International Conference on Machine Learning Technologies最新文献

A Comparison of PSO-Based Informative Path Planners for Autonomous Surface Vehicles for Water Resource Monitoring 基于pso的自动水面车辆水资源监测信息路径规划比较

International Conference on Machine Learning Technologies Pub Date : 1900-01-01 DOI: 10.1145/3529399.3529442

Micaela Jara Ten Kathen, Isabel Jurado Flores, Daniel Gutiérrez-Reina

{"title":"A Comparison of PSO-Based Informative Path Planners for Autonomous Surface Vehicles for Water Resource Monitoring","authors":"Micaela Jara Ten Kathen, Isabel Jurado Flores, Daniel Gutiérrez-Reina","doi":"10.1145/3529399.3529442","DOIUrl":"https://doi.org/10.1145/3529399.3529442","url":null,"abstract":"Preserving water resources is an objective that is constantly being pursued. Monitoring the aquatic environments is an action to fulfill this objective, since the state of the water quality will be controlled. The monitoring task can be carried out with Autonomous Surface Vehicles equipped with sensors that measure water quality parameters and with a monitoring system. This paper presents a comparison between informative path planners based on PSO for autonomous surface vehicles for water resources monitoring. The case presented is the case of Ypacarai Lake. The simulations carried out allow visualizing and comparing the response of different methods. The methods evaluated are the Local Best method, the Global Best method, the Uncertainty method, the Contamination method, the Classic PSO, Enhanced GP-based PSO, and the Epsilon Greedy method. For the optimization of the Enhanced GP-based PSO coefficients, Bayesian optimization is selected. The results show that the Enhanced GP-based PSO is the algorithm with the best solutions for monitoring the lake environment.","PeriodicalId":149111,"journal":{"name":"International Conference on Machine Learning Technologies","volume":"517 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116235838","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

Prediction of Cryptocurrency Price Dynamics with Multiple Machine Learning Techniques 使用多种机器学习技术预测加密货币价格动态

International Conference on Machine Learning Technologies Pub Date : 1900-01-01 DOI: 10.1145/3340997.3341008

Zhengyang Wang, Xingzhou Li, Jinjin Ruan, J. Kou

引用次数: 10

International Conference on Machine Learning Technologies Pub Date : 1900-01-01 DOI: 10.1145/3529399.3529428

Adelaida Zuluaga Cajiao, Andrés Rosso-Mateus

引用次数: 1

Evaluating the FixMatch Semi-Supervised Algorithm for Unbalanced Image Data 评价非平衡图像数据的FixMatch半监督算法

International Conference on Machine Learning Technologies Pub Date : 1900-01-01 DOI: 10.1145/3529399.3529419

A. Sajun, I. Zualkernan

引用次数: 0

Dynamic User-Centric Clustered Workplaces for COVID-19 Control Measures Based on Geofencing and Deep Learning 基于地理围栏和深度学习的以用户为中心的动态集群工作场所COVID-19控制措施

International Conference on Machine Learning Technologies Pub Date : 1900-01-01 DOI: 10.1145/3529399.3529435

Ahmed Mostafa Abdelkhalek, N. E. M. Mohamed, Mostafa M. Abdelhakam, M. M. Elmesalawy

{"title":"Dynamic User-Centric Clustered Workplaces for COVID-19 Control Measures Based on Geofencing and Deep Learning","authors":"Ahmed Mostafa Abdelkhalek, N. E. M. Mohamed, Mostafa M. Abdelhakam, M. M. Elmesalawy","doi":"10.1145/3529399.3529435","DOIUrl":"https://doi.org/10.1145/3529399.3529435","url":null,"abstract":"Control measures have been applied in recent years due to the COVID-19 pandemic. Different technologies including artificial intelligence (AI) and geofencing are required to be exploited for developing efficient techniques to deal with this crisis. Workplaces are the most dangerous areas that can lead to the infection of the pandemic. This is due to the increased density of people and transactions in limited places. In this paper, an efficient approach is proposed to monitor and impose COVID-19 control measures in workplaces. The workplace environment is clustered based on a dynamic user-centric clustering scheme, where each person in the workplace is assigned to a set of associated geofences that form its cluster. For each geofence, different wireless and network metrics are used for generating its digital signature. An efficient technique based on deep learning is proposed to generate the geofence digital signature and detect whether the person is inside his associated cluster or not. Experimental results show the effectiveness of the proposed technique for different locations in a real workplace. Specifically, an accuracy of 92.86% is achieved in a workplace environment by the proposed approach.","PeriodicalId":149111,"journal":{"name":"International Conference on Machine Learning Technologies","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121531432","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: 2