The Second International Conference on Computer Science's Complex Systems and their Applications (Oum El Bouaghi, Algeria, May 25-26, 2021)

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2nd International Conference on Computer Science's Complex Systems and their Applications
(University of Oum El Bouaghi, 2021-05-25) Marir, Toufik; Bourouis, Abdelhabib; Benaboud, Rohallah
A comparative study of perceptual hashing algorithms: Application on fingerprint images
(University of Oum El Bouaghi, 2021-05-25) Hamadouche, Maamar; Zebbiche, Khalil; Guerroumi, Mohamed; Tebbi, Hanane; Zafoune, Youcef
Perceptual image hashing has been broadly used in the literature to authenticate images or to identify similar contents for image copy detection. It can be used to improve the security of fingerprint-based identification systems, especially to guarantee the authenticity of fingerprint images. In this paper, a comparative study of the most used techniques in the field of perceptual hashing is provided, aiming at evaluating their performance when applied to fingerprint images. The study includes five techniques, namely: A-Hash, D-Hash, P-Hash, WHash and SVD-Hash. The performance has been assessed in terms of perceptual robustness, discrimination capability and authentication characteristics, through extensive experiments. The obtained results are promising and show that overall both the A-Hash and P-Hash performed well when compared to other evaluated techniques.
A Survey on Identity-based Key Management Schemes in Mobile Ad hoc networks
(University of Oum El Bouaghi, 2021-05-25) Gasmi, Kenza; Bourouis, Abdelhabib; Benaboud, Rohallah
Mobile Ad hoc networks attract more attention over the years, but the security matter of this type of network makes it hard to achieve all of their advantages. Cryptographic key management is the cornerstone for building any robust network security solution. Identity-based cryptography is a promising solution that resists well the key escrow problem which is suitable for Mobile Ad hoc networks. In this paper, we give an overview of the most important identity-based encryption schemes proposed in the last decade; combined with other techniques to enhance it and provide better results for Mobile Ad hoc networks. Hence, we give a comparative analysis to highlight their advantages and weaknesses. This work gives insights into a recent research to point out its interesting features, take advantages of its strength, ovoid its weaknesses and to lay out the future directions in this area.
An extended artificial bee colony with skyline operator for solving the QoS uncertainty-aware web service composition under interval QoS properties
(University of Oum El Bouaghi, 2021-05-25) Seghir, Fateh; Ghizlane, Khababa
In this paper, we provide an extended artificial bee colony (EABC) algorithm with skyline operator for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the uncertain QoS properties have been expressed as intervals numbers. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then we employ the skyline operator to reduce the search space of IQSC. Whereas, the EABC algorithm has been performed to solve IQSC in a reduced search space more effectively and efficiently. To validate the performance and efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO algorithm on an interval extended version of the public QWS dataset.
An Improved Binary Particle Swarm Optimization of RFM’s for ALSAT2 Imagery
(University of Oum El Bouaghi, 2021-05-25) Mezouar , Oussama; Meskine, Fatiha; Boukerch, Issam
Rational function model (RFM) is commonly used in photogrammetric and remote sensing applications because it does not need sensor parameters. Therefore, the RFM terms or also rational polynomial coefficients (RPCs) have no physical significance but depends on many ground control points (GCPs) that make the model prone to the over parameterization problem. This paper proposes a new binary particle swarm optimization algorithm to surmount the issue of over parameterization and find the optimum combination of RPCs for the RFM by adding a new transfer function in binary PSO in order to increase the convergence speed and avoid the local minimum phenomenon. The results showed that the proposed method is compatible with different types of RFM, more stable, and gives higher accuracy than the traditional binary PSO.
Bandwidth Provision through Disjoint Multipath RPL in the IoMT
(University of Oum El Bouaghi, 2021-05-25) Kettouche, Souhila; Maimour, Moufida; Derdouri,Lakhdar
Internet of Multimedia Things (IoMT) is one extensively current topic of the Internet of Things (IoT) due to the immersive growth of multimedia applications in several fields. In LowPower and Lossy Networks (LLNs) where sensor nodes are a key component, providing a satisfactory quality of service (QoS) as well as a user quality of experience (QoE) for such applications is a challenging task. In fact, high bandwidth and substantial computation resources are required. To provide sufficient bandwidth to handle these high data rate applications, we propose to extend RPL to enable for simultaneous use of disjoint multiple paths. This is done on top of the already maintained DODAG structure with the least induced overhead. Furthermore, we suggest applying a low-complexity encoding method on the captured images. Based on both QoS and QoE metrics, we evaluate the performance of our disjoint multipath RPL (DM-RPL) for real video clip transmission using the IoTLAB testbed. Our results show that multipath provides more bandwidth as the PDR is increased. Video quality is further improved thanks to the adopted data reduction at the source. All of this translates into less energy being consumed.
Behavioural verification of limited resources systems under true concurrency semantics
(University of Oum El Bouaghi, 2021-05-25) Bouneb; Messaouda; Saidouni; Djamel Eddine
In this paper we propose a true concurrency semantics for limited resources systems using K-bounded Petri net as modeling formalism and maximality labeled transition system (MLTS) as semantics model. Indeed the model of MLTS expresses clearly the semantics of true parallelism of concurrent systems. The proposed operational maximality semantics for Kbounded Petri nets makes it possible to interpret any K-bounded Petri net in terms of MLTS. Through an example we show the interest of the proposed semantics in comparison with the interleaving semantics and the ST semantics. The comparison concerns the preservation of true concurrency and the reduction of the size of the semantics model. Furthermore, we will show that expected CTL properties may be verified on the corresponding maximality labeled transition system of a modeled system using our developed tool.
Biometric Image Encryption Scheme based on Modified Double Random Phase Encoding System
(University of Oum El Bouaghi, 2021-05-25) Yahi, Amina; Bekkouche, Tewfik; Diffellah, Nacira; Daachi, Mohamed El Hossine
In this paper, an opto-digital encryption scheme based on a modified Double Random Phase Encoding (DRPE) system is proposed. Two biometric modalities are used in this work which is the face and the corresponding finger print of the same person. Firstly the face biometric image is encrypted chaotically using the permutation-diffusion architecture. Then obtained encrypted face is multiplied element by element by a constructed mask formed by injecting the finger print image within the phase of this mask. The obtained result will be transformed into a frequency domain by the two-dimensional. Fourier transform or any of its derivatives, resulting complex image is exactly the encrypted biometric image. Experiment computer simulations confirm the efficiency of this work in terms of histogram analysis, loss data and sensitivity test when compared with existing works.
Compressed VGG16 Auto-Encoder for Road Segmentation from Aerial Images with Few Data Training
(University of Oum El Bouaghi, 2021-05-25) Abdeldjalil, Kebir; Taibi, Mahmoud; Serradilla, Francisco; Spain, Madrid
Deep Learning methods have found many applications such as segmentation, recognition and classification. However, almost all of these methods require large data-set for the training step and a long training time. Indeed, in surveillance video domain, as for many real-world applications, samples are only accessible in limited amounts owing to acquisition and experiments complexity. In this work, we introduce compressed VGG Auto-Encoder system for road image segmentation in highresolution aerial imagery. The objective of our experiments is to improve the methodology of distinguishing the road network when only few Data is available. We propose an approach based on compressed Auto-encoder; focus on avoiding the over-fitting effect by generating new data augmentation, based on basic filter transformation to increase and enhance the quality of data training, in the aim of learn an appropriate and simplified representation of data from the original data set in order to obtain a deeper insight from large data-set, and to achieve a quick segmentation training time. Our model achieve a good result and is considered as the best network for fast and accurate segmentation of road images, compared to other models. Furthermore, we provide an explanation of these techniques and some recommendation for their use in the field of deep learning.
Deep Neural Transformer Model for Mono and Multi Lingual Machine Translation
(University of Oum El Bouaghi, 2021-05-25) Khaber, Mohamed Islam; Frahta, Nabila; Moussaoui, Abdelouahab; Saidi, Mohamed
In recent years, the Transformers have emerged as the most relevant deep architecture, especially machine translation. These models, which are based on attention mechanisms, outperformed previous neural machine translation architectures in several tasks. This paper proposes a new architecture based on the transformer model for the monolingual and multilingual translation system. The tests were carried out on the IWSLT 2015 and 2016 dataset. The Transformers attention mechanism increases the accuracy to more than 92% that we can quantify by more than 4 BLEU points (a performance metric used in machine translation systems).
Distributed Secure Services Based on IoT and Blockchain for e-Health remote care
(University of Oum El Bouaghi, 2021-05-25) Reffad, Hamza; Djenaoui, Abdelatif; Alti, Adel
Nowadays, the Internet of Thing (IoT) is a potentially powerful solution for health applications. It is a smart technology that provides remote care in real time and requires low latency health data processing and transmission. The large number of connected objects to Cloud can be a problem for low-latency workloads, which is the case of several health mobile applications. To this end, Fog Computing, has emerged, where Cloud computing is extended to the edge of the network to reduce latency and network congestion. It provides a highly virtualized platform that provides health data storage on remote public Cloud servers to which users cannot be fully trusted, especially when we are dealing with sensitive data like health data. In fact, it becomes necessary to rethink a new more robust secure technique. To provide such technique, we proposed a new secure solution called IoToDChain for e-Health mobile application, based on cryptographic techniques especially Elliptic Curve Diffie Hellman-RSA and the Blockchain paradigm. They exchange of a secret key in confidential and robust manner and protect patients’ privacy in a mobile-Fog-Cloud environment. The experiments achieved promising results for good data protection against the most known attacks in healthcare systems.
Evaluation and comparison study of video streaming routing protocols in vehicular ad-hoc networks
(University of Oum El Bouaghi, 2021-05-25) Zaidi, Sofiane; Ogab, Mostafa; Khamer, Lazhar
Video streaming is a challenging issue in Vehicular Ad-Hoc Networks (VANETs), due to the strict video streaming Quality of Service (QoS) requirements, such as throughput, delivery ratio, and transmission delay. Moreover, video streaming is influenced by VANET characteristics, such as the high dynamic topology, fluctuation of vehicle density, and environmental obstacles. In VANET, video streaming can be achieved through different VANET communication types, such as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to Broadband cloud (V2B). Based on these communications, the vehicles can exchange between them the video stream over single or multi-hop link. When the video content is delivered over a multi-hop link, the vehicles have to use a routing protocol to disseminate the video stream through a path (s) between the sender (s) end the receiver (s) vehicles. In this paper, we have presented an overview of popular existing routing protocols for video streaming in VANET, such as AODV, AOMDV, DSR, and DSDV. Furthermore, we have evaluated and compared these protocols in terms of some QoS evaluation metrics, such as throughput, packet delivery ratio, and end-to-end delay in function with vehicles density in order to judge which one is outperforming for video streaming in VANET. The simulation results show that the reactive routing protocols (AODV, AOMDV, DSR) provide higher throughput and packet delivery ratio than DSDV proactive routing protocol. However, DSDV achieves lower end-to-end delay than AODV, AOMDV, DSR routing protocols
Evaluation of ANN, ICA-ANN and PSO-ANN predicting ability in the prediction of CO2 emissions during the calcination of cement raw material
(University of Oum El Bouaghi, 2021-05-25) Boukhari, Yakoub
Cement industry releases large amounts of carbon dioxide CO2 as by-product to the atmosphere during the calcination of cement raw material. In fact, the calcination is a complex process and not completely understood. The amount of CO2 emitted varies with the grain size, chemical composition, burning temperature and time to pass through the kiln during calcination process. However, due to interaction of several parameters, it is not easy to establish accurate mathematic model to calculate the real amount of CO2 emission. Moreover, using the laboratory experiments to determine the amount of CO2 emissions are not usually easy, time-consuming, expensive and require good quality of reagents and equipments. To overcome the above problems, artificial neural network (ANN), ANN optimised by imperialist competitive algorithm (ICAANN), ANN optimised by particle swarm optimization (PSOANN) are applied to predict amount of CO2 emissions. A comparative accuracy of these tools is evaluated based on the coefficient of determination R2, R2 adjusted, mean absolute percentage error (MAPE) and scatter index (SI). The results obtained are promising and demonstrate that all proposed tools represent a good alternative for the prediction of CO2 emission with adequate accuracy. PSO and ICA are capable to improve the predicting accuracy of ANN. In addition, PSO-ANN can predict slightly better than ICA-ANN. Based on testing data, the results obtained show that 98.61%, 98.18% and 97.5% of experimental data are explained by PSO-ANN, ICAANN and ANN, respectively with average relative error less than 1.41%and SI less than 0.1.
Image denoising algorithms using norm minimization techniques
(University of Oum El Bouaghi, 2021-05-25) Diffellah, Nacira; Bekkouche, Tewfik; Hamdini, Rabah
Image denoising is one of the fundamental image processing problems. Noise removal is an important step in the image restoration process. In this paper, firstly we develop and implement two different image denoising algorithms based on norm minimization, namely `1 and `2-regularization applied to images contaminated by gaussian noise. Then, after their discretization and implementation, we perform a comparison between the two methods using several test images. Through this study, the algorithm which minimizes `2-norm of gradient of image has a unique solution and it’s easy to implement, but it doesn’t accept contour discontinuities, causing the obtained solution to be smooth. The `2-norm will blur the edges of the image. In order to preserve sharp edges, `1-norm is introduced. There are different methods to solve the problem of energy minimization. In this work, we have chosen the discretization finite difference method before applying the gradient descent algorithm to optimize the signal (2D grayscale images) denoising functionality. Experiments results, show that `1 regularization encourages image smoothness while allowing for presence of jumps and discontinuities, a key feature for image processing because of the importance of edges in human vision.
Lips Recognition for Biometric Identification Systems
(University of Oum El Bouaghi, 2021-05-25) Boucetta, Aldjia; Boussaad, Leila
In recent years, researches in biometric methods have gained much attention and they have advanced to a wide scope in security concepts. Therefore, many biometric technologies have been developed and enhanced with many of the most successful security applications. Lately, lip-based biometric identification becomes one of the most relevant emerging tools, which comes from criminal and forensic real-life applications. The main purpose of this paper is to prove the benefit of lips as a biometric modality, by using both handcraft and deeplearning based feature extraction methods. So, we consider three different techniques, Histogram of Oriented Gradients(HOG), Local Binary Pattern(LBP) and pretrained Deep-CNN. All results are confirmed by a ten-fold cross-validation method using two datasets, NITRLipV1 and database1. The mean accuracy is found to be very high in all the experiments carried out. Also the feature extraction using the Inceptionv3 model always achieve highest mean accuracy.
Modularity maximization to find community structure in complex networks
(University of Oum El Bouaghi, 2021-05-25) Saoud, Bilal
Complex networks have in generally communities. These communities are very important. Network’s communities represent sets of nodes, which are very connected. In this research, we developed a new method to find the community structure in networks. Our method is based on flower pollination algorithm (FPA) witch is used in the splitting process. The splitting of networks in our method maximizes a function of quality called modularity. We provide a general framework for implementing our new method to find community structure in networks. We present the effectiveness of our method by comparison with some known methods on computer-generated and real-world networks.
Navigation of a Differential Drive Mobile Robot Using Nonlinear Model Predictive Control
(University of Oum El Bouaghi, 2021-05-25) Benchouche, Welid; Mellah, Rabah; Bennouna, Mohammed Salah
In this paper, an implementation of a very fast nonlinear model-based predictive controller using a newly developed open-source toolkit (CasADi) was used to attain the two control goals of differential drive mobile robots, point stabilization (regulation) and trajectory following (time-varying reference). The controller’s stability was assured by the addition of final state equality constraints, which in general require a long optimization horizon for feasibility. In the work presented here, we performed a full-scale simulation proving the applicability of the terminal stabilization equality constraint have been performed. The obstacle avoidance problem has been solved by adding the obstacle position as a constraint in the main optimal control problem.
NECS-based Cache Management in the Named Data Networking
(University of Oum El Bouaghi, 2021-05-25) Fathallah, Nour Al Huda; Bouziane, Hafiza; Sharafiya, Abdullah
The Information-Centric Networking ICN architectures proposed to overcome the problems of the actual internet architecture. One of the main straight points of the ICN architectures is in-network caching. The effectiveness of the adopted caching strategy, which manages and decides where to store them, influences the performance of the ICN. However, the major issue that faces the caching strategies in the ICN architectures is the strategic selection of the cache routers to store the data through its delivery path. This will reduce congestion, optimize the distance between the consumers and the required data furthermore improve latency and alleviate the viral load on the servers. In this paper, we propose a new efficient caching strategy for the Named Data Networking architecture NDN named NECS, which is the most promising architecture between all the ICN architectures. The proposed strategy reduces the traffic redundancy, eliminates the useless replication of contents, and improves the replay time for users due to the strategic position of cache routers. Besides, we evaluate the performance of this proposed strategy and compare it with three other NDN caching strategies, using the simulator network environment NdnSIM. Based on the simulations carried out, we obtained interesting and convincing results.
On the Drivers’ Behavior Evaluation using Vehicular Networks
(University of Oum El Bouaghi, 2021-05-25) Bendouma, Tahar; Tahari, Abdou El Karim; Kerrache, Chaker Abdelaziz; Boukhelkhaly, Mama Chima; Bendoumay, Rekaia; Lagraa, Nasreddine
With the emergence of connected and intelligent vehicles, various research projects aiming at reducing traffic accidents by detecting driver behavior have also emerged. These vehicles are generally equipped with cameras and sensors that can be used to detect driver’s fatigue, drowsiness, and distraction using different technologies and a multitude of classification techniques. In this work, we propose a new real-time driver behavior-detection technique based on vehicle-to-vehicle communication (V2V) and by exploiting the information carried by the periodically exchanged messages known as Cooperative Awareness Message (CAM) that are a part of the European ETSI-ITS standard (or Basic safety message BSM in the US standard). These information include the vehicle’s current speed, the average speed, the position, the acceleration, to name a few. In our proposal, each vehicle can classify its neighbors (normal, aggressive) according to its driver’s driving style. An audio or video message can be then generated to warn the driver of any vehicle presenting a danger. Simulations conduct in both rural and urban environments depict that our proposal called ”Vehicular Ad-Hoc Network Exchange Message (VanetExM)” can determine the state of the driver with a relatively high success rate and low overhead.
Robust characteristics for texture classification
(University of Oum El Bouaghi, 2021-05-25) Maarouf, Abderrazak Ayoub; Hachouf, Fella
In this paper, an exhaustive search for relevant characteristics for automatic texture classification has been carried out. These features have been extracted from different cooperative methods dealing with texture characterization. An optimal features vector has been constructed using genetic algorithms (GA) to avoid characteristics redundancy . Then texture classification has been performed using multi-class SVM, k-nearest neighbors, and random forest classifier algorithms. Obtained results on three texture databases are very satisfying against those produced by existing methods.

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