2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)最新文献

Perceptual Forecasting Model of Power Big Data Based on Improved Random Forest Algorithm 基于改进随机森林算法的电力大数据感知预测模型

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00060

Bo Jia, Minrong Wu, Bin Li, Ye Yu, N. Zhang, Guowu Ma

{"title":"Perceptual Forecasting Model of Power Big Data Based on Improved Random Forest Algorithm","authors":"Bo Jia, Minrong Wu, Bin Li, Ye Yu, N. Zhang, Guowu Ma","doi":"10.1109/MLISE57402.2022.00060","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00060","url":null,"abstract":"Under the background of the rapid development of the smart grid and ubiquitous power Internet of things, the number of network terminals and users has increased greatly, resulting in a gradual increase in the number and types of services that need to be carried in the distribution and consumption communication network. Although network virtualization technology can shield the differences brought by physical layer network heterogeneity, during the allocation process, the physical layer network is affected by factors such as the deployment environment, usage time, the network load, etc., making its running state time-varying. Therefore, the mapping results and transmission quality of various services are affected, and the reliability of service transmission is reduced. Based on the fact that the network operation state of the infrastructure layer directly affects the transmission quality of virtual network services, this paper introduces a reliability evaluation model based on random forest and conducts experiments and analysis on the main link mapping algorithm designed based on the evaluation model through simulation experiments. The results show that the algorithm has good resource allocation ability and a low impact on the number of services. It can further improve the acceptance rate of virtual network service mapping and improve the transmission quality of services, which is of great significance to the development of the smart grid.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127192716","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

APPFed: A Hybrid Privacy-Preserving Framework for Federated Learning over Sensitive Data 一种用于敏感数据联邦学习的混合隐私保护框架

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00084

Ruichu Yao, Kunsheng Tang, Bingbing Fan

引用次数: 0

Research on big data analysis and processing system based on Spark platform 基于Spark平台的大数据分析处理系统研究

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/mlise57402.2022.00059

Yansong Li

引用次数: 0

The Comparison of Six Prediction Models in Machine Learning: Based on the House prices Prediction 机器学习中六种预测模型的比较——以房价预测为例

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00095

Yizhi Wang

引用次数: 1

Technical research on machine learning framework based on optimization algorithm 基于优化算法的机器学习框架技术研究

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00074

Yansong Li

引用次数: 0

Analysis of the Relevance between Title of Product and Search Term 产品标题与搜索词的相关性分析

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00041

Jiayi Clien, Yutao Wei, Yukang Zou

引用次数: 0

Achieving Object-Following Function Using Braitenberg Vehicles 使用britenberg车辆实现目标跟踪功能

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00050

Hanxiao Xie

{"title":"Achieving Object-Following Function Using Braitenberg Vehicles","authors":"Hanxiao Xie","doi":"10.1109/MLISE57402.2022.00050","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00050","url":null,"abstract":"Braitenberg Vehicles (BV) are robots with a relatively simple and concise structure that are controlled directly by sensors including distance sensors and velocity sensors. With such characteristics, the hypothesis that BV can be used as an alternative object-following robot than PID controlling robots is proposed. However, experiments directly related to this topic were inadequate. To further investigate if BV can achieve such an object-following function, this study implemented MATLAB Simulink Program to simulate the experimental environment, the object-following function of both BV and PID controlling robots, and compared their efficiency, stability, and accuracy. With data illustrated in diagrams, conclusions were made that BV achieves the object-following function similar to PID controlling robots, even though BV is relatively unstable in its velocity. Since BV have much lower production costs, they still hold numerous advantages in object-following applications. This research was based on the general principle of robotics, and also proposed plausible BV applications and ways to improve its object-following functions.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712281","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

Image Denoising Method and System Based on Enhanced Deep Dilated Convolutional Neural Network 基于增强深度扩展卷积神经网络的图像去噪方法及系统

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00094

Meng Li, Kaili Feng, Tianping Li, Guanxing Li

引用次数: 0

Weather Classification for Multi-class Weather Image Based on CNN 基于CNN的多类天气图像天气分类

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/mlise57402.2022.00079

Yuchen Hao, Yuxuan Zhu

引用次数: 1

Sentiment Analysis Method for Agricultural Product Review Based on Corpus Characteristics and Deep Learning Model 基于语料库特征和深度学习模型的农产品评论情感分析方法

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00091

Zihao Zhou, Jie Chen, J. Wu, Ruoyu Wang

{"title":"Sentiment Analysis Method for Agricultural Product Review Based on Corpus Characteristics and Deep Learning Model","authors":"Zihao Zhou, Jie Chen, J. Wu, Ruoyu Wang","doi":"10.1109/MLISE57402.2022.00091","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00091","url":null,"abstract":"Although deep learning models are widely used in text sentiment analysis, it is a challenging task to extract richer semantic features to improve model performance in corpora with weak label characteristics. This study crawls the agricultural product review of Jingdong e-commerce as a corpus, and proposes a deep learning method based on the characteristics of the corpus for sentiment analysis. The method first uses frequent item mining to construct a sentiment dictionary, and converts weakly labeled data into high-quality corpus through sentiment value calculation. Secondly, Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) are combined in the sentiment analysis model, and the word vectors trained by Glove and Word2vec are imported into the multi-channel neural network, so that the model can learn local and global semantic features in parallel, and embed the attention mechanism in the channel. The experimental results show that the performance of the model considering the characteristics of the corpus is significantly improved, and the MAtt-CNN-BiLSTM model constructed in this paper has the best performance in the experiments under the three datasets.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130568420","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