Boudjelal Meftah

University Mustapha Stambouli of Mascara · computer sciences

Today, all scientific advancements related to medical image processing aim to develop a unique computational model. The latter will mimic the way humans interpret images. In the present paper, we propose a formal approach biologically inspired by the human natural vision system’s mechanisms. To that end, we use the spiking neural network model for...

In this paper, we consider a biologically inspired spiking neural network model for motion detection. The proposed model simulates the neurons’ behavior in the cortical area MT to detect different kinds of motion in image sequences. We choose the conductance-based neuron model of the Hodgkin–Huxley to define MT cell responses. Based on the center-s...

Grey wolf optimizer (GWO) is a recent swarm intelligence metaheuristic that mimics the leadership hierarchy of grey wolves. GWO was originally developed to address continuous optimization problems however, various versions of GWO algorithms are available in the literature that have been successfully applied to a wide range of problems. This work pr...

Skin lesion is one of the most critical challenges nowadays due to the difficulty of distinguishing a benign lesion from a malignant one. Melanoma represents a malignant melanocytic type of cancer among the most dangerous ones. In contrast, basal cell carcinoma and squamous cell carcinoma represent no malignant melanocytic types of cancer that thre...

With minimal resources and trained labour, digital image processing techniques can be used for diagnostic support system in illness identification at an early stage. These methods can also aid doctors during clinical evaluations by removing the need for invasive pathological investigations. By just viewing the colour content of the image of the pal...

For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural...

In the recent era, deep learning has become a crucial technique for the detection of various forms of skin lesions. Indeed, Convolutional neural networks (CNN) have became the state-of-the-art choice for feature extraction. In this paper, we investigate the efficiency of three state-of-the-art pre-trained convolutional neural networks (CNN) archite...

A mobile ad hoc network (MANET) is a set of mobile and self-organizing nodes that cooperate to create dynamic network architecture to establish communications. Its characteristics present critical challenges: limited residual energy of nodes and transmission range, wireless links sensitivity to environmental effects, and the mobility aspect, which...

The emergence of personalized medicine and its exceptional advancements reveal new needs regarding the availability of adequate medical decision-making models. Considering detailed data on this medicine, the creation of a medical decision-making system may encounter many inhibitory factors, such as data representation, data reduction, data classifi...

Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these...

Ad Hoc wireless mobile networks are characterized by a lack of central administration and the fact that any element of the network, being very mobile, is susceptible to disappear. In an Ad Hoc network, all the elements must cooperate in order to establish a temporary network to communicate. This communication is affected by the links stability main...

Ad Hoc wireless mobile networks are characterized by a lack of central administration and the fact that any element of the network, being very mobile, and susceptible to disappear. In an Ad Hoc network, all the elements must cooperate in order to establish a temporary network to communicate. This communication is affected by the links stability mai...

High computation power is required to execute complex scientific workflows. Cloud computing resources are used viably to perform such complex workflows. Task clustering has demonstrated to be an efficient technique to decrease system overhead and to enhance the fine computational granularity tasks of a scientific workflow executed on distributed re...

The mobile ad hoc wireless networks are characterized by the absence of central administration and any network element may be very mobile. There is no fixe element within an ad hoc network. In fact, within these networks, all elements must cooperate so as to create a temporary topology which facilitates communication. To create this topology and ca...

The mobile ad hoc wireless networks are characterized by the absence of central administration and any network element may be very mobile. There is no fixe element within an ad hoc network. In fact, within these networks, all elements must cooperate so as to create a temporary topology which facilitates communication. To create this topology and ca...

Spiking neural networks (SNNs) fall into the third generation of artificial neural network models, increasing the level of realism in a neural simulation. In this paper, a spiking neural network is presented for detecting and tracking of a moving object in video sequences with a static camera. The motion estimation of the object is carried out by m...

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, computer resources and network failures are no more exceptions, but belong to the normal system behavior. Fault tolerance in grid computing improves enormously the calculation time and gives...

This paper presents a review of the state-of-the-art convolutional neural network based deep learning techniques, used for skin lesion analysis specially the problem of Melanoma including lesion segmentation; lesion classification; and lesion detection and tracking, In recent years CNN, based methods would greatly benefit the advancement of skin le...

Unlike empirical medicine centered on a common attitude for all, personalized medicine centered on the attitude adapted to the patient profile. This profile may contain heterogeneous and/or homogeneous, structured and/or unstructured, temporal and/or non-temporal data. The representation of these data in a datamining process shows a major challenge...

Automatic query expansion (AQE) based on Pseudo Relevance Feedback (PRF) is a useful technique for enhancing the effectiveness of information retrieval systems. In this article, we propose the study of the behavior of a set of features based on the query and the set of feedback documents in the aim to control automatically the convergence of the qu...

Fault Tolerance for a Scientific Workflow System in a Cloud Computing Environment

The mobile Ad hoc wireless networks are characterized by the absence of central administration and then any network element being very mobile, is likely to disappear. There is no fixed element within an Ad hoc network. In fact, within these networks, all elements must cooperate so as to create a temporary architecture which facilitates communicatio...

Many scientific workflows are composed of fine computational granularity tasks, where the task runtime may be shorter than the system overhead—the period of time during which miscellaneous work other than the user’s computation is performed. Task clustering methods merge several short tasks into a single job such that the job runtime is increased a...

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, the characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. In this paper, we propose FT-GRC a fault tolerance model that seeks to find...

Fault tolerance in grid computing improves enormously the calculation time and gives more confidence to the users of grids. The characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. We propose, in this paper, a fault tolerance model that seeks to find the most suitable substitute for the faile...

In this paper, we propose a spiking neural network model for edge detection in images. The proposed model is biologically inspired by the mechanisms employed by natural vision systems, more specifically by the biologically fulfilled function of simple cells of the human primary visual cortex that are selective for orientation. Several aspects are s...

This paper concentrates on the use of Echo State Networks (ESNs), an effective form of reservoir computing, to improve microscopic cellular image segmentation. An ESN is a sparsely connected recurrent neural network in which most of the weights are fixed a priori to randomly chosen values. The only trainable weights are those of links connected to...

Data compression is taking an important part in computer research on applications like tele-video- conferencing, remote sensing, document and medical imaging, and facsimile transmission; while our stocking disk and our passing bands are limited. This field regroups two families: Lossy compression, with acceptable loss of information, and lossless c...

Artificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest...

Pattern recognition basically assigns a label to a given input image. Pattern recognition is done on the basis of classes to which an input image belongs. A pattern could be a fingerprint image, a handwritten cursive word, a human face, or a speech signal. In this paper we consider to analyze back propagation algorithm and feed forward algorithm us...

Artificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest...

Pattern recognition basically assigns a label to a given input image. Pattern recognition is done on the basis of classes to which an input image belongs. A pattern could be a fingerprint image, a handwritten cursive word, a human face, or a speech signal. In this paper we consider to analyze back propagation algorithm and feed forward algorithm us...

This study proposes a new method of extracting and tracking a non_rigid object moving while allowing a static camera. For object extraction we first detect an object using a spiking neural networks for extracting its edge. For object tracking we take this edge as model of the object to localize and match its motion in the next frame by using a Haus...

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are present in images. Artificial neural networks have been well developed so far. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often r...

Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy for cell microscopic image segme...

Le processus de segmentation des images est l’un des plus critiques dans l'analyse d'image, dont le but est de savoir quels objets sont présentés dans les images en rassemblant des pixels entre eux suivant des critères prédéfinis. Chaque groupe de pixels forme ainsi une région ou un segment. Les réseaux de neurones artificiels ont été bien développ...

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often referr...

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron Networks (SNNs) are often refer...

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron Networks (SNNs) are often referr...