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Purpose This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency (NTF-IDF), is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages. The paper also propose a comparative study between the popular FTF-IDF and NTF-IDF and their impacts on different machine learning (ML) classifiers for document categorization goals. Design/methodology/approach After preprocessing textual data, the original Neutrosophic TF-IDF applies the neutrosophic inference system (NIS) to produce weights for terms representing a document. Using the local frequency TF, global frequency IDF and text N's length as NIS inputs, this study generate two neutrosophic weights for a given term. The first measure provides information on the relevance degree for a word, and the second one represents their ambiguity degree. Next, the Zhang combination function is applied to combine neutrosophic weights outputs and present the final term weight, inserted in the document's representative vector. To analyze the NTF-IDF impact on the classification phase, this study uses a set of ML algorithms. Findings Practicing the neutrosophic logic (NL) characteristics, the authors have been able to study the ambiguity of the terms and their degree of relevance to represent a document. NL's choice has proven its effectiveness in defining significant text vectorization weights, especially for text classification tasks. The experimentation part demonstrates that the new method positively impacts the categorization. Moreover, the adopted system's recognition rate is higher than 91%, an accuracy score not attained using the FTF-IDF. Also, using benchmarked data sets, in different text mining fields, and many ML classifiers, i.e. SVM and Feed-Forward Network, and applying the proposed term scores NTF-IDF improves the accuracy by 10%. Originality/value The novelty of this paper lies in two aspects. First, a new term weighting method, which uses the term frequencies as components to define the relevance and the ambiguity of term; second, the application of NL to infer weights is considered as an original model in this paper, which also aims to correct the shortcomings of the FTF-IDF which uses fuzzy logic and its drawbacks. The introduced technique was combined with different ML models to improve the accuracy and relevance of the obtained feature vectors to fed the classification mechanism.

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.

In this paper, we present a new technique of tridimensional reconstruction from a sequence of uncalibrated stereo images taken with cameras having varying parameters. At first, our system allows to recover initial coordinates of a set of 3D points. In this context, we have used our method of self-calibration based on the use of unknown 3D scene with its image projections and genetic algorithms to estimate all intrinsic parameters. After that extrinsic parameters are estimated based on classical pose estimation algorithms. Matching points and estimated value of intrinsic and extrinsic parameters are used to estimate initial 3D model that helps us in the initialization step. In order to have a reliable and relevant 3D reconstruction the proposed method is based on good and new exploitation of bundle adjustment (without camera poses initialization) technique based on Levenberg-Marquardt optimization with the aim to estimate our optimal 3D model that has special features compared to the classical case because it masks the pose parameters estimation in the optimization process. Finally, 3D mesh of the 3D scene is constructed with Delaunay algorithm and the 2D image is projected on the 3D model to generate the texture mapping. Experiments is conducted on real data to achieve demonstrate the validity and the performance of the proposed approach in terms of convergence, simplicity, stability and reconstruction quality.

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.

Image mosaicking is a process of assembling multiple images to create an image with a larger field of view. It is used in different studies, but some errors, like ghosting or parallax effects, could occur when the images contain dynamic elements. To avoid the failure of the mosaic and to solve these errors, a new method that searches an optimal seamline for dynamic mosaicking is presented. By finding regions that are similar between the images, and regions that are not alike, the seamline was computed by going through the similar regions and by avoiding the not common regions. To achieve this, a combination of Canny edge detector, and the outliers and inliers from the RANSAC method were used to identify these regions. Then, the regions were incorporated in an intensity difference to create a map that reveals them. Thus, the optimal seamline was computed by going through the similar regions and by avoiding the unalike regions. The experimental results show that the proposed approach is capable of generating robust mosaics against ghosting and parallax effects.

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 last years, several biometric modalities are coming back into the field of education. Especially, face recognition modality that allows authenticating humans based on their facial features. The main objective of this paper is to implement the recognition system part for a global software that is intended to be used in many applications related to the educational field. Therefore, we propose to combine the face description method based on Local Gradient Probabilistic Pattern (LGPP), the two dimensional subspace methods, and machine learning classifiers. Firstly, we extract principal face component using the LGPP descriptor. Then, 2DDWT, 2DPCA, and 2DLDA subspace methods were implemented to reduce face features. Finally, support vector machine (SVM) and artificial neural network (ANN) based machine learning algorithms are applied to classify the set of the final features vectors. The experimental results on relevant databases demonstrate the effectiveness of the proposed system.

Point matching via corresponding circles (ICC) is a technique for removing outliers (mismatches) from given putative point correspondences in image pairs. It can be used as a basis for a wide range of applications including structure-from-motion, 3D reconstruction, tracking, image retrieval, registration, and object recognition. In this paper, we propose a new method called Fast Identification of point correspondences by Corresponding Circles (FICC) that improves the quality of the rejection mismatches and reduces the cost of computing it. In particular, we propose a new strategy that aims to take better advantage of the corresponding circles and reduces the number of putative points correspondences tested by the corresponding circles in each iteration rather than all set of putative correspondences, as in the original ICC. This reduces the computing time and together with a more efficient tool for rejecting mismatches which leads to significant gains in efficiency. We provide comparative results illustrating the improvements obtained by FICC over ICC, and we compare with many state-of-the-art methods.

In this paper, we present a novel efficient face description method called Electric Virtual Binary Pattern (EVBP). The main idea of EVBP descriptor is to combine Local Binary Pattern (LBP) and our new Model based on the Virtual Electric Field. This model consider the neighborhood of each pixel as a grid of virtual electric charges that are electrostatically balanced. Then, we apply the LBP principle for this neighborhood to generate the new EVBP pixel representation. Based on the four trivial space directions, this representation is computed using the corresponding four electrical interactions. Moreover, the spatially enhanced Local Binary Pattern Histogram (eLBPH) algorithm is employed to extract features. Therefore, the proposed EVBP descriptor led to reduce the features vector size by 93.75%. Consequently, we moved from 255 bin-histograms for LBP to 16 bin-histograms for EVBP descriptor. Extensive experiments were carried on relevant databases have proved the effectiveness of the proposed approach.

This paper introduces a new mesh segmentation approach into semantic parts, most closely resemble those made by humans, which is based on the pixel color of the images used in the 3D reconstruction. This approach allows to segment the mesh into semantic and a much simpler way than most of the mesh segmentation methods that are based on the geometrical characteristics of the mesh. The principle of our method is to establish a link between the color objects of the scene and the mesh while exploiting the link between the interest points of the images brought into play and the vertices of 3D mesh. The results in great part, reflect the efficiency and performance of our method.

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%.

The main problem in the handwritten character recognition systems (HCR) is to describe each character by a set of features that can distinguish it from the other characters. Thus, in this paper, we propose a robust set of features extracted from isolated Amazigh characters based on decomposing the character image into zones and calculate the density and the total length of the histogram projection in each zone. In the experimental evaluation, we test the proposed set of features, to show its performance, with different classification algorithms on a large database of handwritten Amazigh characters. The obtained results give recognition rates that reach 99.03% which we presume good and satisfactory compared to other approaches and show that our proposed set of features is useful to describe the Amazigh characters.

Self-calibrate cameras with varying focal length.Automatic estimation of the intrinsic parameters of the camera.Works freely in the domain of self-calibration without any prior knowledge about the scene or on the cameras. We propose a new self-calibration technique for cameras with varying intrinsic parameters that can be computed using only information contained in the images themselves. The method does not need any a priori knowledge on the orientations of the camera and is based on the use of a 3 D scene containing an unknown isosceles right triangle. The importance of our approach resides at minimizing constraints on the self-calibration system and the use of only two images to estimate these parameters. This method is based on the formulation of a nonlinear cost function from the relationship between two matches which are the projection of two points representing vertices of an isosceles right triangle, and the relationship between the images of the absolute conic. The resolution of this function enables us to estimate the cameras intrinsic parameters. The algorithm is implemented and validated on several sets of synthetic and real image data.

Using a variational calculation within the effective mass approximation, we have calculated the binding energy and the polarizability of a polaron bound to a shallow hydrogenic impurity located at the center of spherical quantum dots embedded in a glass matrix, considering an infinite confining potential. The binding energy of the ground state is calculated as a function of the radius of the dots for different values of weak electric field applied in the z-direction. The polaronic effect has been considered by using the Lee–Low–Pines variational method. Our results show that the polarizability and the binding energy are very sensitive to the polaronic and the confinement effects.

This Recognition of Arabic text handwritten awaits precise recognition solutions. There are a lot of difficulties facing a good handwritten Arabic recognition system such as unlimited variant in human handwriting, similarities of different character shapes, and their location in the word. This paper presents a handwriting Arabic text recognition system. It decomposes the text image into text line images and extracts a set of simple statistical features from a narrow window which is sliding a long that text line, then it injects the resulting feature vectors to the Hidden Markov Model Toolkit (HTK). HTK is a portable toolkit for speech recognition system. In recognized state, the concatenation of characters to form words is modelled by simple lexical models, each word is modelled by a stochastic finite-state automaton (SFSA), and the concatenation of words into sentences is modelled by an n-gram language model. The proposed system is applied to a data corpus constructed by Text lines examples from the “Arabic-Numbers”, which contains 1905 sentences and 47 words. This phrase is written by 5 different peoples.