2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)最新文献_第4页

Analysis of Solutions for a Reaction-Diffusion Epidemic Model 一类反应-扩散流行病模型解的分析

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585987

Khelifa Bouaziz, Redouane Douaifia, S. Abdelmalek

引用次数: 0

A Machine Learning-Based Tool for Exploring COVID-19 Scientific Literature 基于机器学习的COVID-19科学文献探索工具

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585958

M. Allaoui, Nour El-Houda Sayah Ben Aissa, Abdellah Ben Belghith, M. L. Kherfi

{"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":"29 1","pages":"0"},"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}

引用次数: 1

Linear Process With Associated Innovations Under Weak Dependence 弱依赖下具有关联创新的线性过程

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585976

Sara Imane Zemoul, Y. Berkoun

引用次数: 0

A Cloud Portal for Consumer’s Needs in the Cloud Context 在云环境中满足消费者需求的云门户

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585960

Ryma Messaouda Amara, Nacer Eddine Zarour, O. Boussaid, Oussama Arki, Chabane Djeddi

引用次数: 0

Towards a Novel Cryptanalysis Platform based Regions Of Interest Detection via Deep Learning models 基于深度学习模型的兴趣区域检测的新型密码分析平台

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585924

Zakaria Tolba, M. Derdour, R. Menassel

{"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":"52 1","pages":"0"},"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}

引用次数: 4

General Decay for a Coupled System of Viscoelastic Wave Equation of Infinite Memory with Acoustic Boundary Conditions 具有声学边界条件的无限记忆粘弹性波动方程耦合系统的一般衰减

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585955

Abdelaziz Limam, B. Benabderrahmane, Y. Boukhatem

引用次数: 0

Continuity in Time of Weak Solutions for the Nonlinear Evolution Dam Problem Associated With a Compressible Fluid Flow 含可压缩流体的非线性演化坝问题弱解的时间连续性

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585927

E. Zaouche

引用次数: 0

Regularized Micromechanical Modeling for the Prediction of Electro-Elastic Behavior of Reinforced Piezoelectric Composites 基于正则化微力学模型的增强压电复合材料电弹性性能预测

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585962

Nada Tassi, A. Bakkali, Nadia Fakri, L. Azrar

引用次数: 1

On the Existence and Uniqueness of Positive Solution for a Degenerate Reaction-Diffusion Problem 一类退化反应扩散问题正解的存在唯一性

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585992

Khaoula Imane Saffidine, Salim Mesbahi

引用次数: 0

EEG Classification-based Comparison Study of Motor-Imagery Brain-Computer Interface 基于脑电分类的运动-图像脑机接口比较研究

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) Pub Date : 2021-09-21 DOI: 10.1109/ICRAMI52622.2021.9585902

Kheira Djelloul, Abdelkader Nasreddine Belkacem

{"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":"112 1","pages":"0"},"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}

引用次数: 0