Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585987
Khelifa Bouaziz, Redouane Douaifia, S. Abdelmalek
This work mainly focuses on the dynamics of an epidemiologically emerging reaction-diffusion system. We establish global presence and the outcomes of asymptotic local and global stability to resolve the proposed system for a fairly broad class of nonlinearity that describes the transmission of an infectious disease between individuals by means of the appropriate Lyapunov function. the basic reproduction number can play a role in determining whether a disease will become extinct or persistent. Finally, we present an example that clarifies and confirms the results of the study throughout the paper.
{"title":"Analysis of Solutions for a Reaction-Diffusion Epidemic Model","authors":"Khelifa Bouaziz, Redouane Douaifia, S. Abdelmalek","doi":"10.1109/ICRAMI52622.2021.9585987","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585987","url":null,"abstract":"This work mainly focuses on the dynamics of an epidemiologically emerging reaction-diffusion system. We establish global presence and the outcomes of asymptotic local and global stability to resolve the proposed system for a fairly broad class of nonlinearity that describes the transmission of an infectious disease between individuals by means of the appropriate Lyapunov function. the basic reproduction number can play a role in determining whether a disease will become extinct or persistent. Finally, we present an example that clarifies and confirms the results of the study throughout the paper.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114187577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585958
M. Allaoui, Nour El-Houda Sayah Ben Aissa, Abdellah Ben Belghith, M. L. Kherfi
The advent of the COVID-19 pandemic caused by the Sars-CoV2 virus has caused serious damage in different areas. This has prompted thousands of researchers from different disciplines (biology, medicine, artificial intelligence, economics, etc.) to publish a very large number of scientific articles in a very short period, to answer questions related to this pandemic. This abundance of literature, however, raised another problem. It has indeed become extremely difficult for a researcher or a decision-maker to stay up to date with the latest scientific advances or to locate scientific articles related to a specific aspect of this pandemic. In this paper, we present an intelligent tool based on Machine learning, which automatically organizes a large dataset of Covid-19 related scientific literature and visualizes them in a way that helps these people navigating easily through this dataset and locating the sought documents easily. The documents are first pre-processed and transformed into numerical features. Then, those features are passed through a deep denoising autoencoder followed by Uniform Manifold Approximation and Projection technique (UMAP) to reduce their dimensionality into a 2D space. The projected data are then clustered with Agglomerative Clustering Algorithm. This is followed by a topic modeling step which we performed using Latent Dirichlet Allocation (LDA), in order to assign a label to each cluster. Finally, the documents are visualized to the user in an interactive interface that we developed. The experiments we conducted proved that our tool is efficient and useful.
{"title":"A Machine Learning-Based Tool for Exploring COVID-19 Scientific Literature","authors":"M. Allaoui, Nour El-Houda Sayah Ben Aissa, Abdellah Ben Belghith, M. L. Kherfi","doi":"10.1109/ICRAMI52622.2021.9585958","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585958","url":null,"abstract":"The advent of the COVID-19 pandemic caused by the Sars-CoV2 virus has caused serious damage in different areas. This has prompted thousands of researchers from different disciplines (biology, medicine, artificial intelligence, economics, etc.) to publish a very large number of scientific articles in a very short period, to answer questions related to this pandemic. This abundance of literature, however, raised another problem. It has indeed become extremely difficult for a researcher or a decision-maker to stay up to date with the latest scientific advances or to locate scientific articles related to a specific aspect of this pandemic. In this paper, we present an intelligent tool based on Machine learning, which automatically organizes a large dataset of Covid-19 related scientific literature and visualizes them in a way that helps these people navigating easily through this dataset and locating the sought documents easily. The documents are first pre-processed and transformed into numerical features. Then, those features are passed through a deep denoising autoencoder followed by Uniform Manifold Approximation and Projection technique (UMAP) to reduce their dimensionality into a 2D space. The projected data are then clustered with Agglomerative Clustering Algorithm. This is followed by a topic modeling step which we performed using Latent Dirichlet Allocation (LDA), in order to assign a label to each cluster. Finally, the documents are visualized to the user in an interactive interface that we developed. The experiments we conducted proved that our tool is efficient and useful.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126914686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585976
Sara Imane Zemoul, Y. Berkoun
We are interested in some asymptotic properties of the least squares estimator of the parameter of an autoregression process of order one (AR(1)) when the innovations are weakly dependent in certain sense. The results are based on some theorems relating to negatively associated (NA) and weakly dependent variables.
{"title":"Linear Process With Associated Innovations Under Weak Dependence","authors":"Sara Imane Zemoul, Y. Berkoun","doi":"10.1109/ICRAMI52622.2021.9585976","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585976","url":null,"abstract":"We are interested in some asymptotic properties of the least squares estimator of the parameter of an autoregression process of order one (AR(1)) when the innovations are weakly dependent in certain sense. The results are based on some theorems relating to negatively associated (NA) and weakly dependent variables.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126053310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Facing to many Cloud providers’ offers in the Cloud Computing market, the consumer is confused in choosing the appropriate Cloud. Therefore, we propose a Cloud Portal, which helps this consumer to choose the adequate Cloud provider according to his needs. This portal is based primarily on customer needs on one side, on the other side on concepts and techniques like the Multi-criteria AnalysisMethod, the Weighted K-Nearest Neighbor Method. Using this solution avoids the consumer to lose time, money and help him to select the right Cloud. Finally, the proposed process to build the portal is illustrated by using a case study and demonstrates how it works.
{"title":"A Cloud Portal for Consumer’s Needs in the Cloud Context","authors":"Ryma Messaouda Amara, Nacer Eddine Zarour, O. Boussaid, Oussama Arki, Chabane Djeddi","doi":"10.1109/ICRAMI52622.2021.9585960","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585960","url":null,"abstract":"Facing to many Cloud providers’ offers in the Cloud Computing market, the consumer is confused in choosing the appropriate Cloud. Therefore, we propose a Cloud Portal, which helps this consumer to choose the adequate Cloud provider according to his needs. This portal is based primarily on customer needs on one side, on the other side on concepts and techniques like the Multi-criteria AnalysisMethod, the Weighted K-Nearest Neighbor Method. Using this solution avoids the consumer to lose time, money and help him to select the right Cloud. Finally, the proposed process to build the portal is illustrated by using a case study and demonstrates how it works.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585924
Zakaria Tolba, M. Derdour, R. Menassel
Cryptanalysis is an audit step that leads designers to develop more robust cryptographic algorithms and assess algorithms’ overall performance. The fundamental problem is that the human evaluation of the cryptanalysis results is essential in this process. It optionally allows the remarkable convergence towards a promising result if it is based on better criteria, as it does not allow to find any solutions.To overcome the human intervention in this process we propose, in this work, a new cryptanalysis platform of image permutation-only cipher based on the detection of significant parts (ROIs) implementing the genetic algorithm and two models based on deep learning namely: Faster R-cnn for object detection and Mask R-cnn for segmentation.This is to automate the process of decryption keys evaluation and minimize the search space, which makes it possible to directly determine the permutation key or the most part of it. This work is applicable to color (RGB) images encrypted by pixel permutation techniques. It is independent of the permutation algorithm and it based on cipher text only attack by the advantages of those models exploitation to discover the correlation between adjacent pixels and to ameliorate this significant correlation by the genetic algorithm.
{"title":"Towards a Novel Cryptanalysis Platform based Regions Of Interest Detection via Deep Learning models","authors":"Zakaria Tolba, M. Derdour, R. Menassel","doi":"10.1109/ICRAMI52622.2021.9585924","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585924","url":null,"abstract":"Cryptanalysis is an audit step that leads designers to develop more robust cryptographic algorithms and assess algorithms’ overall performance. The fundamental problem is that the human evaluation of the cryptanalysis results is essential in this process. It optionally allows the remarkable convergence towards a promising result if it is based on better criteria, as it does not allow to find any solutions.To overcome the human intervention in this process we propose, in this work, a new cryptanalysis platform of image permutation-only cipher based on the detection of significant parts (ROIs) implementing the genetic algorithm and two models based on deep learning namely: Faster R-cnn for object detection and Mask R-cnn for segmentation.This is to automate the process of decryption keys evaluation and minimize the search space, which makes it possible to directly determine the permutation key or the most part of it. This work is applicable to color (RGB) images encrypted by pixel permutation techniques. It is independent of the permutation algorithm and it based on cipher text only attack by the advantages of those models exploitation to discover the correlation between adjacent pixels and to ameliorate this significant correlation by the genetic algorithm.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129638709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585955
Abdelaziz Limam, B. Benabderrahmane, Y. Boukhatem
A coupled system of viscoelastic wave equation of infinite memory is considered. Our system is coupled with the acoustic boundary conditions. Under a very general assumption on the relaxation function, we establish a uniform decay rate. This work substantially improves the earlier results in cases of acoustic boundary conditions.
{"title":"General Decay for a Coupled System of Viscoelastic Wave Equation of Infinite Memory with Acoustic Boundary Conditions","authors":"Abdelaziz Limam, B. Benabderrahmane, Y. Boukhatem","doi":"10.1109/ICRAMI52622.2021.9585955","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585955","url":null,"abstract":"A coupled system of viscoelastic wave equation of infinite memory is considered. Our system is coupled with the acoustic boundary conditions. Under a very general assumption on the relaxation function, we establish a uniform decay rate. This work substantially improves the earlier results in cases of acoustic boundary conditions.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132903003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585927
E. Zaouche
In this work, we consider the weak formulation of the evolution dam problem related to a compressible fluid flow governed by a nonlinear Darcy’s law. We prove the continuity in time of weak solutions for this problem which represents an extension of the regularity result obtained in the heterogeneous case [13].
{"title":"Continuity in Time of Weak Solutions for the Nonlinear Evolution Dam Problem Associated With a Compressible Fluid Flow","authors":"E. Zaouche","doi":"10.1109/ICRAMI52622.2021.9585927","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585927","url":null,"abstract":"In this work, we consider the weak formulation of the evolution dam problem related to a compressible fluid flow governed by a nonlinear Darcy’s law. We prove the continuity in time of weak solutions for this problem which represents an extension of the regularity result obtained in the heterogeneous case [13].","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126001737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585962
Nada Tassi, A. Bakkali, Nadia Fakri, L. Azrar
In this paper, the effective electro-elastic (EE) behavior of piezoelectric composite is predicted and analyzed based on a regularized micromechanical modeling. The mathematical modeling is based on Green’s function approach to derive the localization equation coupled with regularization and conditioned procedure. The ill-conditioned problem is present when going through the inversion of the localization tensor due to the large dispersion between elastic, dielectric, and piezoelectric coefficients. This problem is addressed using the Tikhonov regularization method. The choice of the regularization parameter is studied to be optimal and to assure the solution stability, and the convergence to the desired solution. The Homogenization of effective properties is obtained through the averaged procedure and a regularized Mori-Tanaka model. The effective electro-elastic properties are predicted with respect to the shape of constituents as well as to the volume fraction of inclusions.
{"title":"Regularized Micromechanical Modeling for the Prediction of Electro-Elastic Behavior of Reinforced Piezoelectric Composites","authors":"Nada Tassi, A. Bakkali, Nadia Fakri, L. Azrar","doi":"10.1109/ICRAMI52622.2021.9585962","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585962","url":null,"abstract":"In this paper, the effective electro-elastic (EE) behavior of piezoelectric composite is predicted and analyzed based on a regularized micromechanical modeling. The mathematical modeling is based on Green’s function approach to derive the localization equation coupled with regularization and conditioned procedure. The ill-conditioned problem is present when going through the inversion of the localization tensor due to the large dispersion between elastic, dielectric, and piezoelectric coefficients. This problem is addressed using the Tikhonov regularization method. The choice of the regularization parameter is studied to be optimal and to assure the solution stability, and the convergence to the desired solution. The Homogenization of effective properties is obtained through the averaged procedure and a regularized Mori-Tanaka model. The effective electro-elastic properties are predicted with respect to the shape of constituents as well as to the volume fraction of inclusions.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126015001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585992
Khaoula Imane Saffidine, Salim Mesbahi
The objective of this paper is to show the existence and uniqueness of positive solutions for a class of quasilinear degenerate parabolic reaction-diffusion problems defined in a bounded domain, which have many applications in various applied sciences. Its specificity lies in the introduction of degenerate diffusion. Our approach towards our goal is mainly based on the method of upper and lower solutions. The result obtained is applied to the Lotka-Volterra model.
{"title":"On the Existence and Uniqueness of Positive Solution for a Degenerate Reaction-Diffusion Problem","authors":"Khaoula Imane Saffidine, Salim Mesbahi","doi":"10.1109/ICRAMI52622.2021.9585992","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585992","url":null,"abstract":"The objective of this paper is to show the existence and uniqueness of positive solutions for a class of quasilinear degenerate parabolic reaction-diffusion problems defined in a bounded domain, which have many applications in various applied sciences. Its specificity lies in the introduction of degenerate diffusion. Our approach towards our goal is mainly based on the method of upper and lower solutions. The result obtained is applied to the Lotka-Volterra model.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129674465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585902
Kheira Djelloul, Abdelkader Nasreddine Belkacem
For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.
{"title":"EEG Classification-based Comparison Study of Motor-Imagery Brain-Computer Interface","authors":"Kheira Djelloul, Abdelkader Nasreddine Belkacem","doi":"10.1109/ICRAMI52622.2021.9585902","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585902","url":null,"abstract":"For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128658006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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