Applied Medical Informaticvs最新文献

{"title":"Replacing the SIR epidemic model with a neural network and training it further to increase prediction accuracy","authors":"G. Bogacsovics, A. Hajdu, Róbert Lakatos, Marcell Beregi-Kovács, Attila Tiba, H. Tomán","doi":"10.33039/AMI.2021.02.003","DOIUrl":"https://doi.org/10.33039/AMI.2021.02.003","url":null,"abstract":"Researchers often use theoretical models which provide a relatively sim- ple, yet concise and effective way of modelling various phenomena. However, it is a well-known fact that the more complex the model, the more complex the mathematical description is. For this reason, theoretical models generally avoid large complexity and aim for the simplest possible definition, which although makes models mathematically more manageable, in practice it also often leads to sub-optimal performance. Furthermore, the data collected during the observations usually contain confounding factors, for which a simple theoretical model can not be prepared. Overall, mathematical models are usually too rigid and sophisticated, and therefore cannot really deal with sudden changes in the environment. The application of artificial intelligence, however, provides a good opportunity to develop complex models that can combine the basic capabilities of the theoretical models with the ability to learn more complex relationships. It has been shown [16] that with neural networks, we can build such models that can approximate mathematical functions. Trained artificial neural networks are thus able to behave like theoretical models developed for different fields, while still retaining their overall flexibility, which guarantees an overall better performance in a complex realworld environment. The aim of our study is to show our notion that we can create an architecture using neural networks, which is able to approximate a given theoretical model, and then further improve it with the help of real data to suit the real world and its various aspects better. In order to validate the functionality of the architecture developed by us, we have selected a simple theoretical model, namely the Kermack-McKendrick one [4] as the base of our research. This is an SIR [2] model, which is a relatively simple compartmental epidemic model, based on differential equations that can be used well for infections that spread very similarly to influenza or COVID. However, on one hand, the SIR model relies too heavily on its parameters, with slight changes in them leading to drastic overall changes of the S, I and R curves, and on the other hand, the simplicity of the SIR model distorts its accuracy in many cases. In our paper, by using the SIR model, we will show that the architecture described above can be a valid approach to modeling the spread of a given disease (such as influenza or COVID-19). To this end, we detail the accuracy of our models with different settings and configurations and show that it performs better than both a simple mathematical model and a plain neural network with randomly initialized weights.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"284 1","pages":"73-91"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87096682","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}

{"title":"Script-aided generation of Mental Cutting Test exercises using Blender","authors":"Robert Tóth","doi":"10.33039/AMI.2021.03.011","DOIUrl":"https://doi.org/10.33039/AMI.2021.03.011","url":null,"abstract":"This paper presents a possible generation process how to efficiently model, export and render resources of Mental Cutting Test exercises with the use of Blender.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90841426","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}

{"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}

{"title":"Dealing with uncertainty: A rough-set-based approach with the background of classical logic","authors":"Tamás Kádek, Tamás Mihálydeák","doi":"10.33039/AMI.2021.02.005","DOIUrl":"https://doi.org/10.33039/AMI.2021.02.005","url":null,"abstract":"The representative-based approximation has been widely studied in rough set theory. Hence, rough set approximations can be defined by the system of representatives, which plays a crucial role in set approximation. In the authors’ previous research a possible use of the similarity-based rough set in first-order logic was investigated. Now our focus has changed to representative-based approximation systems. In this article the authors show a logical system relying on representative-based set approximation. In our approach a three-valued partial logic system is introduced. Based on the properties of the approximation space, our theorems prove that in some cases, there exists an efficient way to evaluate the first-order formulae.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81801193","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}

{"title":"Compositional trend filtering","authors":"Christopher Rieser, P. Filzmoser","doi":"10.33039/AMI.2021.02.004","DOIUrl":"https://doi.org/10.33039/AMI.2021.02.004","url":null,"abstract":"Trend filtering is known as the technique for detecting piecewise linear trends in univariate time series. This technique is extended to the setting of compositional data, which are multivariate data where only the relative information is of importance. According to this, we formulate the problem and present a procedure how to efficiently solve it. To show the usefulness of this method, we consider the number of COVID-19 infections in several European countries in a chosen time period.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"23 1","pages":"257-270"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82096999","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}

{"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}

{"title":"Trees classification based on Fourier coefficients of the sapflow density flux","authors":"D. Efrosinin, I. Kochetkova, N. Stepanova, Alexey Yarovslavtsev, K. Samouylov, R. Valentini","doi":"10.33039/AMI.2021.03.002","DOIUrl":"https://doi.org/10.33039/AMI.2021.03.002","url":null,"abstract":"In this paper we study the possibility to use the artificial neural networks for trees classification based on real and approximated values of the sap flow density flux describing water transport in trees. The data sets were generated by means of a new tree monitoring system TreeTalker. The Fourier series-based model is used for fitting the data sets with periodic patterns. The multivariate regression model defines the functional dependencies between sap flow density and temperature time series. The paper shows that Fourier coefficients can be successfully used as elements of the feature vectors required to solve different classification problems. Here we train multilayer neural networks to classify the trees according to different types of classes. The quality of the developed model for prediction and classification is verified by numerous numerical examples.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"21 1","pages":"109-123"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76516547","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}

{"title":"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","authors":"A. A. Berlin, V. Nikol'skiy, I. Krasotkina, T. Dudareva, V. N. Gorbatova, A. V. Sorokin, V. Lobachev, S. Dubina, M. Sinkevich, Jsc «Energotex»","doi":"10.31044/1994-6260-2021-0-5-2-11","DOIUrl":"https://doi.org/10.31044/1994-6260-2021-0-5-2-11","url":null,"abstract":"The analysis of the modern regulatory framework for quality testing of bituminous binders has been carried out. A methodology for effectiveness evaluation of powder modifiers introduced into asphalt concrete mixtures according to «Russian dry process» was proposed. It is based on rheological tests of «model binders» (MB). The set of tests and standard indicators in a wide temperature range were determined. A new indicator for evaluation of the binder quality is proposed. It is destruction temperature of bituminous binders during fatigue tests.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"20 1","pages":"2-11"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87731737","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}

{"title":"Corrigendum to “Pentagonal and heptagonal repdigits” [Annales Mathematicae et Informaticae 52 (2020) 137–145]","authors":"Bir Kafle, F. Luca, A. Togbé","doi":"10.33039/AMI.2021.03.010","DOIUrl":"https://doi.org/10.33039/AMI.2021.03.010","url":null,"abstract":"Our original paper [1], contains some typos that we would like to fix here. These typos do not affect the final results that we obtained.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79462547","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}

{"title":"Portfolio solver for verifying Binarized Neural Networks","authors":"Gergely Kovásznai, Krisztián Gajdár, Nina Narodytska","doi":"10.33039/AMI.2021.03.007","DOIUrl":"https://doi.org/10.33039/AMI.2021.03.007","url":null,"abstract":"Although deep learning is a very successful AI technology, many concerns have been raised about to what extent the decisions making process of deep neural networks can be trusted. Verifying of properties of neural networks such as adversarial robustness and network equivalence sheds light on the trustiness of such systems. We focus on an important family of deep neural networks, the Binarized Neural Networks (BNNs) that are useful in resourceconstrained environments, like embedded devices. We introduce our portfolio solver that is able to encode BNN properties for SAT, SMT, and MIP solvers and run them in parallel, in a portfolio setting. In the paper we propose all the corresponding encodings of different types of BNN layers as well as BNN properties into SAT, SMT, cardinality constrains, and pseudo-Boolean constraints. Our experimental results demonstrate that our solver is capable of verifying adversarial robustness of medium-sized BNNs in reasonable time and seems to scale for larger BNNs. We also report on experiments on network equivalence with promising results.","PeriodicalId":8040,"journal":{"name":"Applied Medical Informaticvs","volume":"53 1","pages":"183-200"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74965803","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}