read Full Paper

Decision Support Technique for Supply Chain Management

In this paper, we propose a method for supporting decision makers in the domain of supply chain management. Our objective is the global optimization instead of optimizing independent subsystems of the supply chain. The method architecture is based on combination of the simulation and optimization techniques which includes a multi-objectives optimization module and a simulation module. The optimization module is based on genetic algorithms and the simulation module uses effective alternative designs proposed by strategic and tactic decisions to find global optimal solution using the optimal scheduling solution proposed by the genetic algorithm for operational decisions. The experimental results show the efficiency and the feasibility of the proposed approach.
Citation

M. BOURAHLA Mustapha, (2013), "Decision Support Technique for Supply Chain Management", [national] CIT , CIT

Read Full Paper

Generating Diagnoses for Probabilistic Model Checking Using Causality

One of the most major advantages of Model checking over other formal methods of verification, its ability to generate an error trace in case of a specification falsified in the model. We call this trace a counterexample. However, understanding the counterexample is not that easy task, because model checker generates usually multiple counterexamples of long length, what makes the analysis of counterexample time-consuming as well as costly task. Therefore, counterexamples should be small and as indicative as possible to be understood. In probabilistic model checking (PMC) counterexample generation has a quantitative aspect. The counterexample in PMC is a set of paths in which a path formula holds, and their accumulative probability mass violates the probability bound. In this paper, we address the complementary task of counterexample generation which is the counterexample diagnosis in PMC. We propose an aided-diagnostic method for probabilistic counterexamples based on the notion of causality and responsibility. Given a counterexample for a Probabilistic CTL (PCTL) formula that doesn’t hold over Discreet-Time-Markov-Chain (DTMC) model, this method guides the user to the most responsible causes in the counterexample.
Citation

M. BOURAHLA Mustapha, (2013), "Generating Diagnoses for Probabilistic Model Checking Using Causality", [national] CIT , CIT

Read Full Paper
2012

Automatic Generation of OWL Ontologies from UML Class Diagrams Based on MetaModelling and Graph Grammars

Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared
Citation

M. BOURAHLA Mustapha, (2012), "Automatic Generation of OWL Ontologies from UML Class Diagrams Based on MetaModelling and Graph Grammars", [national] Engineering and Technology International Journal of Computer , World Academy of Science

Read Full Paper
2009

Verification of Complex Real-Time Systems using Rewriting Logic

This paper presents a method for model checking dense complex real-time systems. This approach is implemented at the meta level of the Rewriting Logic system Maude. The dense complex real-time system is specified using a syntax which has the semantics of timed automata and the property is specified with the temporal logic TLTL (Timed LTL). The well known timed automata model checkers Kronos and Uppaal only support TCTL model checking (a very limited fragment in the case of Uppaal). Specification of the TLTL property is reduced to LTL and its temporal constraints are captured in a new timed automaton. This timed automaton will be composed with the original timed automaton representing the semantics of the complex real-time system under analysis. Then, the product timed automaton will be abstracted using partition refinement of state space based on strong bi-simulation. The result is an untimed automaton modulo the TLTL property which represents an equivalent finite state system to be model checked using Maude LTL model checking. This approach is successfully tested on industrial designs.
Citation

M. BOURAHLA Mustapha, (2009), "Verification of Complex Real-Time Systems using Rewriting Logic", [national] JCIT , CIT

Read Full Paper
2008

Generating Exact Approximations to Model Check Concurrent System

n this paper, we present a method to generate abstractions for model checking concurrent systems. A programusing a defined syntax and semantics, first describes the concurrent system that can be infinite. This program is abstractedusing the framework of abstract interpretation where an abstract function will be given. This abstract program isdemonstrated to be an accurate approximation of the original program that may contain spurious behaviours. These spuriousbehaviours will be identified and removed using a new defined abstraction framework based on the restrictions. The newproduced abstract program is an exact approximation of the original program
Citation

M. BOURAHLA Mustapha, (2008), "Generating Exact Approximations to Model Check Concurrent System", [national] IAJIT , IAJIT

Read Full Paper
← Back to Researchers List