International Journal Papers

Permanent URI for this collectionhttp://dl.cerist.dz/handle/CERIST/17

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A Clustering application for the Web Usage Mining.
(Pushpa Publishing House, 2013-07) Kouici, Salima; Khelladi, Abdelkader
The web usage mining constitutes a new branch of the web mining. It allows the study of the behavior of both users and potential customers via their site navigation. The mainly used source for the web usage mining is the servers log files. A log file contains an important mass of data, including user’s information (username, used software, etc.) and all the queries he has made on the web site (requested files, the number of bytes transferred, time spent on each page, the page of entry to the site, etc.). In this work, we shall outline an application made on this type of data, which is based on a clustering method, namely K-means. This application allows the definition of homogeneous groups constituting users’ profiles so that to anticipate the needs and with a view of communication adapted to each segment of users. In this application, we have recorded some technical problems. These problems concern the data cleaning (removing queries of images and multimedia files associated with web pages, removing queries from search engines, etc.) and the setting up of visitor sessions, knowing that a session is a sequence of pages viewed by the same user.
A cooperative framework for automated segmentation of tumors in brain MRI images
(Springer, 2023-03) Hadjadj, Zineb
Brain tumor segmentation from 2D Magnetic Resonance Images (MRI) is an important task for several applications in the field of medical analysis. Commonly, this task is performed manually by medical professionals, but it is not always obvious due to similarities between tumors and normal tissue and variations in tumor appearance. Therefore, the automation of medical image segmentation remains a real challenge that has attracted the attention of several researchers in recent years. Instead of choosing between region and contour approaches, in this article, we propose a region-edge cooperative method for brain tumor segmentation from MRI images. The region approach used is support vector machines (SVMs), one of the popular and highly motivated classification methods, the method distinguishes between normal and abnormal pixels based on some features: intensity and texture. To control and guide the segmentation region, we take advantage of the Ron Kimmel geodesic Active Contour Model (ACM) which produces a good delimitation of the boundaries of the object. The two methods have been cooperated sequentially in order to obtain a flexible and effective system for brain tumor segmentation. Experimental studies are performed on synthetic and real 2D MRI images of various modalities from the radiology unit of the university hospital center in Bab El Oued Algeria. The used MRI images represent various tumor shapes, locations, sizes, and intensities. The proposed cooperative framework outperformed SVM-based segmentation and ACM-based segmentation when executed independently.
A critical review of quality of service models in mobile ad hoc networks
(Inderscience Enterprises Ltd, 2019) Bouchama, Nadir; Aïssani, Djamil; Djellab, Natalia; Nouali-Taboudjemat, Nadia
Quality of service (QoS) provisioning in mobile ad hoc networks (MANETs) consists of providing a complex functionality in a harsh environment where resources are scarce. Thus, it is very challenging to build an efficient solution to address this issue. The proposed solutions in the literature are broadly classified into four categories, namely: QoS routing protocols, QoS signalling, QoS-aware MAC protocols and QoS models, which are the main concern of our study. The contribution of this paper is threefold: Firstly, we propose a set of guidelines to deal with the challenges facing QoS models design in ad hoc networks. Secondly, we propose a new taxonomy for QoS models in ad hoc networks. Finally, we provide an in-depth survey and discussion of the most relevant proposed frameworks.
A distributed mutual exclusion algorithm over multi-routing protocol for mobile ad hoc networks
(Taylor et Francis, 2008-04-15) Derhab, Abdelouahid; Badache, Nadjib
In this paper, we propose a new architecture to solve the problem of mutual exclusion in mobile ad hoc networks (MANET). The architecture is composed of two layers: (i) a middleware layer that contains a token-based distributed mutual exclusion algorithm (DMEA) and (ii) a network layer that includes two routing forwarding strategies: one to route request messages and the other to route the token message. We also propose a request ordering policy that ensures the usual mutual exclusion properties and reduces the number of hops traversed per critical section (CS) access. The paper also addresses the problem of network partitioning and unreachable nodes. The proposed mutual exclusion algorithm is further enhanced to provide fault tolerance by preventing the loss of the token and generating a new token if the token loss event occurs. The performance complexity as well as the experimental results show that the proposed algorithm experiences low number of hops per CS access.
A fault tolerant services discovery by self organisation: a MAS approach.
(Inderscience, 2013) Mellah, Hakima; Hassas, Salima; Drias, Habiba
The service discovery has become an emerging phenomena in software engineering and process engineering as well. The paper presents a multi agent system (MAS) approach, for service discovery process, based on a self-organising protocol. This feature is very crucial for assuring a correct service delivery, to avoid failures or mal-function for the service discovery environment. The requirement for self-organising choreographed services have been well realised, in case of operational, functional and behavioural faults. The self-organising protocol is conceived from bacteria colony.
A formal model for output multimodal HCI
(Springer Vienna, 2015-07) Mohand Oussaïd, Linda; Ait Sadoune, Idir; Ait Ameur, Yamine; Ahmed-Nacer, Mohamed
Multimodal human–computer interaction (HCI) combine modalities at an abstract specification level in order to get information from the user (input multimodality) and to return information to the user (output multimodality). These multimodal interfaces use two mechanisms: first, the fusion of information transmitted by the user on different modalities during input interaction and second, the fission or decomposition of information produced by the functional core in order to distribute the composite information on the different modalities during output interaction. In this paper, we present a generic approach to design output multimodal interfaces. This approach is based on a formal model, composed of two models: semantic fission model for information decomposition process and allocation model for modalities and media allocation to composite information. An Event-B formalization has been proposed for the fission model and for allocation model. This Event-B formalization extends the generic model and support the verification of some relevant properties such as safety or liveness. An example of collision freeness property verification is presented in this paper.
A framework for space-efficient read clustering in metagenomic samples
(BioMed Central, 2017-03-14) Alanko, Jarno; Cunial, Fabio; Belazzougui, Djamal; Mäkinen, Veli
Background: A metagenomic sample is a set of DNA fragments, randomly extracted from multiple cells in an environment, belonging to distinct, often unknown species. Unsupervised metagenomic clustering aims at partitioning a metagenomic sample into sets that approximate taxonomic units, without using reference genomes. Since samples are large and steadily growing, space-efficient clustering algorithms are strongly needed. Results: We design and implement a space-efficient algorithmic framework that solves a number of core primitives in unsupervised metagenomic clustering using just the bidirectional Burrows-Wheeler index and a union-find data structure on the set of reads. When run on a sample of total length n, with m reads of maximum length ℓ each, on an alphabet of total size σ, our algorithms take O(n(t+logσ)) time and just 2n+o(n)+O(max{ℓ σlogn,K logm}) bits of space in addition to the index and to the union-find data structure, where K is a measure of the redundancy of the sample and t is the query time of the union-find data structure. Conclusions: Our experimental results show that our algorithms are practical, they can exploit multiple cores by a parallel traversal of the suffix-link tree, and they are competitive both in space and in time with the state of the art.
A Framework for Space-Efficient String Kernels
(Springer, 2017-02-17) Belazzougui, Djamal; Cunial, Fabio
String kernels are typically used to compare genome-scale sequences whose length makes alignment impractical, yet their computation is based on data structures that are either space-inefficient, or incur large slowdowns. We show that a number of exact kernels on pairs of strings of total length n, like the k-mer kernel, the substrings kernels, a number of length-weighted kernels, the minimal absent words kernel, and kernels with Markovian corrections, can all be computed in O(nd) time and in o(n) bits of space in addition to the input, using just a rangeDistinct data structure on the Burrows–Wheeler transform of the input strings that takes O(d) time per element in its output. The same bounds hold for a number of measures of compositional complexity based on multiple values of k, like the k-mer profile and the k-th order empirical entropy, and for calibrating the value of k using the data. All such algorithms become O(n) using a suitable implementation of the rangeDistinct data structure, and by concatenating them to a suitable BWT construction algorithm, we can compute all the mentioned kernels and complexity measures, directly from the input strings, in O(n) time and in O(n log ⁡σ) bits of space in addition to the input, where σ is the size of the alphabet. Using similar data structures, we also show how to build a compact representation of the variable-length Markov chain of a string T of length n, that takes just 3n log ⁡σ+o(n log ⁡σ) bits of space, and that can be learnt in randomized O(n) time using O(n log ⁡σ) bits of space in addition to the input. Such model can then be used to assign a probability to a query string S of length m in O(m) time and in 2m+o(m) bits of additional space, thus providing an alternative, compositional measure of the similarity between S and T that does not require alignment.
A new hybrid model of convolutional neural networks and hidden Markov chains for image classification
(Springer, 2023-05) Goumiri, Soumia; Benboudjema , Dalila; Pieczynski, Wojciech
Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely used in image processing. This paper presents a new hybrid model composed of both CNNs and HMCs. The CNN model is used for feature extraction and dimensionality reduction and the HMC model for classification. In the new model, named CNN-HMC, convolutional and pooling layers of the CNN model are applied to extract features maps. Also a Peano scan is applied to obtain several HMCs. Expectation–Maximization (EM) algorithm is used to estimate HMC’s parameters and to make the Bayesian Maximum Posterior Mode (MPM) classification method used unsupervised. The objective is to enhance the performances of the CNN models for the image classification task. To evaluate the performance of our proposal, it is compared to six models in two series of experiments. In the first series, we consider two CNN-HMC and compare them to two CNNs, 4Conv and Mini AlexNet, respectively. The results show that CNN-HMC model outperforms the classical CNN model, and significantly improves the accuracy of the Mini AlexNet. In the second series, it is compared to four models CNN-SVMs, CNN-LSTMs, CNN-RFs, and CNN-gcForests, which only differ from CNN-HMC by the second classification step. Based on five datasets and four metrics recall, precision, F1-score, and accuracy, results of these comparisons show again the interest of the proposed CNN-HMC. In particular, with a CNN model of 71% of accuracy, the CNN-HMC gives an accuracy ranging between 81.63% and 92.5%.
A novel descriptor (LGBQ) based on Gabor filters
(Springer, 2023-12-23) Aliradi, Rachid; Ouamane , Abdelmalik
Recently, many existing automatic facial verification methods have focused on learning the optimal distance measurements between facials. Especially in the case of learning facial features by similarity which can make the proposed descriptors too weak. To justify filling this gap, we have proposed a new descriptor called Local Binary Gabor Quantization (LGBQ) for 3/2D face verification based on Gabor filters and uses tensor subspace transformation. Our main idea is to binarize the responses of eight Gabor filters based on eight orientations as a binary code which is converted into a decimal number and combines the advantage of three methods: Gabor, LBP, and LPQ. These descriptors provide more robustness to shape variations in face parts such as expression, pose, lighting, and scale. To do this, we have chosen to merge two techniques which are multilinear whitened principal component analysis (MWPCA) and tensor exponential discriminant analysis (TEDA). The experimentation is using two publicly available databases, namely, Bhosphorus, and CASIA 3D face database. The results show the supremacy of our method in terms of accuracy and execution time compared to state-of-the-art methods.
A Semantic vector space and features-based approach for automatic information filtering
(Elsevier, 2004) Nouali, Omar; Blache, Philippe
With advances in communication technology, the amount of electronic information available to the users will become increasingly important. Users are facing increasing difficulties in searching and extracting relevant and useful information. Obviously, there is a strong demand for building automatic tools that capture, filter, control and disseminate the information that will most likely match a user's interest. In this paper we propose two kinds of knowledge to improve the efficiency of information filtering process. A features-based model for representing, evaluating and classifying texts. A semantic vector space to complement the features-based model on taking into account the semantic aspect. We used a neural network to model the user's interests (profiles) and a set of genetic algorithms for the learning process to improve filtering quality. To show the efficacy of such knowledge to deal with information filtering problem, particularly we present an intelligent and dynamic email filtering tool. It assists the user in managing, selecting, classifying and discarding non-desirable messages in a professional or non-professional context. The modular structure makes it portable and easy to adapt to other filtering applications such as the web browsing. We illustrate and discuss the system performance by experimental evaluation results
A Survey on Distributed Graph Pattern Matching in Massive Graphs
(ACM, 2021-02) Bouhenni, Sarra; Yahiaoui, Saïd; Nouali-Taboudjemat, Nadia; Kheddouci, Hamamache
Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.
Ad hoc networks routing protocols and mobility
(2006-04) Djenouri, Djamel; Derhab, Abdelouahid; Badache, Nadjib
An ad hoc network is a temporary infrastructureless network, formed dynamically by mobile devices without turning to any existing centralized administration. To send packets to remote nodes, a node uses other intermediate nodes as relays, and ask them to forward its packets. For this purpose, a distributed routing protocol is required. Because the devices used are mobile, the network topology is unpredictable, and it may change at any time. These topology changes along with other intrinsic features related to mobile devices, such as the energy resource limitation, make ad hoc networks challenging to implement efficient routing protocols. In this paper, we drive a GloMoSim based simulation study, to investigate the mobility effects on the performance of several mobile ad hoc routing protocols.
Adaptive Fault Tolerant Checkpointing Algorithm for Cluster Based Mobile Ad Hoc Networks
(Elsevier, 2015) Mansouri, Houssem; Badache, Nadjib; Aliouat, Makhlouf; Pathan, Al-Sakib Khan
Mobile Ad hoc NETwork (MANET) is a type of wireless network consisting of a set of self-configured mobile hosts that can communicate with each other using wireless links without the assistance of any fixed infrastructure. This has made possible to create a distributed mobile computing application and has also brought several new challenges in distributed algorithm design. Checkpointing is a well explored fault tolerance technique for the wired and cellular mobile networks. However, it is not directly applicable to MANET due to its dynamic topology, limited availability of stable storage, partitioning and the absence of fixed infrastructure. In this paper, we propose an adaptive, coordinated and non-blocking checkpointing algorithm to provide fault tolerance in cluster based MANET, where only minimum number of mobile hosts in the cluster should take checkpoints. The performance analysis and simulation results show that the proposed scheme performs well compared to works related.
Algerian Scientific Abstracts : un système d'information pour la valorisation de la recherche scientifique algérienne
(Association française des documentalistes et des bibliothécaires spécialisés, Paris, FRANCE, 1997) Bakelli, Yahia
Les publications scientifiques et techniques sont indispensables à la valorisation des résultats de la recherche. La concrétisation du transfert d'information qu'elles assurent suppose l'existence d'un dispositif d'accès et d'exploitation : un système d'information signalant ces publications de façon systématique, exhaustive et rigoureuse. La projection de cette hypothèse au cas de l'Algérie fait apparaître des dysfonctionnements dans le système de diffusion de l'IST nationale. Le projet ASA, initié au CERIST depuis 1993, a pour vocation d'y remédier : il s'agit d'une banque de données bibliographiques baptisée Algerian scientific abstracts, qui recense et analyse les publications produites au niveau national dans tous les domaines scientifiques et techniques. Un premier noyau de références a été constitué et gravé sur un cédérom prototype, actuellement en test, qui sert à la promotion du projet ASA auprès de toutes les parties concernées : établissements universitaires, sociétés savantes, entreprises, administrations, etc.
An adaptive hierarchical master-worker (AHMW) framework for grids - Application to B&B algorithms
(Elsevier, 2012) Bendjoudi, Ahcène; Melab, Nouredine; Talbi, El-Ghazali
Well-suited to embarrassingly parallel applications, the master–worker (MW) paradigm has largely and successfully used in parallel distributed computing. Nevertheless, such a paradigm is very limited in scalability in large computational grids. A natural way to improve the scalability is to add a layer of masters between the master and the workers making a hierarchical MW (HMW). In most existing HMW frameworks and algorithms, only a single layer of masters is used, the hierarchy is statically built and the granularity of tasks is fixed. Such frameworks and algorithms are not adapted to grids which are volatile, heterogeneous and large scale environments. In this paper, we revisit the HMW paradigm to match such characteristics of grids. We propose a new dynamic adaptive multi-layer hierarchical MW (AHMW) dealing with the scalability, volatility and heterogeneity issues. The construction and deployment of the hierarchy and the task management (deployment, decomposition of work, distribution of tasks, . . .) are performed in a dynamic collaborative distributed way. The framework has been applied to the parallel Branch and Bound algorithm and experimented on the Flow-Shop scheduling problem. The implementation has been performed using the ProActive grid middleware and the large experiments have been conducted using about 2000 processors from the Grid’5000 French nation-wide grid infrastructure. The results demonstrate the high scalability of the proposed approach and its efficiency in terms of deployment cost, decomposition and distribution of work and exploration time. The results show that AHMW outperforms HMW and MW in scalability and efficiency in terms of deployment and exploration time.
An end-to-end secure key management protocol for e-health applications
(Elsevier, 2015) Abdmeziem, Mohammed Riyadh; Tandjaoui, Djamel
Key distribution is required to secure e-health applications in the context of Internet of Things (IoT). However, resources constraints in IoT make these applications unable to run existing key management protocols. In this paper, we propose a new lightweight key management protocol. This protocol is based on collaboration to establish a secure end-to-end communication channel between a highly resource constrained node and a remote entity. The secure channel allows the constrained node to transmit captured data while ensuring confidentiality and authentication. To achieve this goal, we propose offloading highly consuming cryptographic primitives to third parties. As a result, the constrained node obtains assistance from powerful entities. To assess our protocol, we conduct a formal validation regarding security properties. In addition, we evaluate both communication and computational costs to highlight energy savings. The results show that our protocol provides a considerable gain in energy while its security properties are ensured.
ArA*summarizer: An Arabic text summarization system based on subtopic segmentation and using an A* algorithm for reduction
(Wiley, 2020-04-19) Bahloul, Belahcene; Aliane, Hassina; Benmohammed, Mohamed
Automatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster-based, and graph-based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall-oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n-gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works.
AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News & Hate Speech Detection Dataset
(Elsevier, 2021) Hadj Ameur, Mohamed Seghir; Aliane, Hassina
Along with the COVID-19 pandemic, an "infodemic" of false and misleading information has emerged and has complicated the COVID-19 response efforts. Social networking sites such as Facebook and Twitter have contributed largely to the spread of rumors, conspiracy theories, hate, xenophobia, racism, and prejudice. To combat the spread of fake news, researchers around the world have and are still making considerable efforts to build and share COVID-19 related research articles, models, and datasets. This paper releases "AraCOVID19-MFH"1 a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset. Our dataset contains 10,828 Arabic tweets annotated with 10 different labels. The labels have been designed to consider some aspects relevant to the fact-checking task, such as the tweet’s check worthiness, positivity/negativity, and factuality. To confirm our annotated dataset’s practical utility, we used it to train and evaluate several classification models and reported the obtained results. Though the dataset is mainly designed for fake news detection, it can also be used for hate speech detection, opinion/news classification, dialect identification, and many other tasks.
Automatic Classification and Filtering of Electronic Information: Knowledge-Based Filtering Approach
(Zarqa Private University, Jordan, 2004) Nouali, Omar; Blache, Philippe
In this paper we propose an artificial intelligent approach focusing on information filtering problem. First, we give an overview of the information filtering process and a survey of different models of textual information filtering. Second, we present our E-mail filtering tool. It consists of an expert system in charge of driving the filtering process in cooperation with a knowledge-based model. Neural networks are used to model all system knowledge. The system is based on machine learning techniques to continuously learn and improve its knowledge all along its life cycle. This email filtering tool assists the user in managing, selecting, classify and discarding non-desirable messages in a professional or non-professional context. The modular structure makes it portable and easy to adapt to other filtering applications such as web browsing. The performance of the system is discussed.