Applied Medical Informaticvs最新文献_第4页

A barycentric coordinates-based visualization framework for movement of microscopic organisms 一个基于质心坐标的可视化框架，用于微观有机体的运动

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.04.006

Andrea Bodonyi, Győző Kurucz, G. Holló, Roland Kunkli

引用次数: 0

Script-aided generation of Mental Cutting Test exercises using Blender 脚本辅助生成的心理切割测试练习使用搅拌机

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.03.011

Robert Tóth

引用次数: 2

Investigation of the efficiency of an interconnected convolutional neural network by classifying medical images 互连卷积神经网络在医学图像分类中的效率研究

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.04.001

Oktavian Lantang, G. Terdik, A. Hajdu, Attila Tiba

{"title":"Investigation of the efficiency of an interconnected convolutional neural network by classifying medical images","authors":"Oktavian Lantang, G. Terdik, A. Hajdu, Attila Tiba","doi":"10.33039/AMI.2021.04.001","DOIUrl":"https://doi.org/10.33039/AMI.2021.04.001","url":null,"abstract":"Convolutional Neural Network (CNN) for medical image classification has produced satisfying work [11, 12, 15]. Several pretrained models such as VGG19 [17], InceptionV3 [18], and MobileNet [8] are architectures that can be relied on to design high accuracy classification models. This work investigates the performance of three pretrained models with two methods of training. The first method trains the model independently, meaning that each model is given an input and trained separately, then the best results are determined by majority voting. In the second method the three pretrained models are trained simultaneously as interconnected models. The interconnected model adopts an ensemble architecture as is shown in [7]. By training multiple CNNs, this work gives optimum results compared to a single CNN. The difference is that the three subnetworks are trained simultaneously in an interconnected network and showing one expected result. ∗This work was supported by the construction EFOP-3.6.3-VEKOP-16-2017-00002. The project was supported by the European Union, co-financed by the European Social Fund. Research was also supported by the ÚNKP-20-4-I New National Excellence Program of the Ministry for Innovation and Technology from the Source of the National Research, Development and Innovation Fund, and by LPDP Indonesia in the form of a doctoral scholarship (https://www.lpdp.kemenkeu.go.id). Annales Mathematicae et Informaticae 53 (2021) pp. 219–234 doi: https://doi.org/10.33039/ami.2021.04.001 url: https://ami.uni-eszterhazy.hu","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"57 1","pages":"219-234"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90846825","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

Dealing with uncertainty: A rough-set-based approach with the background of classical logic 不确定性的处理:基于经典逻辑背景的粗糙集方法

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.02.005

Tamás Kádek, Tamás Mihálydeák

引用次数: 0

Compositional trend filtering 成分趋势滤波

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.02.004

Christopher Rieser, P. Filzmoser

引用次数: 0

Abstractive text summarization for Hungarian 匈牙利语的抽象文本摘要

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.04.002

Zijian Győző Yang, A. Agocs, Gábor Kusper, T. Váradi

{"title":"Abstractive text summarization for Hungarian","authors":"Zijian Győző Yang, A. Agocs, Gábor Kusper, T. Váradi","doi":"10.33039/AMI.2021.04.002","DOIUrl":"https://doi.org/10.33039/AMI.2021.04.002","url":null,"abstract":"In our research we have created a text summarization software tool for Hungarian using multilingual and Hungarian BERT-based models. Two types of text summarization method exist: abstractive and extractive. The abstractive summarization is more similar to human generated summarization. Target summaries may include phrases that the original text does not necessarily contain. This method generates the summarized text by applying keywords that were extracted from the original text. The extractive method summarizes the text by using the most important extracted phrases or sentences from the original text. In our research we have built both abstractive and extractive models for Hungarian. For abstractive models, we have used a multilingual BERT model and Hungarian monolingual BERT models. For extractive summarization, in addition to the BERT models, we have also made experiments with ELECTRA models. We find that the Hungarian monolingual models outperformed the multilingual BERT model in all cases. Furthermore, the ELECTRA small models achieved higher results than some of the BERT models. This result is important because the ELECTRA small models have much fewer parameters and were trained on only 1 GPU within a couple of days. Another important consideration is that the ELECTRA models are much smaller than the BERT models, which is important for the end users. To our best knowledge the first extractive and abstractive summarization systems reported in the present paper are the first such systems for Hungarian.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"15 1","pages":"299-316"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80912316","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: 5

Trees classification based on Fourier coefficients of the sapflow density flux 基于树形树密度通量傅里叶系数的树形树分类

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.03.002

D. Efrosinin, I. Kochetkova, N. Stepanova, Alexey Yarovslavtsev, K. Samouylov, R. Valentini

引用次数: 2

Rubber and rubber-polymer modifiers of asphalt concrete mixtures produced by method of high-temperature shear grinding. Part 2. Methodology for effectiveness evaluation of modifiers 高温剪切研磨法制备沥青混凝土混合料的橡胶及橡胶聚合物改性剂。第2部分。改性剂有效性评价方法

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.31044/1994-6260-2021-0-5-2-11

A. A. Berlin, V. Nikol'skiy, I. Krasotkina, T. Dudareva, V. N. Gorbatova, A. V. Sorokin, V. Lobachev, S. Dubina, M. Sinkevich, Jsc «Energotex»

引用次数: 1

Corrigendum to “Pentagonal and heptagonal repdigits” [Annales Mathematicae et Informaticae 52 (2020) 137–145]

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.03.010

Bir Kafle, F. Luca, A. Togbé

引用次数: 0

Portfolio solver for verifying Binarized Neural Networks 验证二值化神经网络的组合求解器

Applied Medical Informaticvs Pub Date : 2021-01-01 DOI: 10.33039/AMI.2021.03.007

Gergely Kovásznai, Krisztián Gajdár, Nina Narodytska

引用次数: 1