2020 International Conference on Intelligent Systems and Computer Vision (ISCV)最新文献

Early detection of COVID19 by deep learning transfer Model for populations in isolated rural areas 基于深度学习迁移模型的偏远农村人群covid - 19早期检测

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204099

M. Qjidaa, Y. Mechbal, A. Ben-fares, H. Amakdouf, M. Maaroufi, B. Alami, H. Qjidaa

引用次数: 14

Network selection based on Cosine Similarity and Combination of Subjective and Objective Weighting 基于余弦相似度和主客观加权相结合的网络选择

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204217

Said Radouche, C. Leghris

{"title":"Network selection based on Cosine Similarity and Combination of Subjective and Objective Weighting","authors":"Said Radouche, C. Leghris","doi":"10.1109/ISCV49265.2020.9204217","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204217","url":null,"abstract":"In the next generation heterogeneous wireless and mobile networks, the mobile terminal equipped with a multi-interface may be able to choose an optimal access network anywhere and at any time. The seamless handover between different technologies (Cellular, WiMAX, Wi-Fi, and Satellite) is referred to as vertical handover (VHO). However, the main challenge is to provide seamless connectivity to the mobile terminal in this heterogeneous environment. Therefore, VHO needs an effective network selection process based on multiple network parameters. This article presents a new network selection algorithm based on cosine similarity to rank alternative networks and combination method that integrates both subjective weights, calculated using the user’s experience, and objective weights, determined by the Entropy method. The performance indicators used in this study are ranking abnormality and the number of handoffs. Obtained results indicate that the developed method performed better than the conventional MADM methods widely used in the context of vertical handover namely TOPSIS, VIKOR, and GRA.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116559089","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: 7

Capsule Network Based on Scalograms of Electrocardiogram for Myocardial Infarction Classification 基于心电图尺度图的胶囊网络用于心肌梗死分类

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204138

Imane El Boujnouni, Abdelhak Tali, K. Bentaleb

引用次数: 1

Fast Depth Map Intra Mode Prediction Based on Self-organizing Map 基于自组织图的深度图模式内快速预测

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204033

Amal Hammani, Hamza Hamout, A. Elyousfi

引用次数: 0

Frequent Itemsets Methods for Text Clustering 文本聚类的频繁项集方法

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204168

Chama El Saili, Soukaina Fatimi, L. Alaoui

引用次数: 0

A new weighted fuzzy c-means based on the collective behaviour of starling birds 基于椋鸟群体行为的加权模糊c均值

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204249

Saloua El Motaki, Ali Yahyaouy, H. Gualous, J. Sabor

引用次数: 0

Enhancing GPSR routing protocol based on Velocity and Density for real-time urban scenario 实时城市场景下基于速度和密度的GPSR路由协议改进

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204293

Amina Bengag, A. Bengag, Mohamed el Boukhari

{"title":"Enhancing GPSR routing protocol based on Velocity and Density for real-time urban scenario","authors":"Amina Bengag, A. Bengag, Mohamed el Boukhari","doi":"10.1109/ISCV49265.2020.9204293","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204293","url":null,"abstract":"The main goal of VANET is to improve the traffic safety, by minimizing the number of road accidents. Hence, a reliable and rapid communication is crucial for vehicles. However, the high dynamic topology of VANETs, the frequently link breakage problem and the high speed of vehicles have brought great challenges to the routing design of VANETs. It is difficult for existing routing protocols for WSN and MANETs to adapt the high dynamics of vehicular networks. Therefore, various number of routing protocols have been proposed to deal with the specific characteristics of VANETs. GPSR for Greedy Perimeter Stateless Routing is the most popular protocol, but it still suffers from frequent link breakages issue due to the high-mobility of vehicles, which cause a low PDR and throughput. In this investigation, we introduce two new strategies to enhance the classical GPSR protocol, by reducing the problem of link breakages and getting a stable route that improve the PDR and throughput in addition to reduce the routing overhead. The two proposed routing protocols E-GPSR and DVA-GPSR guide the selection of the next hop node based not only on the position but also on other important metrics of the participating nodes. Simulation results show that DVA-GPSR and E-GPSR protocols perform remarkably GPSR in terms of PDR, throughput, and routing overhead by varying the number of vehicles in a real urban scenario.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124478166","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: 6

EDF-based real-time scheduling for self-powered sensors: a survey of main theoretical results 基于edf的自供电传感器实时调度:主要理论成果综述

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204172

M. Chetto, Audrey Queudet

引用次数: 0

Color Stereo Image Zero-Watermarking using Quaternion Radial Tchebichef Moments 基于四元数径向切切夫矩的彩色立体图像零水印

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204169

M. Yamni, H. Karmouni, M. Sayyouri, H. Qjidaa

引用次数: 9

A hybrid Deep Learning Strategy for an Anomaly Based N-IDS 基于异常的N-IDS混合深度学习策略

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204227

Hanene Mennour, S. Mostefai

{"title":"A hybrid Deep Learning Strategy for an Anomaly Based N-IDS","authors":"Hanene Mennour, S. Mostefai","doi":"10.1109/ISCV49265.2020.9204227","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204227","url":null,"abstract":"This paper presents a hybrid deep learning neural network for classifying the network traffic data. In this regard, a Stacked Autoencoder and Feedforward neural network with tangent activation function have been employed. Firstly, we pre-trained the stacked Autoencoder with unsupervised learning method to improve the generalization of the classifier and limit the over-fitting problem in building the Feedforward neural network. in this state, the data is reconstructed into a new representation. After that, the Feedforward neural network as a supervised classifier has been stacked on the top. The purpose was to map the data in this new representation into class predictions. Finally, we have fine-tuned the entire network to accomplish the optimal hybrid model. A k fold cross-validation has been conducted to validate the system. CICIDS2017 datasets has been used in the experiment to classify normal and abnormal behaviour. The experimental results obtained by analyzing the proposed system show their superiority in terms of accuracy, detection rate and false alarm rate as compared to two state-of the-art machine learning algorithms which are Support Vector Machine (SVM) and Deep Neural Network. Our study achieves 98%, 100% for accuracy rates and F1 score respectively.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130914586","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