2022 International Conference on Intelligent Systems and Computer Vision (ISCV)最新文献

A Comparative Evaluation use Bagging and Boosting Ensemble Classifiers Bagging和Boosting集成分类器的比较评价

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806080

Hanae Aoulad Ali, Chrayah Mohamed, Bouzidi Abdelhamid, Nabil Ourdani, T. Alami

引用次数: 4

MTTR Prediction of railway rolling stock using regression algorithms 基于回归算法的铁路车辆MTTR预测

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806066

Z. Ragala, A. Retbi, S. Bennani

{"title":"MTTR Prediction of railway rolling stock using regression algorithms","authors":"Z. Ragala, A. Retbi, S. Bennani","doi":"10.1109/ISCV54655.2022.9806066","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806066","url":null,"abstract":"Railway equipment is very important in terms of maintenance, due to the complexity of its mechanical and electrical systems and the number of interchangeable parts. Thus, in order to ensure reliable and safe performance, railway companies must carry out regular maintenance and replace faulty equipment in a timely manner, without disrupting train operations. In addition, maintenance facilities are spread over the entire rail network, so an intelligent solution is needed to predict maintainability following breakdowns and allow technicians to take the necessary measures to ensure the proper functioning of all their equipment. Mean Time To Repair (MTTR) is one of the performance indicators used to determine the maintainability of an asset. Using historical data, in combination with failure data and maintenance action data, we explore several methods: linear and polynomial regression algorithms, Lasso regression, Ridge regression and random forests to build MTTR prediction models for the rail system. The results obtained are very promising and show that the linear and polynomial algorithms outperform the others on the prediction of maintenance performance in terms of MTTR.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"22 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":"116652059","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: 3

A new decision tree pre-pruning method based on nodes probabilities 一种基于节点概率的决策树预剪枝方法

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806124

Youness Manzali, Pr. Mohamed El Far

引用次数: 0

Localisation and Mapping of Self-driving Vehicles based on Fuzzy K-means Clustering: A Non-semantic Approach 基于模糊k均值聚类的自动驾驶车辆定位与映射:一种非语义方法

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806102

Anas Charroud, Ali Yahyaouy, K. E. Moutaouakil, Uche Onyekpe

{"title":"Localisation and Mapping of Self-driving Vehicles based on Fuzzy K-means Clustering: A Non-semantic Approach","authors":"Anas Charroud, Ali Yahyaouy, K. E. Moutaouakil, Uche Onyekpe","doi":"10.1109/ISCV54655.2022.9806102","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806102","url":null,"abstract":"Localisation and mapping are crucial for autonomous vehicles, as they inform the vehicle of where exactly they are in their environment as well as relevant infrastructures within the identified environment. This paper demonstrates the ability of non-semantic features to represent point clouds and use them to explain the environment. Our proposed architecture uses the Fuzzy K-means approach to extract features from LiDAR scenes in order to reduce the feature map and guarantee that the features are identifiable in each frame. Secondly, global mapping is done with the Gaussian Mixture Model (GMM) to facilitate data association between the frames to be mapped and helps localisation tasks to be performed accurately by the particle filter. The performance of the proposed technique is compared to other state of the art methods over different sequences of the Kitti raw dataset with different environmental structures, weather conditions and seasonal changes. The results obtained demonstrates the superiority of the proposed approach in terms of speed and representativeness of features needed for real-time localisation. Moreso, we achieved competitive accuracies compared to other state-of-the-art methods.","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":"129055259","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: 3

Simulation Experiments of Different Metaheuristics Algorithms using Benchmark Functions: A Performance Study 基于基准函数的不同元启发式算法仿真实验:性能研究

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806089

Imad El Hajjami, B. Benhala

引用次数: 4

Overview of personalization approaches in MOOCs mooc中个性化方法概述

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806079

Youssra Bellarhmouch, Adil Jeghal, N. Benjelloun, H. Tairi

引用次数: 0

Do Patients Tend to Find Positive or Negative Feedback on Social Networks? A Study of The Main Aspects of Modelling Patient Understanding Based on Emotional Variants 患者倾向于在社交网络上找到积极的还是消极的反馈?基于情绪变异的患者理解建模的主要方面研究

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806094

Hanane Grissette, E. Nfaoui

{"title":"Do Patients Tend to Find Positive or Negative Feedback on Social Networks? A Study of The Main Aspects of Modelling Patient Understanding Based on Emotional Variants","authors":"Hanane Grissette, E. Nfaoui","doi":"10.1109/ISCV54655.2022.9806094","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806094","url":null,"abstract":"Information on social networks can have an immediate influence on the public health status regarding a given medication or treatment. That means such positive or negative information about a given drug or service is learned, which frequently affects the emotional state of a patient. Here we draw upon the main question “Do Patients tend to Find Positive or Negative Feedback on Social Networks?”. Using sentiment analysis to comprehend the emotional state of patients is of critical importance, not only analysis of the text is required, a deep study of the main aspects of modeling patient understanding based on the emotional variants approach becomes apparent. Existing feature learning algorithms fail to define a relevant feature that promises the understanding of affect conveyed towards a given target. In this study, the goal is to define a based-emotional embedded-concepts spectrum that consists of defining affective information from selected affect seeds to other biomedical concepts. We develop concept-level emotional classification according to three categorical emotional variants and moral basics: PAIN or AFFECT, MOOD, and POSITIONALITY, where the objective is to predict the predominant concept-level affects. The experimental results on Twitter data show that the proposed strategy achieved significant performance improvements, thus, it might have an impact in real-world scenarios and helps provide situational awareness.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"35 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":"125456381","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

Framework for Estimating Distance and Detecting Object on Mono-camera 单摄像机距离估计与目标检测框架

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806075

Junxian Ma, Chuyue Yu, Yiwei Xia, X. Ren, V. Tsviatkou, A. A. Boriskevich

引用次数: 2

Efficient Localization And Reconstruction Of 3D Objects Using The New Hybrid Squire Moment 使用新的混合Squire力矩的3D对象的有效定位和重建

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806086

Ouchker Rabab, Mohamed Amine Tahiri, Ahmed Bencherqui, H. Amakdouf, Mohamed Ouazzani Jamil, H. Qjidaa

引用次数: 0

Recognition of Arabic digits using a convolutional neural network 使用卷积神经网络识别阿拉伯数字

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806129

Mouhssine El Atillah, Khalid El Fazazy, J. Riffi

引用次数: 0