Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence最新文献_第2页

Layout Analysis of Tibetan Historical Documents Based on Deep Learning 基于深度学习的藏文历史文献布局分析

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3357790

Yong Cuo, N. Tashi, Zhengzhen Liu, Qiuhua Wei, Luosang Gadeng, Gama Trashi

{"title":"Layout Analysis of Tibetan Historical Documents Based on Deep Learning","authors":"Yong Cuo, N. Tashi, Zhengzhen Liu, Qiuhua Wei, Luosang Gadeng, Gama Trashi","doi":"10.1145/3357777.3357790","DOIUrl":"https://doi.org/10.1145/3357777.3357790","url":null,"abstract":"Tibetan historical document are vast, second in quantity only to Chinese historical document in China, and they are considered a treasure of Chinese culture. The digital protection and utilization of Tibetan literature resources is a hot topic in the field of literature digitization. Layout analysis is an important basic step in the digitization of historical document. Tibetan historical document have a complex layout, a variety of graphic and text forms, and diverse backgrounds, all of which have an impact on the layout analysis. We design a method combining deep learning text line detection with rule-based layout analysis to realize layout analysis of Tibetan historical document. This method first conducts text detection through deep learning, then constructs text lines, and finally segments horizontal text regions and vertical text regions by rule analysis to realize the segmentation of the layout. Our self-built datasets with rich sample types show that the proposed method can achieve detection of a variety of layouts with high accuracy and provide reliable text regions for subsequent text recognition, thus offering strong application value.","PeriodicalId":127005,"journal":{"name":"Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132334858","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

Generating Adversarial Samples with Convolutional Neural Network 用卷积神经网络生成对抗样本

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3357791

Zhongxi Qiu, Xiaofeng He, Lingna Chen, Hualing Liu, LianPeng Zuo

引用次数: 1

A Secure Privacy Preserving Proxy re-encryption Scheme for IoT Security using Near-ring 基于近环的物联网安全隐私保护代理重加密方案

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3359011

S. Krishnamoorthy, V. Muthukumaran, J. Yu, B. Balamurugan

引用次数: 7

Mining the Relationship between Crimes, Weather and Tweets 挖掘犯罪、天气和推文之间的关系

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3357787

Joseph Alamo, C. Fortes, Nicole Occhiogrosso, Ching-Yu Huang

{"title":"Mining the Relationship between Crimes, Weather and Tweets","authors":"Joseph Alamo, C. Fortes, Nicole Occhiogrosso, Ching-Yu Huang","doi":"10.1145/3357777.3357787","DOIUrl":"https://doi.org/10.1145/3357777.3357787","url":null,"abstract":"This research project attempts to correlate crime rates in Orlando, Florida to Orlando's weather and Twitter presence. The central dataset of interest details the crime incidents in Orlando, Florida as reported daily by the Orlando Police Department. This dataset gives the dates, categories (e.g. theft, aggravated assault, etc.), and latitude and longitude of each reported crime incident. Using a Twitter developer account, Tweets pertaining to crime are downloaded from the greater Orlando area. Tweets are filtered by the following indexed keywords: \"crime\", \"drugs\", \"narcotics\", \"weapons\", \"assault\", \"theft\", \"robbery\", \"murder\", and \"larceny.\" Additionally, Orlando's daily weather data is collected from the National Oceanic and Atmospheric Administration. Using measures of similarity, it is discovered that crime in Orlando is concentrated most closely near Orlando's downtown center. Using regression, moderate correlations are drawn between the rates of crime and the posting of crime-related Tweets. Lastly, chi-square tests are used to show the effect of weather on crime. High crime rates are associated with average daily temperatures above 60°F. Low crime rates are associated with days with precipitation.","PeriodicalId":127005,"journal":{"name":"Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124088128","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

Fusing of Medical Images and Reports in Diagnostics of Brain Diseases 脑疾病诊断中的医学图像融合与报告

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3357793

A. Vatian, N. Gusarova, N. Dobrenko, Anton Klochkov, N. Nigmatullin, A. Lobantsev, A. Shalyto

{"title":"Fusing of Medical Images and Reports in Diagnostics of Brain Diseases","authors":"A. Vatian, N. Gusarova, N. Dobrenko, Anton Klochkov, N. Nigmatullin, A. Lobantsev, A. Shalyto","doi":"10.1145/3357777.3357793","DOIUrl":"https://doi.org/10.1145/3357777.3357793","url":null,"abstract":"The combination of MRI images with textual clinical records, has a great potential since the former contains a raw information about study area of the human body, and the latter contains a human integral assessment of the image performed by doctor. In other words, there is a problem of including integral information received from clinicians in medical image processing at the feature fusion level. On the example of the multiple sclerosis diagnosis we study the methods of training deep neural networks to answer the following questions: is it possible to improve the quality of diagnosis of multiple sclerosis by fusing information obtained from a series of MRI images and from texts of medical reports corresponding to these images; what advantages gives an early or a late fusion method respectively in solving this problem? We proposed the end-to-end architecture of the neural network, which, using the \"early\" information fusion, determines the presence of multiple sclerosis of a patient with a network trust level (accuracy) of 87.5%, compared to the 60% trust level obtained on the same dataset using only MRI images, i.e. without fusion of textual conclusions of radiologists.","PeriodicalId":127005,"journal":{"name":"Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127594101","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: 4

Technology Road Mapping of Two Machine Learning Methods for Triaging Emergency Department Patients in Australia 澳大利亚急诊科患者分诊的两种机器学习方法的技术路线图

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3357779

S. Yan, Junyan Peng, H. Grain, Meng Yi

{"title":"Technology Road Mapping of Two Machine Learning Methods for Triaging Emergency Department Patients in Australia","authors":"S. Yan, Junyan Peng, H. Grain, Meng Yi","doi":"10.1145/3357777.3357779","DOIUrl":"https://doi.org/10.1145/3357777.3357779","url":null,"abstract":"Triaging is the categorization of patients attending the emergency departments (ED) into categories based on the patient's condition at arrival. In Australia, triage is a manual process, not guaranteed consistent and potentially error-prone. The problem with the manual process is that assigning incorrect triage categories to patients can result in delay of treatment for some patients. With the establishment of the Australian national health record system (MyHealth) and clinical data sharing standards such as HL7, it is possible to use patient history information as well as data about the patients' conditions at arrival in ED to quickly and accurately assign a triage category. The wide availability and application of machine learning (ML) methods, including medical applications using such methods, make these methods a possible solution to this problem. Before implementation of ML algorithms in triage, it is essential to understand the multiple dimensions of potential outcomes of health-services, including changes of clinical behaviors and workflows, \"social-economical-technical\", and ethical and legal debates. This research uses Context-Content-Process (CCP), SWOT and \"Khoja--Durrani--Scott\" (KDS) frameworks to provide an initial review of a technical \"roadmap\" of the classification of patients attending emergency departments (ED) using different stages of Naïve Bayes (NB) and Neural Network (NN) machine learning (ML) methods. This is the first research looking at the potential to use ML methods to assist in triage of patients in the Australia context considering outcomes. This research could be used to evaluate automation of the triage process or to support the manual process. The research results suggest that it is necessary to understand these multiple outcomes before future implementations are actually conducted.","PeriodicalId":127005,"journal":{"name":"Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence","volume":"17 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285183","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

Incorporating Singular Value Decomposition in User-based Collaborative Filtering Technique for a Movie Recommendation System: A Comparative Study 结合奇异值分解的基于用户的协同过滤技术在电影推荐系统中的比较研究

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3357782

Vito Xituo Chen, T. Tang

引用次数: 11

Offline Signature Verification using Clustering Technique 基于聚类技术的离线签名验证

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI: 10.1145/3357777.3357789

Varun Pandya

引用次数: 2

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence 2019年模式识别与人工智能国际会议论文集

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.1145/3357777

引用次数: 0