Ahmed Hammouch

Mohammed V University | um5a · Génie Electrique

In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN),...

This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sust...

Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into a...

The high dimensionality of hyperspectral images often imposes a heavy computational burden for image processing. Therefore, dimensionality reduction is often an essential step in order to remove the irrelevant, noisy and redundant bands. And consequently, increase the classification accuracy. However, identification of useful bands from hundreds or...

Nowadays, the hyperspectral remote sensing imagery HSI becomes an important tool to observe the Earth's surface, detect the climatic changes and many other applications. The classification of HSI is one of the most challenging tasks due to the large amount of spectral information and the presence of redundant and irrelevant bands. Although great pr...

Band selection is a great challenging task in the classification of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, this paper introduces a new filter approach for dimension reduction and classification of hyperspectra...

Over the past decades, the hyperspectral remote sensing technology development has attracted growing interest among scientists in various domains. The rich and detailed spectral information provided by the hyperspectral sensors has improved the monitoring and detection capabilities of the earth surface substances. However, the high dimensionality o...

The high dimensionality of hyperspectral images consisting of several bands often imposes a big computational challenge for image processing. Therefore, spectral band selection is an essential step for removing the irrelevant, noisy and redundant bands. Consequently increasing the classification accuracy. However, identification of useful bands fro...

The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and complicates the learning process. In fact, the removal of irrelevant, noisy and redundant bands helps increase the classification accura...

Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classi...

During the last decade, hyperspectral images have attracted increasing interest from researchers worldwide. They provide more detailed information about an observed area and allow an accurate target detection and precise discrimination of objects compared to classical RGB and multispectral images. Despite the great potentialities of hyperspectral t...

Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and irrelevant ones. In this context, we propose a new wrapper method based on normalized mutual information (NMI) and error probabili...

The Hyperspectral image (HSI) contains several hundred bands of the same region called the Ground Truth (GT). The bands are taken in juxtaposed frequencies, but some of them are noisily measured or contain no information. For the classification, the selection of bands, affects significantly the results of classification, in fact, using a subset of...

The Remote sensing provides a synoptic view of land by detecting the energy reflected from Earth's surface. The Hyperspectral images (HSI) use perfect sensors that extract more than a hundred of images, with more detailed information than using traditional Multispectral data. In this paper, we aim to study this aspect of communication in the case o...

Hyperspectral images (HSI) classification is a high technical remote sensing tool. The main goal is to classify the point of a region. The HIS contains more than a hundred bidirectional measures, called bands (or simply images), of the same region called Ground Truth Map (GT). Unfortunately, some bands contain redundant information, others are affe...

Remote sensing is a higher technology to produce knowledge for data mining applications. In principle hyperspectral images (HSIs) is a remote sensing tool that provides precise classification of regions. The HSI contains more than a hundred of images of the ground truth (GT) map. Some images are carrying relevant information, but others describe re...

In the feature classification domain, the choice of data affects widely the results. The Hyperspectral image (HSI), is a set of more than a hundred bidirectional measures (called bands), of the same region (called ground truth map: GT). The HSI is modelized at a set of N vectors. So we have N features (or attributes) expressing N vectors of measure...

Hyperspectral images (HSI) classification is a high technical remote sensing software. The purpose is to reproduce a thematic map . The HSI contains more than a hundred hyperspectral measures, as bands (or simply images), of the concerned region. They are taken at neighbors frequencies. Unfortunately, some bands are redundant features, others are n...

This paper seeks to detect Parkinson’s disease among healthy cases, several neurological diseases, in particular those which are very similar, that is to say representing a parkinsonian syndrome such as multi system atrophy. Early detection based on the phonatory symptoms will offer a possibility to the treatments proposed by the doctors to act eff...

Digital Technology (DT) and Artificial Intelligence (AI) are of decisive importance for the understanding of the virus and the development of prevention and control measures. They can intervene in various fields, in particular, in the deployment of mathematical modeling to analyze the transmission of the virus, structural biology to identify the st...

Cardiovascular disease is the leading cause of death worldwide. The diagnosis is made by non-invasive methods, but far from being comfortable, rapid, and accessible to everyone. Speech analysis is an emerging non-invasive diagnostic tool, and a lot of research has shown that it is efficient in speech recognition, and in detecting Parkinson's diseas...

The wireless body area networks (WBANs) has become increasingly deployed in medical care services as real-time remote monitoring applications for human health. These applications based on a hardware and software infrastructure offer the possibility of continuous remote monitoring of patient’s health parameters. Furthermore, it provides doctors with...

em>The heart is the organ that pumps blood with oxygen and nutrients into all body organs by a rhythmic cycle overlapping between contraction and dilatation. This is done by producing an audible sound which we can hear using a stethoscope. Many are the causes affecting the normal function of this most vital organ. In this respect, the heart sound c...

Indoor communication system has known an exponentially growth due to high demand of wireless data service ratio. As an alternative way, the visible light zone of the electromagnetic spectrum is explored to fulfil the future wireless data traffic requirements. Among the main issues that can be handled in the visible light communication (VLC) system...

In recent decades, researchers have proposed a number of string matching algorithms, searching for instances of one model string in another string or body of text. Channel matching is usually deployed in plagiarism, text mining, network intrusion detection, form recognition, information security, application in bioinformatics and other areas of exp...

The comparison of genomic sequences plays a key role in determining the structural and functional relationships between genes. This comparison is carried out by identifying the similarities, differences and mutations between genomic sequences. This makes it possible to study and analyze the genetic and the evolutionary relationships between organis...

Phonocardiogram signals (PCG) and electrocardiogram signals (PCG) have been used separately for decades to diagnose heart abnormalities. Combining these two synchronous signals is expected to enhance the diagnosis for better medical management of patients. This paper's objective is to highlight the performance comparison between the diagnosis of he...

This work presents an application of an alignment algorithm for correction of data obtained from capillary electrophoresis sequencing experiments. Generally, most existing methods, used in this field of application, suffer from a high computation cost. Our method is based on the principle of the discrete to continuous “DTC” approach and tries to fi...

The change in data processing conditions obtained from biological experiments, in particular, SHAPE (Selective 2′-Hydroxyl Acylation analyzed by the Extension Primer) technique, results in the time shift of the data which are in the form of signals. In this study, a SHAPE data alignment algorithm is proposed using a new pattern recognition approach...

A novel approach for separation among normal and heart murmurs sounds based on Phonocardiogram (PCG) analysis is introduced in this paper. The purpose of this work is to find the appropriate algorithm able to detect heart failures. Different features have been extracted from time and frequency domains. After the normalization step, the Principal Co...

The revolution of digital imaging has led to an explosion in the information produced. The segmentation framework is composed of three stages. First, we use (BSDM) to extract the region of interest brain; this is a pre-segmentation of brain tissues. In the second stage, we detect abnormal regions using level sets methods. This method incorporates s...

In recent decades, researchers have proposed a number of string matching algorithms, Searching for instances of one model string in another string or body of text. Channel matching is usually deployed in plagiarism, text mining, network intrusion detection, form recognition, information security, application in bioinformatics and other areas of exp...

The Hyperspectral image is a substitution of more than one hundred images of the same region called bands. To exploit the richness of this image it is necessary to reduce its dimensionality. The question is how to reduce it? This high dimensionality is also a source of confusion and adversely affects the accuracy of the classification bands and inc...

span lang="EN-US">In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen...

The essential design interest for system designers of wireless communication is modeling distortion introduced by the nonlinear behavior of the components integrated in the design. Wireless communication systems usually suffer from the nonlinearity existing in the Visible Light Communication (VLC) front-end, which is produced by the Light Emitting...

Dealing with the variation in retention time and time series is one of the most popular challenges in the field of analysing data obtained from biological experiments. In most cases, this problem can lead to invalid conclusions. This work aims to propose a new method to cope with this problem by using the principle of the transition from discrete t...

The IMS P2P network is a solution that allows the improvement of the IMS network to support p2p architectures in respect of access network technologies, in this article we propose the collaboration between IMS and the adaptation of quality with scalable video coding and peer-to-peer networks. The proposed scheme has been tested and evaluated in NS2...

In this study we introduced a method for early detecting of Parkinson’s disease (PD) in patients with rapid eye movement sleep behavior disorder (RBD). Patients suffering from RBD are at extremely high risk (> 80%) for developing PD as well as other related neurodegenerative disorders. The database used in this study contains 30 PD patients in the...

Band selection is a great challenging task in the classification of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, this paper introduces a new filter approach for dimension reduction and classification of hyperspectra...

Recently, the hyperspectral sensors has improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classif...

Nowadays, the hyperspectral remote sensing imagery HSI becomes an important tool to observe the Earth’s surface, detect the climatic changes and many other applications. The classification of HSI is one of the most challenging tasks due to the large amount of spectral information and the presence of redundant and irrelevant bands. Although great pr...

A novel method for separation between normal and abnormal heart sounds based on Phonocardiogram (PCG) is presented in this article. For features extraction phase, Mel-Frequency Cepstral Coefficients (MFCC) algorithm is used to extract information from heart sound signals. In this step, changing the frames size, during framing process, shows it infl...

This paper describes a new approach of the first and the second challenge presented by Pattern Analysis, Statistical Modeling and Computational Learning (PASCAL) Classifying Heart Sounds Challenge. The segmentation of phonocardiogram signals into the first heart sound S1 and the second heart sound S2 consists in heart sounds preprocessing, heart so...

The advancement of imaging procedures has made hyperspectral sensors fit for acquiring spectral data in many restricted and bordering bands, which brings about a high relationship between's neighboring bands and high information excess. It is important to decrease these bands previously advance analysis utilizing land cover classification and targe...

This paper presents the optimization of the wind power in a variable speed Wind Energy Conversion System (WECS) using a sensorless Maximum Power Point Tracking (MPPT) strategy that avoids mechanical measurement of the wind speed. The proposed system consists of a turbine, a direct-drive Permanent Magnet Synchronous Generator (PMSG) controlled by a...

Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into a...

In this study, we wanted to discriminate between two groups of participants (patients with Parkinson’s disease and healthy people) by analyzing 3 types of voice recordings. Firstly we collected multiple types of voice recording of three sustained vowels /a/, /o/ and /u/ at a comfortable level which was collected from the 40 participants (20 PD and...

In this paper, a two-level sensorless Maximum Power Point Tracking (MPPT) strategy is presented for a variable speed Wind Energy Conversion System (WECS). The proposed system is composed of a wind turbine, a direct-drive Permanent Magnet Synchronous Generator (PMSG) and a three phase controlled rectifier connected to a DC load. The realised generat...

In this study, we wanted to discriminate between 30 patients who suffer from Parkinson’s disease (PD) and 20 patients with other neurological diseases (ND). All participants were asked to pronounce sustained vowel /a/ hold as long as possible at comfortable level. The analyses were done on these voice samples. Firstly, an initial feature vector extr...

n this study, we wanted to discriminate between three groups of patients. Each group contains nine voice recordings collected from nine patients. The three groups are: Patients who suffer From Multiple system atrophy, patients who suffer from Parkinson’s disease and patients who suffer from other neurological disorders. We extracted from each voice...

Technological advances in signal processing, electronics, embedded systems and neuroscience have allowed the design of devices that help physicians to better assess the evolution of neurological diseases. In this context, we are interested in the development of an intelligent system for the quantification of Parkinson’s disease (PD). In order to ac...

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector...

In this Paper, we wanted to discriminate between two groups of patients (patients who suffer from Parkinson’s disease and patients who suffer from other Neurological disorders). We collected a variety of voice samples from 50 subjects using different recording devices in different conditions. Subsequently, we analyzed and extracted features from th...

Heart disease is the biggest killer in the world, it is a serious public health problem facing the world today. This problem has not only attracted the attention of doctors and cardiologists, but also that of signal processing specialists who seek to effectively detect this disease by treating cardiac signals. This article proposes a heart sounds c...

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease (PD) and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. The technique used in this stu...

The aim of this study is to discriminate between patients with Parkinson's disease and patients who suffer from other Neurological disorders by analyzing voice recordings in cepstral domain. The technique used in this study is to extract cepstral coefficient of ReAlitive SpecTrAl PLP (RASTA-PLP). The extracted RASTA-PLP cepstral coefficients were c...

This paper provides a complet structure of Wind Energy Conversion System (WECS) consisting of a variable speed wind turbine, a drive train, a Permanent Magnet Synchronous Generator PMSG, electronic power converters connected to the grid. To achieve the maximum of generated power the MPPT principle is essential, it is based on the Perturb & Observe...

This article attempts to contribute to heart abnormalities detection by suggesting a segmentation algorithm of S1 and S2 heart sounds. It describes the steps followed and the results achieved during the realization of the first PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) Classifying Heart Sounds Challenge. The perfor...

The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and complicates the learning process. In fact, the removal of irrelevant, noisy and redundant bands helps increase the classification accura...

Wireless communication is the need of the hour, so there is a huge thirst for improvement of the means of communication. Motivated by the looming crisis of radio frequency (RF) spectrum, light fidelity (Li-Fi) which is a technology attached to the visible light communications (VLC) offers many key advantages and effective solutions to the problems...

Parkinson's disease (PD) is a neurodegenerative disorder of unknown etiology. It causes, during its course, vocal impairment in approximately 90% of patients. PD patients suffer from hypokinetic dysarthria, which manifests in all aspects of speech production: respiration, phonation, articulation, nasality and prosody. To evaluate these, clinicians...

Point pattern matching (PPM), is an important problem arises in many computer visions, pattern recognition and computational geometry fields. This paper presents an important amelioration and generalization of discrete to continuous approach. With a very fast running time, numerically stable, easy to implement and dealing with large PPM application...

Early detection of a brain tumor is much easier to cure than to find out at an advanced stage. In this article we present a tumor extraction method based on the decision tree classifier for early and rapid detection. This method can also provide the tumor characteristics, the exact location and it geometric properties. Our method will be applied to...

Band selection is one of the most important problems in hyper-spectral image classification. Indeed, the presence of irrelevant and/or redundant bands can harm the performance of classification accuracy. This paper investigates the effectiveness of four mutual information feature selector (MIFS) algorithms to select the informative bands for hyper-...

In this article we present a new method for detecting and segmenting brain tumour regions weighted brain MRI in T1 (with contrast). This method consists of three main stages: (i) extracting the region of interest (brain) using our EMBE method; (ii) study and histogram analysis of the MRI image to create learning and initialise the classification al...

In this article we treat a very interesting and very important research subject that affects directly the human brain. The subject here is the tumor detection on a brain MRI. The tumors evaluation, its progressions and its area, are very important data to assist the doctors in the disease diagnosis. However, tumor observation and image analysis pro...

Most of speaker identification systems are based on the computation of distance or likelihood between the feature vectors of the unknown speaker and the models in the database. The identification process depends on the number of feature vectors, their dimensionality and the number of speakers. This research aims to develop a system able to identify...