Search

The Fuzzy Inference System (FIS) is frequently utilized in a variety of Text Mining applications. In the text processing domains, where the amount of the processed data is vast, inserting manual rules for FIS remains a real issue, especially in the text processing domains, where the size of the processed databases is enormous. Therefore, an automated and optimal inference rules (IR) selection strengthens the FIS process. In this work, we propose to apply the FP-Growth as an association model algorithm and an automatic way to identify IR for fuzzy text vectorization. Once the fuzzy vectors are generated, we call the selection variables algorithms, e.g., Info Gain and Relief, to reduce the given descriptor dimensionality. To test the new descriptor performance, we propose multi-classes text classifification systems using several machine learning algorithms. Applying benchmarked databases, the new technique to produce Fuzzy descriptors achieves a signifificant gain in time, precision rules, and weighting quality. Moreover, comparing the classifification systems, the accuracy is improved by 10% comparing with other approaches.

Much of the Internet's communication is encrypted, and its content is only accessible at two endpoints, a client and a server. However, any encryption requires a key that must be negotiated without being revealed to potential attackers. The so-called TLS (Transport Layer Security) handshake is often used for this task without obviating that many fundamental parameters of TLS connections are transmitted explicitly. Thus, third parties have access to metadata, including information about the endpoints, how the connection is used. On the other hand, QoS is considered the central part of the communication used to judge the deliverable quality through several parameters (latency, jitter ...). This document describes a secure approach and meets mainly the requirements of quality of service on a communication channel (free, loaded, congested ...), using the robustness and flexibility of the TLS protocol represented on the characteristics of existing encryption keys on its list of "ciphers suites." We focused more particularly on the AES key (Advanced Encryption Standard), including the different sizes (128,192,256), given its resistance to various classical attacks (differential, linear, ...) and its lightness compared to other protocols such as DES, 3DES ... This method is useful in continuous communications in a time axis (video sequence, VOIP call...).

Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be victim of distributed attacks. Attackers can perform intrusions to breakdown the network during the routing process, intercept gathered data by dropping or re-sharing them. To avoid the increasing of security issues, many attack identification models were proposed in WSNs in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. In this work, a new method is introduced to analyze and classify WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security, it efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system which learn from routing dataset to identify network malicious entries. The proposed models is based on Hidden Markov Model (HMM), Gaussian Mixture Model (GMM) stochastic assumptions. Also, dimensionality reduction technique was deployed to select the most relevant features for training. The experimentation phase was realized on own made dataset reflecting different network situations of normal and attacked cases. The outcomes performances of the proposed method were obtained with classification accuracy of 92.18% using 2HMM/3GMMclassifier. This result demonstrate the quality of our proposed approach compared with existing literature and its usefulness to improve the security of WSN.

Evolving of Wireless Sensor Network (WSNs) a type of infrastructure-less networks by tens, hundreds, thousandths, or billions of devices that provide indispensable services for the phenomenon study become more used in many industries with many applications. Forest fire tracking, environmental monitor, healthcare, seismic and volcano surveillance, Internet of Devices and everything, and now internet of vehicles is the new trend of network of devices. These devices and things mostly exchange data between many sources and destinations using a special computer mechanism, the routing protocols to optimized the using of network and devices resource to retrieve their path.Additionally, constraints like network capabilities, storage, computational, energy consumption, and so on, many researches were conducted to proposed routing protocols that limited the network effort and the all of things’ interaction to extend the lifetime. In the present paper Antnet routing protocol which is classed in the Ant Colony Optimization heuristic is inspired on the ants behavior to define a stochastic based model permitting to predict the ants path to reach final destination using biological substance deposit on all visited paths to establish a new form of routing table that can be exploit for the purpose of the optimization objective. Conducted experimentation and obtained results show that the using of the pheromone in multiple paths by ants during trips permit to conserve effectively the available resources, reduce control overhead, residual overall, and optimize the energy consumption. Our new routing mechanism based on stochastic models performs well that compared existing routing.

Passwords/passphrases can be either system generated or user-selected. A combination of both approaches is also possible—encryption created by the system and assigned to the user by the information system meeting the policy requirements. Policy rules can be designed to increase security and usability factors, such as information storage and retrieval. This paper proposes an algorithm dedicated to the security of passphrases in an online authentication, so the passphrase entered will be stored in a remote database. Through an SHA-3 hash function, the system must hash the pass phase. Before storage, the system must apply random rotations on the already generated HASH while eliminating any traceability performed on the different transactions performed. To prevent the hacker from using them recurrently if he wants to attack our database. Then the system must recover the real HASH and then the passphrase based on the data provided by the user in the form of codes.

Image mosaicking is a series of successive algorithms that use a sequence of images or a video of a scene to create a single image with a wider field of view of the whole scene. In most cases, when dynamic objects exist in the input data, issues, such as ghosting, parallax effects or object duplication, are visible in the resulting mosaic. These technical errors appear when the objects in motion aren't properly treated. In order to create good result mosaics, a new method is presented in this paper. The proposed approach uses all the images at the same time to divide the images into different areas by using k-means clustering to create categories, then each category is recommended from the original images with a recommender system. In fact, by considering each image as a user, each pixel as an item, and each item belonging to a category, it is possible to use a recommender system by computing scores with the item profiles. The resulting mosaic will then be a new user to the system. Furthermore, by clustering the images, projection errors are avoided and a better quality mosaic can be created as is seen in the obtained results.

Automatic Speech Recognition (ASR) for Amazigh speech, particularly Moroccan Tarifit accented speech, is a less researched area. This paper focuses on the analysis and evaluation of the first ten Amazigh digits in the noisy conditions from an ASR perspective based on Signal to Noise Ratio (SNR). Our testing experiments were performed under two types of noise and repeated with added environmental noise with various SNR ratios for each kind ranging from 5 to 45 dB. Different formalisms are used to develop a speaker independent Amazigh speech recognition, like Hidden Markov Model (HMMs), Gaussian Mixture Models (GMMs). The experimental results under noisy conditions show that degradation of performance was observed for all digits with different degrees and the rates under car noisy environment are decreased less than grinder conditions with the difference of 2.84% and 8.42% at SNR 5 dB and 25 dB, respectively. Also, we observed that the most affected digits are those which contain the "S" alphabet.

Image mosaicking is a combination of algorithms that use two or several images to create a single image. The resulting mosaic is a representation of a scene of the used images with a larger field of vision. However, since dynamic objects can exist in the overlap regions of these images, ghosting and parallax effects appear, therefore poor results are obtained. To overcome these unwanted effects and to achieve better results, a new method is presented in this paper. This approach uses a new way to detect dynamic objects in the common areas by using a fractional Brownian motion with a predetermined similarity function instead of a noise function, the Zero Normalized Cross Correlation. Thus, it will ensure that a map is created with each pixel having a unique value based on their surroundings even in homogeneous areas. Furthermore, this new approach combines the previously computed map with the machine learning algorithm A* for a fast and efficient way to find an optimal seamline. Consequently, the obtained experimental results were compared with different methods and better results were obtained as can be seen by a better quality seamline measure, a result mosaic without any artifacts and a faster computation time.

Per-pixel extrusion mapping consists of creating a virtual geometry stored in a texture over a polygon model without increasing its density. There are four types of extrusion mapping, namely, basic extrusion, outward extrusion, beveled extrusion, and chamfered extrusion. These different techniques produce satisfactory results in the case of plane surfaces, but when it is about the curved surfaces, the silhouette is not visible at the edges of the extruded forms on the 3D surface geometry because they not take into account the curvature of the 3D meshes. In this paper, we presented an improvement that consists of using a curved ray-tracing to correct the silhouette problem by combining the per-pixel extrusion mapping techniques and the quadratic approximation computed at each vertex of the 3D mesh.

Per-pixel extrusion mapping is an image-based modeling and rendering technique that can be used in a large variety of computer graphic applications. This technique makes it possible to create geometries without polygonal meshes by extrusion of a basic form stored in a 2D binary image. It is available in four versions: basic extrusion, outward extrusion, beveled extrusion, and chamfered extrusion, and all allow the production of satisfactory results in terms of interactivity or visual quality. The problem is that the extruded surfaces do not exhibit microrelief effects and have a lack of realistic superficial wrinkles. To solve this problem, we proposed in this paper a combination of the per-pixel extrusion mapping and the bump mapping in order to add a microrelief effect to extruded surfaces without changing the basic geometry. For the real-time synchronization between the depth scaling of the microreliefs and the resulting shading, we proposed to use a scale factor to further disturb the normal in a function of depths.

As part of our contributions to researches on the ongoing COVID-19 pandemic worldwide, we have studied the cough changes to the infected people based on the Hidden Markov Model (HMM) speech recognition classification, formants frequency and pitch analysis. In this paper, An HMM-based cough recognition system was implemented with 5 HMM states, 8 Gaussian Mixture Distributions (GMMs) and 13 dimensions of the basic Mel-Frequency Cepstral Coefficients (MFCC) with 39 dimensions of the overall feature vector. A comparison between formants frequency and pitch extracted values is realized based on the cough of COVID-19 infected people and healthy ones to confirm our cough recognition system results. The experimental results present that the difference between the recognition rates of infected and non-infected people is 6.7%. Whereas, the formant analysis variation based on the cough of infected and non-infected people is clearly observed with F1, F3, and F4 and lower for F0 and F2.

Clustering is a predominant technique used in image segmentation due to its simple, easy and efficient approach. It is very important for the analysis, extraction and interpretation of images; which makes it used in multiple applications and in various fields. In this article, we propose a different image segmentation technique based on the cooperation between an optimization algorithm which is the Cuckoo Search Algorithm (CSA) and a clustering technique which is the Fuzzy C-means (FCM). The clustering method we propose goes through two major steps. In the first step, CSA explores the entire search space of the specified data to find the optimal clustering centers. Subsequently, these centers are evaluated using a new objective function. The result of the first step is used to initialize the FCM algorithm in the second step. The efficiency of the suggested method is measured on several images selected from the BSD300 database and we compare it with other algorithms such as FCM optimized by genetic algorithms (FCM-GA) and FCM optimized by particle swarm optimization (FCM-PSO). The experimental results on the different algorithms used in this paper show that the proposed method improves the segmentation results, based on the analysis of the best values of fitness, MSE, PSNR, CC, RI, GCE, BDE and VOI.

Secure routing is a difficult task because of the limited nature of wireless sensor network resources. This paper provides a Hidden Markov Model solution to identify malicious nodes in wireless sensor networks through prevention of black hole attack. Our proposed approach based on a new routing algorithm which analyses shortest path in order to avoid malicious node path. Our results demonstrate the success and the efficiency of our proposed routing algorithm.

Per-pixel Revolution Mapping is an image-based modeling and rendering technique or IBMR (Image-Based Modeling and Rendering). It consists of creating a virtual 3D objects without polygonal meshes. This technique uses a single RGBA texture that stores the needed data to generate the revolved surface. The main problem with this technique is that the 3D revolved models are rendered without realistic surface wrinkles. In this paper, we presented an improvement to enhance the realism of the 3D revolved models by combining the revolution mapping and the bump mapping. In order to synchronize between real-time depth scaling of the microrelief and the resulting shading, we have added a scaling factor that makes it possible to have a realistic depth animation. This new technique creates very convincing 3D models with realistic looking surface wrinkles and allows rendering at interactive frame rates.

In this paper we examined the human voice of 20 adults (20 smokers and 20 non-smokers) to determine the effects of cigarette smoking on formants frequency, pitch, shimmer and jitter based on 3 Amazigh language vowels (A, I, U). The statistical data parameters are collected from male Moroccan speakers aged between 26 and 50 years old. Our results show that, the pitch values of smokers are lower compared to those of non-smokers. Also, smokers’ formants frequency F1 and F2 are close to non-smokers ones for the three considered vowels .Whereas, F3 and F4 are lower in the case of smokers. Shimmer and Jitter analysis showed higher values for these parameters among smoker.

The normality tests are widely used to estimate the difference between an empirical distribution and the normal distribution. In this paper, we propose to apply the Jarque–Bera normality test (JB-test) in the face recognition field. In order to find out the face areas, which can be approximately represented using a normal distribution, we propose to build a new descriptor based on the JB- test and Local Binary Pattern (LBP) descriptor. To predict whether a pixel belongs to the homogeneous area, Jarque–Bera Local Binary Pattern (JB-LBP) computes the confidence interval basing on the JB- test and uses it later to generate a new pixel grayscale value. The obtained results show that the homogeneous areas pixels are in same way encoded. Therefore, we can easily distinguish them from the peaks areas. Moreover, to reveal that the proposed face representation gives better performances, we combine it with the K-means clustering, the K-Nearest Neighbors (KNN) and the Multi-layers Perceptron (MLP).

Automatic speech recognition is a technology that allows a computer to transcribe in real time spoken words into readable text. In this work an HMM automatic speech recognition system was created to detect smoker speaker. This research project is carried out using Amazigh language for comparison of the voice of normal persons to smokers one. To achieve this goal, two experiments were performed, the first one to test the performance of the system for non-smokers for different parameters. The second experiment concern smokers speakers. The corpus used in this system is collected from two groups of speaker, non-smokers and smokers native Morocan tarifit speakers aged between 25 and 55 years. Our experimental results show that we can use our system to make diagnostic for smoking people and confirm that a speaker is smoker when the observed recognition rate is below 50%.

This paper presents an approach to reconstructing 3D objects based on the generation of dense depth map. From a two 2D images (a pair of images) of the same 3D object, taken from different points of view, a new grayscale image is estimated. It is an intermediate image between a purely 2D image and a 3D image where each pixel of this image represents a z-height according to its gray level value. Our objective therefore is to play on the precision of this map in order to prove the interest and effectiveness of this map on the quality of the reconstruction.

Sentiment analysis is a very substantial area of research in our environment. Many studies have focused on the topic in recent years. It has rapidly gained interest due to the unusual volume of opinion-bearing data on the Internet (Big Social Data). In this paper, we focus on sentiment environment analysis from Amazon customer reviews shared by a machine learning based approach. This process starts with the collection of reviews and their annotation followed by a text pre-processing phase in order to extract words that are reduced to their root. These words will be used for the construction of input variables using several combinations of extraction and weighting schemes. Classification is then performed by a supervised Machine Learning classifier. The results obtained from the experiments are very promising.

In this work, we propose a method for reconstructing three-dimensional scenes from a sequence of images of which only one image is calibrated. The system is initialized by a calibrated image. The projection matrices of the other images in the sequence are estimated, from the first projection matrix (obtained by the calibration of the camera) and the matching between the images, by solving a system of linear equations. The intrinsic and extrinsic parameters of the camera used are estimated from the Cholesky decomposition of the projection matrix. Finally, obtaining the 3D scene is based on the Euclidean reconstruction of the detected and matched control points in the pairs of images.

Scalability is an important and crucial issue which in routing protocols for Wireless Sensor Networks (WSNs). In this paper, we present an approach to achieving a balanced energy consumption rate using dynamic clustering to provide scalability in WSN. The proposed work in this paper is based on the dynamic clustering using k-means compared to LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most simple and effective clustering solutions widely deployed for WSN. The simulation results show that our proposed algorithm significantly improves high network scalability compared to LEACH.

Securing Data in the cloud based on Fully Homomorphic Encryption (FHE) is a new and potential form of security that allows computing on encrypted data without decrypted it first. However, a practical FHE solution is not available for implementation today. In this work, we propose a platform based on open source solutions to perform data computations (addition and multiplication) on encrypted form. In addition, taking account of efficiently and the security component, the most popular partially homomorphic encryption algorithms (RSA, Paillier and ElGamal) are studied to analyze the process times of encryption, decryption and computation of each algorithm. Furthermore, to compromise between performance and security, we need to study different key sizes and different data sizes as parameters.

In this study, we propose a new method for indexing and retrieval of 3D models in large databases base d on binary images extracted from the 3D object called “ level cut” LC. These cuts are obtained by the inter section of the set of the plans with the 3D object. A set of e quidistant parallel plans generates by the intersec tion with the 3D object a set of cuts that used to indexing the 3D m odel. We are based on these cuts to describe the 3D object by using the vectors descriptors based on these cuts. To validate our descriptor we extract a test databa se from the NTU base. The robustness of our descriptor is well demonstrated by the comparison with the two will known descriptors, the 3D Zernike descriptor and the 3D i nvariant moment’s descriptor. The topological problem of the external surfaces that representing the 3D object h as been confronted, that shows the superiority of t he cut method, because it is keeping the external surfaces details of the 3D object during the cutting step.

This work proposes a method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used with fundamental matrices to determine the projection matrices. The present approach is based on the formulation of a nonlinear cost function from the determination of a relationship between two points of the scene and their projections in the image planes. The resolution of this function enables us to estimate the intrinsic parameters of different cameras. The strong point of the present approach is clearly seen in the minimization of the three constraints of a self-calibration system (a pair of images, 3D scene, any camera): The use of a single pair of images provides fewer equations, which minimizes the execution time of the program, the use of a 3D scene reduces the planarity constraints, and the use of any camera eliminates the constraints of cameras having constant parameters. The experiment results on synthetic and real data are presented to demonstrate the performance of the present approach in terms of accuracy, simplicity, stability, and convergence.

This paper presents a new approach to be used for people detection and tracking in image sequences based on color control points and skin color modeling. This method aims to track these people in complex situations such as players on a soccer field. Each person in the image is represented by several control points which are obtained using a color version of the Harris algorithm detector. Each control point is characterized by the local appearance which is a vector of local characteristics. Then we determine the rules that define the skin regions in three different color spaces such as RGB, HSV and YCbCr, and we apply these rules to our images to segment skin regions. Using a set of control points and skin regions allows us to track a person by matching control points based on the measure of ZNCC correlation «Zero mean Normalized Cross Correlation». The simulations and experimental results show the robustness of our algorithms in terms of stability and convergence. Performance is illustrated by some examples. Thus, our method fits well with the noise conditions.

In this paper, we are interested in the problem of Euclidean 3D reconstruction of unknown objects by passive stereo vision method. Our method is based on the combination between Harris and Sift interest point detectors, to take advantage of the power of these two detectors, which will be useful when matching step, as a key step for 3D reconstruction, In order to have a sufficient number of matches distributed on the images. These matches will be used to estimate the 3D points (the projection matrices will be estimated after calibration using 3D Calibration Pattern). Finally, a 3D mesh is constructed by 3D Delaunay triangulation, applied to the 3D points reconstructed. Experimental results prove that this method is practical and gives satisfying results without going through the propagation step.

In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation. The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45{\deg} and 135{\deg}) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach., Comment: 12 pages,13 figure,International Journal on Natural Language Computing(IJNLC),ISSN:2278-1307[Online];2319-4111[Print],August 2013, Volume 2, Number 4

We emphasise on the influence of geometric constraints in 3D reconstruction. In fact, we have used a new approach of 3D reconstruction which is based on bundle adjustment optimisation and constraints. Our purpose is to achieve 3D reconstruction in a simple, fast and efficient way without any prior information about cameras and 3D space. Constraints use brings us many advantages; it helps us to reduce estimated parameters number and stabilises good results quality. We try as much as possible through a comparative analysis to prove the importance of constraints use, in terms of results reliability, process speed and convergence rate. We use uncalibrated views, so we don't have any pertinent initial values used in Levenberg-Marquardt. On the hand, and thanks to the estimation of cameras' positions with constraints during Levenberg-Marquardt optimisation, we solve with the convergence problem satisfactorily. Several data will be used with the aim to demonstrate the efficiency of our present approach under different uses and kinds of constraints.