2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)最新文献_第8页

Fraud Detection Based on Graph Neural Networks with Self-attention 基于自关注图神经网络的欺诈检测

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00075

Min Li, Mengjie Sun, Qianlong Liu, Yumeng Zhang

引用次数: 0

Prediction of Power Consumption of Hydroelectric Power Station by Levenberg-Marquardt-BP Algorithm 基于Levenberg-Marquardt-BP算法的水电站用电量预测

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00017

Xin Du

{"title":"Prediction of Power Consumption of Hydroelectric Power Station by Levenberg-Marquardt-BP Algorithm","authors":"Xin Du","doi":"10.1109/AINIT54228.2021.00017","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00017","url":null,"abstract":"Improving the predicting and monitoring of station power consumption of hydropower stations is of great significance to realize the fine management of energy efficiency of hydropower stations and reduce the level of station power consumption. The reliability of electrical equipment operation is very important for the safe and stable operation of hydropower stations.The power consumption of hydropower stations is closely related to the operating status of electrical equipment of hydropower stations. this paper establishes a BP neural network prediction model based on the Levenberg-Marquardt algorithm (Levenberg-Marquardt-BP) to accurately predict the power consumption of electrical equipment in a hydropower station. Field tests show that the RMSE of Levenberg-Marquardt-BP prediction method is 2.1%, which is much lower than the conventional BP prediction algorithm. The Levenberg-Marquardt-BP algorithm also can quicken the algorithm convergence speed and its convergence steps are 35% of the conventional BP prediction algorithm.The analysis of prediction examples proves the reliability and effectiveness of the Levenberg-Marquardt-BP prediction method.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125055651","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

A Cancer Cell Image Classification Program : Based on CNN Model 一种基于CNN模型的癌细胞图像分类程序

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00037

Yu Qin

{"title":"A Cancer Cell Image Classification Program : Based on CNN Model","authors":"Yu Qin","doi":"10.1109/AINIT54228.2021.00037","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00037","url":null,"abstract":"Breast Cancer, as a deadly existence, has negatively influenced women’s health. To determine whether a cell is benign or malignant is critical for doctors to diagnose breast cancer. However, simply judging whether the cells are malignant by the appearance of the X-ray’s outcome greatly reduces the efficiency of diagnosis and the probability of misdiagnosis. This paper proposed a simulation model to tackle this issue, which can be utilized to analyze and determine whether the cell is benign or malignant. With the help of CNN and Text-to-speech, I have researched a solution for doctors to identify Breast Cancer with the help of machine learning. The experiment consists of Image classification built upon CNN and a user interface for upload functionality by Streamlit framework, combined with an NLP speech synthesis interface to communicate the result done with gTTS. The result of the experiment has brought up a fast, efficient, accurate result after the model has been trained measured by sensitivity and specificitysignflcantly reduced the amount of error rate. Building an interface that interacts with the result allows the result to be more visualized and straightforward.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128836548","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

A Topic-level Sentiment Classification Model Based on Deep Learning 基于深度学习的主题级情感分类模型

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00079

Lizhong Xiao, Liang Li

引用次数: 0

Word File Parsing Based On Python 基于Python的Word文件解析

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00113

Jinlan Kong, Q. Zhou, Tianyi Ma, Yuqi He, HongJi Kong

引用次数: 0

A Recognition Method of Cattle and Sheep Based on Convolutional Neural Network 基于卷积神经网络的牛羊识别方法

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00088

Fangyu Sun, Handong Wang, Jiawei Zhang

{"title":"A Recognition Method of Cattle and Sheep Based on Convolutional Neural Network","authors":"Fangyu Sun, Handong Wang, Jiawei Zhang","doi":"10.1109/AINIT54228.2021.00088","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00088","url":null,"abstract":"Facing the problem of low recognition accuracy caused by the confusing background information and low quality of monitoring images of automatic recognition for cattle and sheep animals, this paper proposes a convolutional neural network-based animal identification method for cattle and sheep. Thus, the recognition accuracy can be improved in the case of multiple images. First, the original image data is enhanced by randomly cropping, randomly inverting angles, and randomly horizontal rollback, and then a binary classification model for cattle and sheep recognition based on the VGG-16 convolutional neural network is built. Then the relevant hyperparameters will be continuously adjusted to increase the number of iterations. A higher recognition accuracy rate will finally be achieved. To verify the effectiveness of the method, this article adopted 260 and 110 cattle and sheep pictures respectively from open resources for training and testing. The experimental results showed that the highest recognition accuracy of the test set reached 96.67%, making the average accuracy rate as high as 90.95%, approximately 5.4% higher than the accuracy rate of other traditional VGG network models. This method showed faster speed and more extensive generalization, providing a practical technological reference for cattle and sheep recognition and binary classification problems.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127400818","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

An interactive prediction system of breast cancer based on ResNet50, chatbot and PyQt 基于ResNet50、聊天机器人和PyQt的乳腺癌交互式预测系统

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00068

Xi Yang, Daiming Yang, Chenfeng Huang

{"title":"An interactive prediction system of breast cancer based on ResNet50, chatbot and PyQt","authors":"Xi Yang, Daiming Yang, Chenfeng Huang","doi":"10.1109/AINIT54228.2021.00068","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00068","url":null,"abstract":"Breast cancer has gradually become an important killer that endangers people’s health. How to diagnose breast cancer quickly and accurately has become a popular research direction. However, traditional testing by the doctor is time-consuming and laborious, and there is still the problem of accuracy. Deep learning becomes a tool of evidence-based medicine, which can effectively solve the above problems and realize the function of detecting breast cancer automatically and with high accuracy. In our study, we selected and applied the optimal CNN model named ResNet50 for breast cancer diagnosis. Due to the small size of images in our dataset, the 3*3 convolutional layer performed better than the 7*7 convolutional layer in our breast cancer classification task. Besides, our pre-trained ResNet50 achieved 94.698% accuracy on the WSI dataset, while un-pretrained ResNet50 only achieved 93.777%. The result presented pretraining in the ImageNet dataset can more effectively reduce the loss and improve accuracy. We also developed an application that integrated the CNN model with a chatbot implemented by NLTK and an interface constructed through PyQt.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128015540","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

Research on Football Team Ability Based on Random Forest and Ranking Method 基于随机森林和排名法的足球队能力研究

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00050

Xu Luo, Kaishuo Liu, Xinkai Yuan

{"title":"Research on Football Team Ability Based on Random Forest and Ranking Method","authors":"Xu Luo, Kaishuo Liu, Xinkai Yuan","doi":"10.1109/AINIT54228.2021.00050","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00050","url":null,"abstract":"The outcome of a football match covers many skills between individual ability and team strategy. This paper is an in-depth analysis of football team strategy. Firstly, the passing network between players is established, and the passing network is used to determine the formation of players. Next, it is determined that the performance indicators that reflect successful team cooperation, including the diversity of game types and the coordination of players, and then determine other team-level processes. Finally, create a model according to the determined performance indicators and team process, and use the model to analyze the structure, configuration, and dynamic characteristics of the team operation. In the experimental stage, through the observation and analysis of team cooperation mode, this paper puts forward the optimization structure strategy to achieve greater efficiency, and helps the coach improve the success rate of the team through network analysis. In this problem, according to all the matches of the four FIFA world cups from 2002 to 2014, we compared three different modeling methods for predicting the score of football matches: the Poisson regression model, random forest and the ranking method. The first two methods are based on the covariate information of the team, while the latter method estimates sufficient capability parameters, which can best reflect the strength of the current team. In this comparison, the prediction method with the best performance on the training data finally becomes the ranking method and random forest. By combining the random forest with the team ability parameters of the ranking method as additional covariates, the prediction ability can be greatly improved.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465598","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

A Yolov3-Based Multi-target Detection System for Complex Scenes 基于yolov3的复杂场景多目标检测系统

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00071

Xingchen Yan, Chen Shuai, Haiyan Zheng

引用次数: 0

Number of Epochs of Each Model and Hyperband’s Classification Performance 每个模型的epoch数和Hyperband的分类性能

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00102

Junjie Hu, Xiushi Feng, Yefeng Zheng

引用次数: 0