2022 International Conference on Intelligent Systems and Computer Vision (ISCV)最新文献_第3页

Placement methods of Virtual Machines in servers 虚拟机在服务器中的放置方法

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806069

Meryeme El Yadari, Ali Yahyaouy, K. El fazazy, S. Le Masson, H. Gualous

{"title":"Placement methods of Virtual Machines in servers","authors":"Meryeme El Yadari, Ali Yahyaouy, K. El fazazy, S. Le Masson, H. Gualous","doi":"10.1109/ISCV54655.2022.9806069","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806069","url":null,"abstract":"In a data center, the amount of data to be stored and processed is increasing more and more, given technological innovation and the diversity of services offered by companies in the cloud. Therefore, there is a need to know how to use server resources in a way that does not waste energy in data centers while optimizing SLAs (Service Level Agreement), and migration of virtual machines (VMs). This paper presents an algorithm named FSS-VM (Fuzzy Soft Set based-Virtual Machine) which consists of placing VMs in physical machines (PMs) taking into consideration the use of CPU, memory, RAM and correlation values. Another algorithm named DRL-VM is presented to make the placement of VMs in servers in an optimal way using heuristics, and taking into consideration software failures, the energy consumed by servers in the data center, and the collocation interference between VMs. As a result, FSS-VM has shown its performance on energy consumption and improvement of the QOS (Quality Of Service) comparing to other methods. The other method which is DRL-VM consists in making the placement of the VMs by choosing the optimal method among dot product, first fit, norm2, $rho$-greedy, $eta-$greedy and $omega$-greedy. Virtual machine placement method is a process that aims to improve the treatment complexity of user’s requests, and optimize energy consumption, both methods need to be enhanced to get better results.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126033724","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

Road Traffic: Deep Q-learning Agent Control Traffic lights in the intersection. 道路交通:深度q -学习智能体控制十字路口交通灯。

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806135

Chaymae Chouiekh, Ali Yahyaouy, A. Aarab, Abdelouahed Sabri

引用次数: 2

Bone age assessment using deep learning architecture: A Survey 使用深度学习架构评估骨龄:一项调查

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806110

Alaa Jamal Jabbar, Ashwan A. Abdulmunem

{"title":"Bone age assessment using deep learning architecture: A Survey","authors":"Alaa Jamal Jabbar, Ashwan A. Abdulmunem","doi":"10.1109/ISCV54655.2022.9806110","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806110","url":null,"abstract":"Skeletal bone age evaluation with X-ray pictures is a routine clinical approach for detecting any abnormalities in bone development in children and neonates. An assessed bone age represents the development of the actual degree, and a substantial difference between the assessed and chronological ages may suggest a growth issue. As a result, skeletal bone age evaluation is used to check for growth anomalies, genetic issues, and endocrine illnesses. The most frequent method for calculating bone age is radiography of the hand and wrist. Additionally, automated approaches for evaluating x-ray of the left hand and wrist development, which reduces the variability between raters in comparison to human ways. No-radiation-based approaches for viewing hand and wrist bones, as in ultrasonography, had been suggested, but are not like accurate radiographic methods. Methods based on magnetic resonance imaging (MRI) are being developed, but further study is needed. Another study is dental age a method of determining bone age that also provides estimates of bone maturity. Precise age estimation was essential to use the appropriate treatment method in pediatric patients also for forensic purposes. The left-hand wrist radiographs are thought it’s the most accurate, more uniform skeleton evaluation approach in this survey as a classification problem, this work shall look at numerous hand and wrist exercises evaluation of skeletal maturity.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117282248","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

New approach of smoothing to extend language model in Lucene Lucene中平滑扩展语言模型的新方法

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806072

Sylia Amarouche, Sabrina Mostefai, Fatiha Amirouche, Said Talbi

引用次数: 0

Offline and Online Evaluation for Recommender Systems 推荐系统的离线和在线评估

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806059

Kawtar Najmani, Lahbib Ajallouda, E. Benlahmar, N. Sael, A. Zellou

{"title":"Offline and Online Evaluation for Recommender Systems","authors":"Kawtar Najmani, Lahbib Ajallouda, E. Benlahmar, N. Sael, A. Zellou","doi":"10.1109/ISCV54655.2022.9806059","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806059","url":null,"abstract":"Recommender systems aim to facilitate decision making for users by offering them information according to their preferences, they are now popular in several application domains. The evaluation of recommender systems is very important to have an effective application in practice. In addition, it focuses to find better algorithms and evaluate their performance. However, researchers did not give much attention to it in this field. There are various ways to evaluate a recommender system. In this paper, we will discuss the main types of evaluations in this domain, which are offline and online evaluation, we will start with an overview of recommender systems, then we will present each type of evaluation, and we will compare the offline and the online evaluation for recommender systems. We will base on ten factors which are, the reproducibility, the reliability of the results of each type of evaluation, the preparation cost, the evaluation, the stability, the possibility of extensibility that’s mean if we can add new metrics or not, the scalability, the passed time, deep analysis, and the sparsity metric. Finally, we will discuss the factors which are presented in the comparison.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125585205","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

An ARIMA Model for Modeling and Forecasting the Dynamic of Univariate Time Series: The case of Moroccan Inflation Rate 单变量时间序列动态建模与预测的ARIMA模型——以摩洛哥通货膨胀率为例

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806073

Jouilil Youness, Mentagui Driss

引用次数: 6

Using Support Vector Regression to Predict the Overall Equipment Effectiveness Indicator* 使用支持向量回归预测整体设备效能指标*

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806111

Mjimer Imane, Es-Saâdia Aoula, E. H. Achouyab

引用次数: 1

A survey of deep learning approaches for classifying ECG heartbeat arrhythmias 心电心律不齐分类的深度学习方法综述

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806085

Mohamed Sraitih, Y. Jabrane

{"title":"A survey of deep learning approaches for classifying ECG heartbeat arrhythmias","authors":"Mohamed Sraitih, Y. Jabrane","doi":"10.1109/ISCV54655.2022.9806085","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806085","url":null,"abstract":"An automated computer aid remains relevant to support cardiology specialists in diagnosing heart disorders and rapidly classifying arrhythmias by using an electrocardiogram (ECG), which is among the most regularly utilized techniques to identify health disorders because hand identification of these heart-beat classes by doctors might take a long time. In this paper, we investigated and reviewed diverse research that worked on ECG arrhythmia identification by employing deep learning approaches. We illustrated and discussed the performance and approaches adopted to identify ECG heart arrhythmias by six commonly utilized methods, including MLP (Multilayer Perceptron), CNN (Convolutional Neural Network), DBN (Deep Belief Network), RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). We considered various limits and disclosed that there is yet space to extend the classification’s performance, precisely by reducing the preprocessing and principally the computational expense. Such a managed paper survey provides specialists with a nearly clear view of some elements of ECG classification methods and allows them to explore points unmet until now.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124474412","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

Moroccan sign language recognition based on machine learning 基于机器学习的摩洛哥手语识别

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806116

S. Abdelouahed, Cherrate Meryem, Yahyaouy Ali, Aarab Abdellah

引用次数: 0

SqueezeNet-Based Range, Angle, and Doppler Estimation for Automotive MIMO Radar Systems 基于squeezenet的汽车MIMO雷达系统的距离、角度和多普勒估计

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806088

Z. Benyahia, M. Hefnawi, M. Aboulfatah, E. Abdelmounim, T. Gadi

{"title":"SqueezeNet-Based Range, Angle, and Doppler Estimation for Automotive MIMO Radar Systems","authors":"Z. Benyahia, M. Hefnawi, M. Aboulfatah, E. Abdelmounim, T. Gadi","doi":"10.1109/ISCV54655.2022.9806088","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806088","url":null,"abstract":"The frequency modulated continuous waveform multiple - input multiple - output (FMCW MIMO) radar is of great interest to the automotive industry that provides high-end automobiles equipped with parking assistance, lane departure warning, and adaptive cruise control. These radars can simultaneously detect the range, angle, and doppler of the surrounding objects, such as cars, trucks, bicycles, and pedestrians and relay this information to the central control to provide a safe and collision-free cruise control for the self-driving vehicle. The traditional approach for the radar range, angle, and Doppler estimations is the Fast Fourier Transform (FFT)FFT which is computationally efficient but suffers from poor angular resolution. On the other hand, high-resolution techniques such as the Multiple SIgnal Classifier (MUSIC), the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), and the Minimum Variance Distortionless Response (MVDR) can achieve more accurate estimations but are computationally expensive. Moreover, these high-resolution techniques are very sensitive to clutter and interferences and cannot effectively distinguish targets from clutters in such an environment. In this paper, we propose a deep-learning- based FMCW MIMO radar in which the range, angle, and Doppler estimation are treated as a multilabel classification problem. The deep - learning approach is based on the SqueezeNet transfer learning approach to overcome the limitations on the amount of training data and training time. Simulation results demonstrate that the proposed approach outperforms the MVDR method in the presence of clusters and jammers and can achieve a high angular resolution of 2 degrees.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134536644","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