2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)
Electric vehicles (EV) have recently appeared on the transport market as a solution for reducing ... more Electric vehicles (EV) have recently appeared on the transport market as a solution for reducing greenhouse gas emissions. They are promising means of clean transportation. However, they are obstructed by their limited autonomy that deprives them of completing long trips without a periodic refueling. To encourage their use, the researchers took an interest in the way to optimally deploy charging infrastructure while minimizing investments costs and maximizing the network’s coverage as part of the facility location problem (FLP). Several models have been designed to solve the location problem for EV charging stations (CS) (LPEVCS). This work proposes a Mixed Integer Programing (MIP) model for the LPEVCS. Given a set of potential sites, a limited number of stations and charging points (CP) to be located, the LPEVCS seeks to find the optimal location of the CS so as to cover the recharging demand as widely as possible and to ensure continuity in the EV trips. To solve this problem on a large scale, we introduce intelligence into swarms. We propose a hybrid method based on the Particle Swarm Optimization (PSO) and Tabu Search (TS) algorithms. This approach is used to solve several generated sets of data with varying sizes using JAVA language and proved the possibility to obtain near-optimal solution for large instances. (Abstract)
Automated assessment of programming assignments continues to be an attractive research topic in c... more Automated assessment of programming assignments continues to be an attractive research topic in computer science education. Various methods have been contrived and applied for over many years. They aim at improving students’ programming skills and optimizing the teaching staff time. Despite the various works carried out in this context, there still gaps to be addressed. The major shortcoming is related to the diversity of correct programs that resolve a programming problem. In this paper, we propose a new evaluation method that can well support the grading process of students’ programming assignments. The proposed method consists of measuring the gap between the student-provided program and the teacher-provided models by using a semantic similarity measure. Our proposed similarity measure is based on the symbolic execution technique that compares two programs according to their symbolic execution outputs. In this work, we present two variants of this method. The first one compares s...
Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2020
Nowadays, Ontologies have become widely used to design formalism for knowledge representation, an... more Nowadays, Ontologies have become widely used to design formalism for knowledge representation, and are considered as the foundation for the Semantic Web. However, with their widespread usage, a question of their complexity evaluation increased even more, especially in some domains that currently know a cruise number of ontologies like Cultural Heritage. In this paper, we present an analysis of the advanced metrics for measuring the design complexity of existing cultural heritage ontologies (CH). In this context, the main goals of this study are to (i) present advanced metrics such as the size of vocabulary, the tree impurity, coupling, average number of path per concept, and average path length, in order to analyze the advanced complexity features of the CH ontologies and their impact on the reuse and evolution of the CH ontologies; (ii) Help developers to decide whether the ontology is over complex that it needs some simplification or rebuilding ; (iii) Make developers clearly realize the impact of the size and scale of ontology. In order to reach these goals, a set of twenty CH ontologies are gathered from the web to measure and analyze their advanced complexity metrics. By analyzing the size of vocabulary, the average number of paths per concept, and average path length, the evaluation results exhibit that the CH ontologies studied are highly complex. In addition, the CH ontologies cannot be easily maintained due to the findings reached through the analysis of the tree impurity and coupling.
With the advance of computational techniques, the amount of genomic data has risen exponentially,... more With the advance of computational techniques, the amount of genomic data has risen exponentially, with a rapid rate [1] making it hard to utilize such data in the medical field without appropriate pre-processing, which in turn leads to more complexity and veracity issues [2] eventually creating multiple complications such as storage, analysis, privacy and security. Therefore, genomic data may look easy to handle in terms of its volume, but it actually requires quite a complicated process due to the complexity, heterogeneity and hybridity of its features. This process is entitled knowledge discovery process [3]: • Data recording Includes the different challenges and tools regarding the capture and storage of data. • Data pre-processing Which includes all the operations of cleaning and appropriation of the captured data to the ready to analyze form in order to optimize the analysis step. • Data analysis The task of evaluating data using different algorithms following a logical reasoning to examine each component of the data provided, with the aim of dispensing insightful outcomes.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • Introducing a new set of fractional-order moments for image representation. • Constructing a set of fractional-order moment invariants for pattern recognition. • Discussing the computational aspects of the proposed fractional-order moments and moment invariants. • Providing numerical experiments to demonstrate their validity and superiority.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights New Fuzzy Nearest Neighbor Classification Method, called Fuzzy Analogy Based Classification (FABC). Describing the domain features by fuzzy sets. Management of uncertainty and impreciseness in classification process by means of aggregation operators. Promising results of the new classifier and compared with advanced Fuzzy Nearest Neighbor Classifiers.
2015 Intelligent Systems and Computer Vision (ISCV), 2015
We propose in this paper, a gender recognition solution under the presence of occlusion and using... more We propose in this paper, a gender recognition solution under the presence of occlusion and using the very restrict samples in the learning base. The developed approach is based on the extraction of pertinent 3D depth-radial curves that cover the nose region and combined dimensionality reduction using sparse random projection method; furthermore we propose an extension of similarity based classification approach to handle recognition task. Experimental results approve the effectiveness of our approach and show that the proposed method is also effective in the presence of variations such as facial expressions and rotation.
2006 2nd International Conference on Information & Communication Technologies
In spite of the several software effort estimation models developed over the last 30 years, provi... more In spite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques such as: case-based reasoning, decision trees, genetic algorithms and neural networks. This paper is devoted to the design of Radial Basis Function Networks for software cost estimation. It shows the impact of the RBFN network structure, especially the number of neurons in the hidden layer and the widths of the basis function, on the accuracy of the produced estimates measured by means of MMRE and Pred indicators. The empirical study uses two different software project datasets namely, artificial COCOMO'81 and Tukutuku datasets.
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Context: Automatic Personality Prediction has experienced significant growth and has appealed to ... more Context: Automatic Personality Prediction has experienced significant growth and has appealed to researchers thanks to its useful applications. However, as far as we are aware, there is a lack of studies that summarize systematically the different works in literature. – Objective: To describe the current state of the art, specifically to: (i) determine the demographic characteristics of the studies published in this topic, (ii) identify the used psychological models, (iii) characterize the prediction approaches in terms of methods, data and features. - Method: We conducted a systematic mapping study by following a recommended process. We employed an automatic search in different well-known databases and applied selection criteria to obtain the final set of papers to be studied. Then we proceeded to the extraction of specific information from selected papers to answer our questions. Results-: We evaluated 379 articles. The results show that the number of primary studies has increased significantly. We identified the trends concerning: personality models, data inputs and sources, prediction types, methods and features. - Conclusion: This overview of the literature may be a solid and comprehensive baseline for researchers in the domain of personality prediction. It may assist in identifying research directions and potential paths to explore.
International Journal of Bioinformatics Research and Applications, 2020
People with autism often engage in stereotyped and repetitive motor movements. Hence, our aim is ... more People with autism often engage in stereotyped and repetitive motor movements. Hence, our aim is to put out a smart video surveillance system that facilitates the diagnosis of autism for doctors. In this respect, we propose an automatic stereotypical motor movement detection system in real time. Firstly, we use the Kinect sensor to monitor the child's with autism movements. Secondly, we propose a data integration process to make the provided data from Kinect sensor more comprehensive and specific. Thirdly, we perform the gesture detection by using the well know machine learning algorithms such as decision tree, artificial neural network and nearest neighbour. The obtained result is very promising and shows that the data integration step enhances the gesture recognition. The experimental results show that our system can achieve above 99.8% recognition rate. Also, we evaluate the efficiency of different machine learning algorithms in recognition tasks.
2017 Intelligent Systems and Computer Vision (ISCV), 2017
The Nearest Neighbor Classification (NNC) has been widely used as classification method, due to i... more The Nearest Neighbor Classification (NNC) has been widely used as classification method, due to its simplicity, classification efficiency and its ability to deal with different classification problems. Despite its good classification accuracy, the NNC suffers from many shortcomings on the execution time, noise sensitivity, high storage requirements and lack of interpretability. In this paper, we propose a new prototype learning algorithm for NNC method. This new algorithm derives a small subset of relevant prototypes from the training set by the means of clustering algorithms. The obtained prototypes enable the NNC method to deal with the aforementioned issues. Moreover, using clustering algorithm involve an interpretable set of prototypes that can describes the application domain. The proposal is experimented on various datasets and compared to other prototypes learning methods. The results show the effectiveness of our approach compared to the existed methods.
Mobile ad-hoc networks are collection of wireless nodes which exchange information dynamically wi... more Mobile ad-hoc networks are collection of wireless nodes which exchange information dynamically without pre-existing infrastructure. The communication has been established by nodes who act as intermediate switch as well transceiver. Nodes are randomly selected for the transmission process. The adhoc routing protocols can be divided into two areas which are Proactive (also called as Table driven) and Reactive (known as on demand or demand driven) protocols. The network topology in ad-hoc network usually changes with time therefore many new challenges appear while routing the packet from source to destination. While transferring packet many numbers of intermediate nodes appear and used for forwarding packet to another intermediate node closer to destination. In this paper we will study different types of proactive protocols who are facing different types of problems such as Types of attacks, dynamically changing topologies, asymmetric link, low transmission power, bandwidth utilization...
In this paper, we propose a generalized approach for mapping a relational database into ontology.... more In this paper, we propose a generalized approach for mapping a relational database into ontology. This proposed approach is composed of two main phases. The first one consists of generating the ontology from the relational database, based on a direct mapping process by using R2RML language. The second one is devoted to evaluating the correctness of the generated ontology, with regards to two validation criteria, Information preservation, and Query preservation. In addition, we evaluate the computational performance of the proposed mapping process in terms of storage space and execution time.
In this paper, we introduce three new implementations of non-native methods for storing RDF data.... more In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly sho...
Programming courses are among all the current academic curricula for engineering studies. Unfortu... more Programming courses are among all the current academic curricula for engineering studies. Unfortunately, students often face difficulties already on the basic concepts. Both students and teachers believe that practical sessions and guided learning lead to good outcomes. On the other hand, it is virtually difficult considering the number of students enrolled on programming courses. This paper presents an automated assessment system for programming assignments, based on two different methods: static and dynamic analysis. The presented system aims at providing the student with an ongoing and various feedback delivered according to the category and the recurrence of errors. The system imbeds an automated error repairing feature for the purposes of insuring the assessment process achievement. It operates if the student fails to submit a correct program despite the feed-back provided by the system. In such cases, the system uses a penalty mechanism, customized by the teacher to grade the ...
This paper investigates with the fuzzy representation of software project attributes. The aim is ... more This paper investigates with the fuzzy representation of software project attributes. The aim is to generate fuzzy sets and their membership functions from numerical data of software project attributes. The proposed fuzzy sets generation process consists in two main steps: First, we use the well-known Fuzzy C-Means algorithm (FCM) and the Xie-Beni validity criterion to decide on the number of fuzzy sets. Second, we use a Real Coded Genetic Algorithm (RCGA) to build membership functions for these fuzzy sets. Membership functions can be trapezoidal, triangular or Gaussian. This study uses the software attributes given in the COCOMO’81 dataset.
In this paper, we propose a new fuzzy similarity-based classification (FSBC) method for the task ... more In this paper, we propose a new fuzzy similarity-based classification (FSBC) method for the task of gender recognition. The proposed method characterises each individual by extracting geometrical features from a 3D facial image using pertinent radial curves. Our approach includes representing the extracted features using fuzzy sets to handle imprecision in its values. Also, the proposed FSBC method recognises the gender of a new person by evaluating his similarity to the male and female samples pre-set as gender representatives set, then we aggregate the obtained similarities to compute the scores of belonging to each gender. In the end, we ascribe to each new person the gender with the higher score. With the proposed method, two main advantages are obtained: First, we used the OWA operator and RIM quantifier to define the percentage of significant features for the similarity assessment. Second, the aggregation process was performed using compensatory operators to ensure the selected gender has high similarities. Experiments were conducted using FRAV3D database, by considering only one frontal pose in the gender representatives set. The obtained gender recognition rate of the proposed method was very promising compared to other classification method.
International Journal of Interactive Mobile Technologies (iJIM)
The extremely fast topology has created new requirements for the geographic routing protocol, whi... more The extremely fast topology has created new requirements for the geographic routing protocol, which has been the most efficient solution for Vehicular Ad-hoc Networks (VANETs). The frequent disconnection of links makes the choice of the next routing node extremely difficult. Hence, an efficient routing algorithm needs to deliver the appropriate path to transfer the data packets with the most relevant quality of service (QoS). In this work, the weight-aware greedy perimeter stateless (WA-GPSR) routing protocol is presented. The enhanced GPSR protocol computes the reliable communication area and selects the next forwarding vehicle based on several routing criteria. The proposal has been evaluated and compared to Maxduration-Minangle GPSR (MM-GPSR) and traditional GPSR using strict metric analysis. Our experimental results using NS-2 and VanetMobiSim, have demonstrated that WA-GPSR has the ability to enhance network performance.
The Dimensionality Curse is one of the most critical issues that are hindering faster evolution i... more The Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (PSO) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (PSO) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (BPSO) with Particl...
The aim of converting relational database into Ontology is to provide applications that are based... more The aim of converting relational database into Ontology is to provide applications that are based on the semantic representation of the data. Whereas, representing the data using ontologies has shown to be a useful mechanism for managing and exchanging data. This is the reason why bridging the gap between relational databases and ontologies has attracted the interest of the ontology community from early years, and it is commonly referred to as the database-to-ontology mapping problem. In this paper, we: (1) propose a new life cycle for ontology learning from RDBs based on the software engineering requirements; (2) describe a new process for building ontology from Relational database based on the predefined life cycle; (3) add three new semantics that can be extracted from RDB;(4) we suggest an evaluation process based on two categories of metrics: (i) Conceptual Ontology (TBox) Evaluation metrics; (ii) Factual ontology (ABox) evaluation metrics.
2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)
Electric vehicles (EV) have recently appeared on the transport market as a solution for reducing ... more Electric vehicles (EV) have recently appeared on the transport market as a solution for reducing greenhouse gas emissions. They are promising means of clean transportation. However, they are obstructed by their limited autonomy that deprives them of completing long trips without a periodic refueling. To encourage their use, the researchers took an interest in the way to optimally deploy charging infrastructure while minimizing investments costs and maximizing the network’s coverage as part of the facility location problem (FLP). Several models have been designed to solve the location problem for EV charging stations (CS) (LPEVCS). This work proposes a Mixed Integer Programing (MIP) model for the LPEVCS. Given a set of potential sites, a limited number of stations and charging points (CP) to be located, the LPEVCS seeks to find the optimal location of the CS so as to cover the recharging demand as widely as possible and to ensure continuity in the EV trips. To solve this problem on a large scale, we introduce intelligence into swarms. We propose a hybrid method based on the Particle Swarm Optimization (PSO) and Tabu Search (TS) algorithms. This approach is used to solve several generated sets of data with varying sizes using JAVA language and proved the possibility to obtain near-optimal solution for large instances. (Abstract)
Automated assessment of programming assignments continues to be an attractive research topic in c... more Automated assessment of programming assignments continues to be an attractive research topic in computer science education. Various methods have been contrived and applied for over many years. They aim at improving students’ programming skills and optimizing the teaching staff time. Despite the various works carried out in this context, there still gaps to be addressed. The major shortcoming is related to the diversity of correct programs that resolve a programming problem. In this paper, we propose a new evaluation method that can well support the grading process of students’ programming assignments. The proposed method consists of measuring the gap between the student-provided program and the teacher-provided models by using a semantic similarity measure. Our proposed similarity measure is based on the symbolic execution technique that compares two programs according to their symbolic execution outputs. In this work, we present two variants of this method. The first one compares s...
Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2020
Nowadays, Ontologies have become widely used to design formalism for knowledge representation, an... more Nowadays, Ontologies have become widely used to design formalism for knowledge representation, and are considered as the foundation for the Semantic Web. However, with their widespread usage, a question of their complexity evaluation increased even more, especially in some domains that currently know a cruise number of ontologies like Cultural Heritage. In this paper, we present an analysis of the advanced metrics for measuring the design complexity of existing cultural heritage ontologies (CH). In this context, the main goals of this study are to (i) present advanced metrics such as the size of vocabulary, the tree impurity, coupling, average number of path per concept, and average path length, in order to analyze the advanced complexity features of the CH ontologies and their impact on the reuse and evolution of the CH ontologies; (ii) Help developers to decide whether the ontology is over complex that it needs some simplification or rebuilding ; (iii) Make developers clearly realize the impact of the size and scale of ontology. In order to reach these goals, a set of twenty CH ontologies are gathered from the web to measure and analyze their advanced complexity metrics. By analyzing the size of vocabulary, the average number of paths per concept, and average path length, the evaluation results exhibit that the CH ontologies studied are highly complex. In addition, the CH ontologies cannot be easily maintained due to the findings reached through the analysis of the tree impurity and coupling.
With the advance of computational techniques, the amount of genomic data has risen exponentially,... more With the advance of computational techniques, the amount of genomic data has risen exponentially, with a rapid rate [1] making it hard to utilize such data in the medical field without appropriate pre-processing, which in turn leads to more complexity and veracity issues [2] eventually creating multiple complications such as storage, analysis, privacy and security. Therefore, genomic data may look easy to handle in terms of its volume, but it actually requires quite a complicated process due to the complexity, heterogeneity and hybridity of its features. This process is entitled knowledge discovery process [3]: • Data recording Includes the different challenges and tools regarding the capture and storage of data. • Data pre-processing Which includes all the operations of cleaning and appropriation of the captured data to the ready to analyze form in order to optimize the analysis step. • Data analysis The task of evaluating data using different algorithms following a logical reasoning to examine each component of the data provided, with the aim of dispensing insightful outcomes.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • Introducing a new set of fractional-order moments for image representation. • Constructing a set of fractional-order moment invariants for pattern recognition. • Discussing the computational aspects of the proposed fractional-order moments and moment invariants. • Providing numerical experiments to demonstrate their validity and superiority.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights New Fuzzy Nearest Neighbor Classification Method, called Fuzzy Analogy Based Classification (FABC). Describing the domain features by fuzzy sets. Management of uncertainty and impreciseness in classification process by means of aggregation operators. Promising results of the new classifier and compared with advanced Fuzzy Nearest Neighbor Classifiers.
2015 Intelligent Systems and Computer Vision (ISCV), 2015
We propose in this paper, a gender recognition solution under the presence of occlusion and using... more We propose in this paper, a gender recognition solution under the presence of occlusion and using the very restrict samples in the learning base. The developed approach is based on the extraction of pertinent 3D depth-radial curves that cover the nose region and combined dimensionality reduction using sparse random projection method; furthermore we propose an extension of similarity based classification approach to handle recognition task. Experimental results approve the effectiveness of our approach and show that the proposed method is also effective in the presence of variations such as facial expressions and rotation.
2006 2nd International Conference on Information & Communication Technologies
In spite of the several software effort estimation models developed over the last 30 years, provi... more In spite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques such as: case-based reasoning, decision trees, genetic algorithms and neural networks. This paper is devoted to the design of Radial Basis Function Networks for software cost estimation. It shows the impact of the RBFN network structure, especially the number of neurons in the hidden layer and the widths of the basis function, on the accuracy of the produced estimates measured by means of MMRE and Pred indicators. The empirical study uses two different software project datasets namely, artificial COCOMO'81 and Tukutuku datasets.
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Context: Automatic Personality Prediction has experienced significant growth and has appealed to ... more Context: Automatic Personality Prediction has experienced significant growth and has appealed to researchers thanks to its useful applications. However, as far as we are aware, there is a lack of studies that summarize systematically the different works in literature. – Objective: To describe the current state of the art, specifically to: (i) determine the demographic characteristics of the studies published in this topic, (ii) identify the used psychological models, (iii) characterize the prediction approaches in terms of methods, data and features. - Method: We conducted a systematic mapping study by following a recommended process. We employed an automatic search in different well-known databases and applied selection criteria to obtain the final set of papers to be studied. Then we proceeded to the extraction of specific information from selected papers to answer our questions. Results-: We evaluated 379 articles. The results show that the number of primary studies has increased significantly. We identified the trends concerning: personality models, data inputs and sources, prediction types, methods and features. - Conclusion: This overview of the literature may be a solid and comprehensive baseline for researchers in the domain of personality prediction. It may assist in identifying research directions and potential paths to explore.
International Journal of Bioinformatics Research and Applications, 2020
People with autism often engage in stereotyped and repetitive motor movements. Hence, our aim is ... more People with autism often engage in stereotyped and repetitive motor movements. Hence, our aim is to put out a smart video surveillance system that facilitates the diagnosis of autism for doctors. In this respect, we propose an automatic stereotypical motor movement detection system in real time. Firstly, we use the Kinect sensor to monitor the child's with autism movements. Secondly, we propose a data integration process to make the provided data from Kinect sensor more comprehensive and specific. Thirdly, we perform the gesture detection by using the well know machine learning algorithms such as decision tree, artificial neural network and nearest neighbour. The obtained result is very promising and shows that the data integration step enhances the gesture recognition. The experimental results show that our system can achieve above 99.8% recognition rate. Also, we evaluate the efficiency of different machine learning algorithms in recognition tasks.
2017 Intelligent Systems and Computer Vision (ISCV), 2017
The Nearest Neighbor Classification (NNC) has been widely used as classification method, due to i... more The Nearest Neighbor Classification (NNC) has been widely used as classification method, due to its simplicity, classification efficiency and its ability to deal with different classification problems. Despite its good classification accuracy, the NNC suffers from many shortcomings on the execution time, noise sensitivity, high storage requirements and lack of interpretability. In this paper, we propose a new prototype learning algorithm for NNC method. This new algorithm derives a small subset of relevant prototypes from the training set by the means of clustering algorithms. The obtained prototypes enable the NNC method to deal with the aforementioned issues. Moreover, using clustering algorithm involve an interpretable set of prototypes that can describes the application domain. The proposal is experimented on various datasets and compared to other prototypes learning methods. The results show the effectiveness of our approach compared to the existed methods.
Mobile ad-hoc networks are collection of wireless nodes which exchange information dynamically wi... more Mobile ad-hoc networks are collection of wireless nodes which exchange information dynamically without pre-existing infrastructure. The communication has been established by nodes who act as intermediate switch as well transceiver. Nodes are randomly selected for the transmission process. The adhoc routing protocols can be divided into two areas which are Proactive (also called as Table driven) and Reactive (known as on demand or demand driven) protocols. The network topology in ad-hoc network usually changes with time therefore many new challenges appear while routing the packet from source to destination. While transferring packet many numbers of intermediate nodes appear and used for forwarding packet to another intermediate node closer to destination. In this paper we will study different types of proactive protocols who are facing different types of problems such as Types of attacks, dynamically changing topologies, asymmetric link, low transmission power, bandwidth utilization...
In this paper, we propose a generalized approach for mapping a relational database into ontology.... more In this paper, we propose a generalized approach for mapping a relational database into ontology. This proposed approach is composed of two main phases. The first one consists of generating the ontology from the relational database, based on a direct mapping process by using R2RML language. The second one is devoted to evaluating the correctness of the generated ontology, with regards to two validation criteria, Information preservation, and Query preservation. In addition, we evaluate the computational performance of the proposed mapping process in terms of storage space and execution time.
In this paper, we introduce three new implementations of non-native methods for storing RDF data.... more In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly sho...
Programming courses are among all the current academic curricula for engineering studies. Unfortu... more Programming courses are among all the current academic curricula for engineering studies. Unfortunately, students often face difficulties already on the basic concepts. Both students and teachers believe that practical sessions and guided learning lead to good outcomes. On the other hand, it is virtually difficult considering the number of students enrolled on programming courses. This paper presents an automated assessment system for programming assignments, based on two different methods: static and dynamic analysis. The presented system aims at providing the student with an ongoing and various feedback delivered according to the category and the recurrence of errors. The system imbeds an automated error repairing feature for the purposes of insuring the assessment process achievement. It operates if the student fails to submit a correct program despite the feed-back provided by the system. In such cases, the system uses a penalty mechanism, customized by the teacher to grade the ...
This paper investigates with the fuzzy representation of software project attributes. The aim is ... more This paper investigates with the fuzzy representation of software project attributes. The aim is to generate fuzzy sets and their membership functions from numerical data of software project attributes. The proposed fuzzy sets generation process consists in two main steps: First, we use the well-known Fuzzy C-Means algorithm (FCM) and the Xie-Beni validity criterion to decide on the number of fuzzy sets. Second, we use a Real Coded Genetic Algorithm (RCGA) to build membership functions for these fuzzy sets. Membership functions can be trapezoidal, triangular or Gaussian. This study uses the software attributes given in the COCOMO’81 dataset.
In this paper, we propose a new fuzzy similarity-based classification (FSBC) method for the task ... more In this paper, we propose a new fuzzy similarity-based classification (FSBC) method for the task of gender recognition. The proposed method characterises each individual by extracting geometrical features from a 3D facial image using pertinent radial curves. Our approach includes representing the extracted features using fuzzy sets to handle imprecision in its values. Also, the proposed FSBC method recognises the gender of a new person by evaluating his similarity to the male and female samples pre-set as gender representatives set, then we aggregate the obtained similarities to compute the scores of belonging to each gender. In the end, we ascribe to each new person the gender with the higher score. With the proposed method, two main advantages are obtained: First, we used the OWA operator and RIM quantifier to define the percentage of significant features for the similarity assessment. Second, the aggregation process was performed using compensatory operators to ensure the selected gender has high similarities. Experiments were conducted using FRAV3D database, by considering only one frontal pose in the gender representatives set. The obtained gender recognition rate of the proposed method was very promising compared to other classification method.
International Journal of Interactive Mobile Technologies (iJIM)
The extremely fast topology has created new requirements for the geographic routing protocol, whi... more The extremely fast topology has created new requirements for the geographic routing protocol, which has been the most efficient solution for Vehicular Ad-hoc Networks (VANETs). The frequent disconnection of links makes the choice of the next routing node extremely difficult. Hence, an efficient routing algorithm needs to deliver the appropriate path to transfer the data packets with the most relevant quality of service (QoS). In this work, the weight-aware greedy perimeter stateless (WA-GPSR) routing protocol is presented. The enhanced GPSR protocol computes the reliable communication area and selects the next forwarding vehicle based on several routing criteria. The proposal has been evaluated and compared to Maxduration-Minangle GPSR (MM-GPSR) and traditional GPSR using strict metric analysis. Our experimental results using NS-2 and VanetMobiSim, have demonstrated that WA-GPSR has the ability to enhance network performance.
The Dimensionality Curse is one of the most critical issues that are hindering faster evolution i... more The Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (PSO) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (PSO) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (BPSO) with Particl...
The aim of converting relational database into Ontology is to provide applications that are based... more The aim of converting relational database into Ontology is to provide applications that are based on the semantic representation of the data. Whereas, representing the data using ontologies has shown to be a useful mechanism for managing and exchanging data. This is the reason why bridging the gap between relational databases and ontologies has attracted the interest of the ontology community from early years, and it is commonly referred to as the database-to-ontology mapping problem. In this paper, we: (1) propose a new life cycle for ontology learning from RDBs based on the software engineering requirements; (2) describe a new process for building ontology from Relational database based on the predefined life cycle; (3) add three new semantics that can be extracted from RDB;(4) we suggest an evaluation process based on two categories of metrics: (i) Conceptual Ontology (TBox) Evaluation metrics; (ii) Factual ontology (ABox) evaluation metrics.
Uploads
Papers by Azeddine Zahi