2021 29th Conference of Open Innovations Association (FRUCT), 2021
The World Health Organization has declared that the new Coronavirus disease (Covid-19) has become... more The World Health Organization has declared that the new Coronavirus disease (Covid-19) has become a pandemic since March 2020. It consists of an emerging viral infection with respiratory swelling that can progress to atypical pneumonia. In fact, experts stress the early detection importance of those infected with COVID-19 virus. In this way, the infected patients will be isolated from others, and then prevent the virus spread. However, prompt assessment of breathing patterns is important for many medical emergencies. We present, in this paper, a deep learning technique-based COVID-19 cough and breath analysis that can recognize positive COVID-19 cases from both negative and healthy COVID-19 cough and breath recorded on smartphones or wearable sensors. Firstly, audio signals, as well as cough and breath, will be preprocessed to remove noise. After that, deep features will be extracted using the deep Long Term Short Memory (LSTM) model. Finally, the recognition step will be performed exploiting extracted audio features. Numerical results prove the efficiency of the proposed deep model in terms of high accuracy level and low loss value compared to the other techniques.
International Journal of Advanced Computer Science and Applications
The demand on high quality palm dates is increasing due to its energy value and nutrient content,... more The demand on high quality palm dates is increasing due to its energy value and nutrient content, which are of great importance in human diet. To meet consumer and market standards with large-scale production, in Oman as among the top date producer, an inline classification system is of great importance. This paper addresses the potentiality of using Machine-Learning (ML) techniques in classifying automatically, without any physical measurement, the six most popular date fruit varieties in Oman. The effect of color, shape, size, and texture features and the critical parameters of the classifiers on the classification efficiency has been endeavored. Three different ML techniques have been used for automatic classification and qualitative comparison: (i) Artificial Neural Networks (ANN), (ii) Support Vector Machine (SVM), and (iii) K-Nearest Neighbor (KNN). Based on the merge of color, shape and size features contributes to achieve the highest accuracy. Experimental results show that the ANN classifier outperforms both SVM and KNN with the highest classification accuracy of 99.2%. This developed vision system in this paper can be successfully integrated in the packaging date factories.
Abstract. For real‐time applications, there are several factors (time, cost, power) that are movi... more Abstract. For real‐time applications, there are several factors (time, cost, power) that are moving security considerations from a function centric perspective into a system architecture (hardware/software) design issue. Advanced Encryption Standard (AES) is used nowadays extensively in many network and multimedia applications to address security issues. The AES algorithm specifies three key sizes: 128, 192 and 256 bits offer‐ ing different levels of security. To deal with the amount of application and intensive computation given by security mechanisms, we define and develop a QoSS (Quality of Security Service) model for reconfigurable AES processor. QoSS has been designed and implemented to achieve a flexible trade‐off between overheads caused by security ser‐ vices and system performance. The proposed architecture can provide up to 12 AES block cipher schemes within a reasonable hardware cost. We envisage a security vector in a fully functional QoSS request to include levels of se...
Atrial Fibrillation (AF) is a health-threatening condition, which is a violation of the heart rhy... more Atrial Fibrillation (AF) is a health-threatening condition, which is a violation of the heart rhythm that can lead to heart-related complications. Remarkable interest has been given to ECG signals analysis for AF detection in an early stage. In this context, we propose an artificial neural network ANN application to classify ECG signals into three classes, the first presents Normal Sinus Rhythm NSR, the second depicts abnormal signal with Atrial Fibrillation (AF) and the third shows noisy ECG signals. Accordingly, we achieve 93.1% accuracy classification results, 95.1% of sensitivity, 90.5% of specificity and 98%. Furthermore, we yield a value of zero error and a low value of cross entropy, which prove the robustness of the proposed ANN model architecture. Thus, we outperform the state of the art by achieving high accuracy classification without pre-processing step and without high level of feature extraction, and then we enable clinicians to determine automatically the class of each patient ECG signal.
... Abstract: The goal of this study is to determine a human visible difference between two image... more ... Abstract: The goal of this study is to determine a human visible difference between two images that may be used as an excellent image quality evaluator. ... How to cite this article: Amine Samet , MAB Ayed , Nouri Masmoudi and Lazhar Khriji , 2005. ...
International Journal of Advanced Robotic Systems, 2011
This paper shows how Q-learning approach can be used in a successful way to deal with the problem... more This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.
Nowadays, there is a significant improvement in technology regarding healthcare. Real-time monit... more Nowadays, there is a significant improvement in technology regarding healthcare. Real-time monitoring systems improve the quality of life of patients as well as the performance of hospitals and healthcare centers. In this paper, we present an implementation of a designed framework of a telemetry system using ZigBee technology for automatic and real-time monitoring of Biomedical signals. These signals are collected and processed using 2-tiered subsystems. The first subsystem is the mobile device which is carried on the body and runs a number of biosensors. The second subsystem performs further processing by a local base station using the raw data which is transmitted on-request by the mobile device. The processed data as well as its analysis are then continuously monitored and diagnosed through a human-machine interface. The system should possess low power consumption, low cost and advanced configuration possibilities. This paper accelerates the digital convergence age through conti...
International Journal of Networking and Virtual Organisations, 2011
... Page 14. 182 N. Hamza et al. Its ... 1, No. 1, March, pp.102–105. Jinwen, X., Yang, C.,Mason,... more ... Page 14. 182 N. Hamza et al. Its ... 1, No. 1, March, pp.102–105. Jinwen, X., Yang, C.,Mason, A. and Zhong, P. (2006) 'Adaptive multi-sensor interface system-on-chip', IEEE Sensors, EXCO, Daegu, Korea, October, pp.41–44. Lakshmi ...
Multichannel signal processing using digital signal processing techniques has received increased ... more Multichannel signal processing using digital signal processing techniques has received increased attention due to its importance in different information technology applications such as multimedia technology and telecommunications. Our objective in this paper is to provide a review for the reader who may be well versed in DSP, and to introduce some existing fuzzy (or fuzzy related) filtering techniques for multichannel (and color in particular) images, for the reader who is just beginning in this field of artificial intelligence. We present a general formulation based on fuzzy concepts, which allows the use of adaptive weights in the filtering structure, and we discuss different filter designs. Some examples illustrate the strong potential of fuzzy nonlinear filters for multichannel signal applications, such as color image processing.
A new class of nonlinear filters called Vector Median Rational Hybrid Filters (VMRHF) for multisp... more A new class of nonlinear filters called Vector Median Rational Hybrid Filters (VMRHF) for multispectral image processing is introduced and applied to color image filtering problems. These filters are based on Rational Functions (RF). The VMRHF filter is a two-stage filter, which exploits the features of the vector median filter and those of the rational operator. The filter output is a result of vector rational function operating on the output of three sub-functions. Two vector median (VMF) sub-filters and one center weighted vector median filter (CWVMF) are proposed to be used here due to their desirable properties, such as, edge and details preservation and accurate chromaticity estimation. Experimental results show that the new VMRHF outperforms a number of widely known nonlinear filters for multispectral image processing such as the Vector Median ilter (VMF) and Distance Directional Filters (DDf) with respect to all criteria used.
2000 10th European Signal Processing Conference, Sep 1, 2000
Résumé/Abstract A new multichannel filtering approach is introduced and analyzed in this paper. T... more Résumé/Abstract A new multichannel filtering approach is introduced and analyzed in this paper. These filters are based on rational functions (RF) using fuzzy transformations of the Euclidean distances among the different vectors to adapt to local data in the image. The output is the result of vector rational operation taking into account three sub-functions, such as two Fuzzy Vector Median (FVM) subfilters and one Fuzzy Center Weighted Vector Median Filter (FCWVMF). Simulation studies indicate that the filters are computationally attractive ...
ABSTRACT This paper presents a new 2-steps system for automatically sort Date fruits in four cate... more ABSTRACT This paper presents a new 2-steps system for automatically sort Date fruits in four categories which are big Sukkari Dates, defected big Sukkari Dates, small Sukkari Dates and Khalas Dates. In the first step, we used the Principal Component Analysis tool for features extraction and data dimensionality reduction. Then, obtained features were injected in a modified Back-Propagation-based Neural Network to be classified. Four tests were made upon a locally made Data base of Dates images, and obtained results varied between 96% and 100% of accuracy.
2021 29th Conference of Open Innovations Association (FRUCT), 2021
The World Health Organization has declared that the new Coronavirus disease (Covid-19) has become... more The World Health Organization has declared that the new Coronavirus disease (Covid-19) has become a pandemic since March 2020. It consists of an emerging viral infection with respiratory swelling that can progress to atypical pneumonia. In fact, experts stress the early detection importance of those infected with COVID-19 virus. In this way, the infected patients will be isolated from others, and then prevent the virus spread. However, prompt assessment of breathing patterns is important for many medical emergencies. We present, in this paper, a deep learning technique-based COVID-19 cough and breath analysis that can recognize positive COVID-19 cases from both negative and healthy COVID-19 cough and breath recorded on smartphones or wearable sensors. Firstly, audio signals, as well as cough and breath, will be preprocessed to remove noise. After that, deep features will be extracted using the deep Long Term Short Memory (LSTM) model. Finally, the recognition step will be performed exploiting extracted audio features. Numerical results prove the efficiency of the proposed deep model in terms of high accuracy level and low loss value compared to the other techniques.
International Journal of Advanced Computer Science and Applications
The demand on high quality palm dates is increasing due to its energy value and nutrient content,... more The demand on high quality palm dates is increasing due to its energy value and nutrient content, which are of great importance in human diet. To meet consumer and market standards with large-scale production, in Oman as among the top date producer, an inline classification system is of great importance. This paper addresses the potentiality of using Machine-Learning (ML) techniques in classifying automatically, without any physical measurement, the six most popular date fruit varieties in Oman. The effect of color, shape, size, and texture features and the critical parameters of the classifiers on the classification efficiency has been endeavored. Three different ML techniques have been used for automatic classification and qualitative comparison: (i) Artificial Neural Networks (ANN), (ii) Support Vector Machine (SVM), and (iii) K-Nearest Neighbor (KNN). Based on the merge of color, shape and size features contributes to achieve the highest accuracy. Experimental results show that the ANN classifier outperforms both SVM and KNN with the highest classification accuracy of 99.2%. This developed vision system in this paper can be successfully integrated in the packaging date factories.
Abstract. For real‐time applications, there are several factors (time, cost, power) that are movi... more Abstract. For real‐time applications, there are several factors (time, cost, power) that are moving security considerations from a function centric perspective into a system architecture (hardware/software) design issue. Advanced Encryption Standard (AES) is used nowadays extensively in many network and multimedia applications to address security issues. The AES algorithm specifies three key sizes: 128, 192 and 256 bits offer‐ ing different levels of security. To deal with the amount of application and intensive computation given by security mechanisms, we define and develop a QoSS (Quality of Security Service) model for reconfigurable AES processor. QoSS has been designed and implemented to achieve a flexible trade‐off between overheads caused by security ser‐ vices and system performance. The proposed architecture can provide up to 12 AES block cipher schemes within a reasonable hardware cost. We envisage a security vector in a fully functional QoSS request to include levels of se...
Atrial Fibrillation (AF) is a health-threatening condition, which is a violation of the heart rhy... more Atrial Fibrillation (AF) is a health-threatening condition, which is a violation of the heart rhythm that can lead to heart-related complications. Remarkable interest has been given to ECG signals analysis for AF detection in an early stage. In this context, we propose an artificial neural network ANN application to classify ECG signals into three classes, the first presents Normal Sinus Rhythm NSR, the second depicts abnormal signal with Atrial Fibrillation (AF) and the third shows noisy ECG signals. Accordingly, we achieve 93.1% accuracy classification results, 95.1% of sensitivity, 90.5% of specificity and 98%. Furthermore, we yield a value of zero error and a low value of cross entropy, which prove the robustness of the proposed ANN model architecture. Thus, we outperform the state of the art by achieving high accuracy classification without pre-processing step and without high level of feature extraction, and then we enable clinicians to determine automatically the class of each patient ECG signal.
... Abstract: The goal of this study is to determine a human visible difference between two image... more ... Abstract: The goal of this study is to determine a human visible difference between two images that may be used as an excellent image quality evaluator. ... How to cite this article: Amine Samet , MAB Ayed , Nouri Masmoudi and Lazhar Khriji , 2005. ...
International Journal of Advanced Robotic Systems, 2011
This paper shows how Q-learning approach can be used in a successful way to deal with the problem... more This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.
Nowadays, there is a significant improvement in technology regarding healthcare. Real-time monit... more Nowadays, there is a significant improvement in technology regarding healthcare. Real-time monitoring systems improve the quality of life of patients as well as the performance of hospitals and healthcare centers. In this paper, we present an implementation of a designed framework of a telemetry system using ZigBee technology for automatic and real-time monitoring of Biomedical signals. These signals are collected and processed using 2-tiered subsystems. The first subsystem is the mobile device which is carried on the body and runs a number of biosensors. The second subsystem performs further processing by a local base station using the raw data which is transmitted on-request by the mobile device. The processed data as well as its analysis are then continuously monitored and diagnosed through a human-machine interface. The system should possess low power consumption, low cost and advanced configuration possibilities. This paper accelerates the digital convergence age through conti...
International Journal of Networking and Virtual Organisations, 2011
... Page 14. 182 N. Hamza et al. Its ... 1, No. 1, March, pp.102–105. Jinwen, X., Yang, C.,Mason,... more ... Page 14. 182 N. Hamza et al. Its ... 1, No. 1, March, pp.102–105. Jinwen, X., Yang, C.,Mason, A. and Zhong, P. (2006) 'Adaptive multi-sensor interface system-on-chip', IEEE Sensors, EXCO, Daegu, Korea, October, pp.41–44. Lakshmi ...
Multichannel signal processing using digital signal processing techniques has received increased ... more Multichannel signal processing using digital signal processing techniques has received increased attention due to its importance in different information technology applications such as multimedia technology and telecommunications. Our objective in this paper is to provide a review for the reader who may be well versed in DSP, and to introduce some existing fuzzy (or fuzzy related) filtering techniques for multichannel (and color in particular) images, for the reader who is just beginning in this field of artificial intelligence. We present a general formulation based on fuzzy concepts, which allows the use of adaptive weights in the filtering structure, and we discuss different filter designs. Some examples illustrate the strong potential of fuzzy nonlinear filters for multichannel signal applications, such as color image processing.
A new class of nonlinear filters called Vector Median Rational Hybrid Filters (VMRHF) for multisp... more A new class of nonlinear filters called Vector Median Rational Hybrid Filters (VMRHF) for multispectral image processing is introduced and applied to color image filtering problems. These filters are based on Rational Functions (RF). The VMRHF filter is a two-stage filter, which exploits the features of the vector median filter and those of the rational operator. The filter output is a result of vector rational function operating on the output of three sub-functions. Two vector median (VMF) sub-filters and one center weighted vector median filter (CWVMF) are proposed to be used here due to their desirable properties, such as, edge and details preservation and accurate chromaticity estimation. Experimental results show that the new VMRHF outperforms a number of widely known nonlinear filters for multispectral image processing such as the Vector Median ilter (VMF) and Distance Directional Filters (DDf) with respect to all criteria used.
2000 10th European Signal Processing Conference, Sep 1, 2000
Résumé/Abstract A new multichannel filtering approach is introduced and analyzed in this paper. T... more Résumé/Abstract A new multichannel filtering approach is introduced and analyzed in this paper. These filters are based on rational functions (RF) using fuzzy transformations of the Euclidean distances among the different vectors to adapt to local data in the image. The output is the result of vector rational operation taking into account three sub-functions, such as two Fuzzy Vector Median (FVM) subfilters and one Fuzzy Center Weighted Vector Median Filter (FCWVMF). Simulation studies indicate that the filters are computationally attractive ...
ABSTRACT This paper presents a new 2-steps system for automatically sort Date fruits in four cate... more ABSTRACT This paper presents a new 2-steps system for automatically sort Date fruits in four categories which are big Sukkari Dates, defected big Sukkari Dates, small Sukkari Dates and Khalas Dates. In the first step, we used the Principal Component Analysis tool for features extraction and data dimensionality reduction. Then, obtained features were injected in a modified Back-Propagation-based Neural Network to be classified. Four tests were made upon a locally made Data base of Dates images, and obtained results varied between 96% and 100% of accuracy.
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Papers by Lazhar Kheriji