Acta Informatica Pragensia最新文献

{"title":"Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification","authors":"J. M. Al-Tuwaijari, Naeem Th. Yousir, Nafea Ali Majeed Alhammad, S. Mostafa","doi":"10.18267/j.aip.189","DOIUrl":"https://doi.org/10.18267/j.aip.189","url":null,"abstract":"Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories: benign and malignant. The three models are supported by transfer learning and have been tested and evaluated based on the criteria of accuracy, loss, precision, recall, f1 score and ROC. Subsequently, the results show that the improved ResNet152 model significantly outperformed the other models and achieved an accuracy score of 92% and an ROC score of 91%. The DenseNet121 and VGG19 models achieve accuracy scores of 90% and 79% and ROC scores of 88% and 75%, respectively. Subsequently, a deep residual learning skin cancer recognition (ResNetScr) system has been implemented based on the ResNet152 model, and it has the capacity to help dermatologists in diagnosing skin cancer.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43661381","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":"Survey on Security and Interoperability of Electronic Health Record Sharing Using Blockchain Technology","authors":"R. P. Puneeth, Govindaswamy Parthasarathy","doi":"10.18267/j.aip.187","DOIUrl":"https://doi.org/10.18267/j.aip.187","url":null,"abstract":"Blockchain is regarded as a significant innovation and shows a set of promising features that can certainly address existing issues in real time applications. Decentralization, greater transparency, improved traceability and secure architecture can revolutionize healthcare systems. With the help of advancement in computer technologies, most healthcare institutions try to store patient data digitally rather than on paper. Electronic health records are regarded as some of the most important assets in healthcare system and are required to be shared among different hospitals and other organizations to improve diagnosis efficiency. While sharing patients’ details, certain basic standards such as integrity and confidentiality of the information need to be considered. Blockchain technology provides the above standards with features of immutability and granting access to stored information only to authorized users. The examination approach depends on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (or PRISMA) rules and an efficient planned search convention is utilized to look through multiple scientific databases to recognize, investigate and separate every important publication. In this paper, we present a solid systematic review on the blockchain and healthcare domain to identify the existing challenges and benefits of applying blockchain technology in healthcare systems. More than 150 scientific papers published in the last ten years are surveyed, resulting in the identifications and summarization of observations made on the different privacy-preserving approaches and also assessment of their performances. We also present a significant architectural solutions of blockchain to achieve interoperability. Thereby, we attempt to analyse the ideas of blockchain in the medical domain, by assessing the advantages and limitations, subsequently giving guidance to other researchers in the area.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49595492","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":"Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System","authors":"M. Balogun, Latifat Adeola Odeniyi, Elijah Olusola Omidiora, S. Olabiyisi, A. Falohun","doi":"10.18267/j.aip.186","DOIUrl":"https://doi.org/10.18267/j.aip.186","url":null,"abstract":"Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system. O.S.O.:","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48920806","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":"What is the Real Threat of Information Explosion?","authors":"Petr Strossa","doi":"10.18267/j.aip.185","DOIUrl":"https://doi.org/10.18267/j.aip.185","url":null,"abstract":"The text is devoted to a consideration of the “information explosion” phenomenon. The exponential growth of publications is compared to the (similarly exponential) growth of population, especially in the countries where most of the publications are created. The increasing tertiary education gross enrolment ratio (naturally associated with involvement in the publication process) is also taken into account. The text comes to a conclusion that either the exponential growth of publications must decrease its base value in our future, or we are heading towards a time point where an increasing number of publications find no readers (if that point is not yet behind us).","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44155628","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":"Use of FURIA for Improving Task Mining","authors":"P. Průcha, Jan Skrbek","doi":"10.18267/j.aip.183","DOIUrl":"https://doi.org/10.18267/j.aip.183","url":null,"abstract":"Companies that use robotic process automation very often deal with the problem of selecting a suitable process for automation. Manual selection of a suitable process is very time-consuming. Therefore, part of the process mining field specializes in selecting suitable processes for automation based on process data. This work deals with the possibility of improving the existing method for finding suitable candidates for automation. To improve the current approach, we remove the limiting restrictions of the current method and use another FURIA rule-learning algorithm for rule detection. We use three different datasets and the WEKA platform to validate the results. The results show that FURIA and the removal of strictly deterministic rules as restrictions turned out to be a competitive approach to the original one. On data presented in this study, the selected approach detected more candidates for automation and with higher accuracy. This study implies that FURIA and not using a strictly deterministic process is an appropriate procedure with certain use cases as other procedures mentioned in this study.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46371308","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":"Improving Privacy-preserving Healthcare Data Sharing in a Cloud Environment Using Hybrid Encryption","authors":"Insaf Boumezbeur, K. Zarour","doi":"10.18267/j.aip.182","DOIUrl":"https://doi.org/10.18267/j.aip.182","url":null,"abstract":"In recent years, cloud computing has been widely used in various fields and is gaining importance in healthcare systems. Patients’ health data are outsourced to cloud storage, enabling healthcare professionals to easily access health information from anywhere and at any time to improve health services. Once patient data are stored in the cloud, they are vulnerable to attacks such as data loss, denial of service (DoS), distributed denial of service (DDoS) and other sorts of cyberattacks. Data confidentiality and patient privacy are more of a problem in the cloud computing context due to their public availability. If a patient's personal information is stolen, he or she may face a range of problems. These are concerns that necessitate more security. The transmission of this sensitive information over the internet is always susceptible to hacking. Therefore, the privacy of patients’ data is considered one of healthcare organizations’ main issues. To overcome this problem, encryption mechanisms that place a significant emphasis on securing data within the cloud environment are used to preserve sensitive health data. A hybrid cryptography approach is employed in this paper to ensure the secure sharing of health data over the cloud. To maintain data privacy and secrecy, a hybrid cryptography mechanism for storing and transporting data to and from the cloud is used. To protect data from malevolent insiders, the encryption key is separated into two halves, controlling access to patient records via a specific technique. This paper shows the implementation and performance evaluation of the proposal as a functional system prototype. The evaluation is based on the key generation time, the record encryption time, the record decryption time, the record upload time and the record download time for different user numbers and different file sizes varying from 0.1 MB to 500 MB. The findings show that the proposal performs better than other state-of-the-art systems and can practically share secure health data in cloud environments.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45098146","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":"Exploring Facebook Identity Construction of Vietnamese Netizens","authors":"Hai Chung Pham, Lien Nguyen, Phuong Tran, T. Tran","doi":"10.18267/j.aip.181","DOIUrl":"https://doi.org/10.18267/j.aip.181","url":null,"abstract":"Studying the ways in which people construct their identities in online environments is a pressing contemporary concern. The research reported in this article was designed to examine the uses of, and influences on, Vietnamese respondents’ identity formation on Facebook. Data were collected by means of a social survey and the application of the Zaltman metaphor elicitation technique, a procedure that searches fo r customers’ thoughts and emotions by digging deep into the visual and non -visual illustrations that customers collect or make on their own. The findings show how Vietnamese Facebook users present themselves and how they thereby facilitate their self-expansion and maintain their sense of self-esteem. According to the analysis, it can be suggested that Facebook is where adults portray their socially conformed versions against social reflection. They use this platform to seek validation, demonstrate their professional side to make them look better in the eyes of society. Drivers of online identity formation are revealed through negotiating with conflicts in their existing identities. The level of self-modification amongst respondents is adjusted in relation to their social vigilance, conformity and motivations in and between social categorization.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44938528","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":"Incremental Model Transformation with Epsilon in Model-Driven Engineering","authors":"Marzieh Ghorbani, Mohammadreza Sharbaf, B. Zamani","doi":"10.18267/j.aip.179","DOIUrl":"https://doi.org/10.18267/j.aip.179","url":null,"abstract":"Model-Driven Engineering (MDE) is a software development paradigm that uses models as the main artifacts in the development process. MDE uses model transformations to propagate changes between source and target models. In some development scenarios, target models should be updated based on the evolution of source models. In such cases, it is required to re-execute all transformation rules to update the target model. Incremental execution of transformations, which partially executes the transformation rules, is a solution to this problem. The Epsilon Transformation Language (ETL) is a well-known model transformation language that does not support incremental executions. In this paper, we propose an approach to support the incremental execution of ETL transformations. Our proposal includes a process, as well as a prototype, to propagate changes to the target model. In the proposed approach, all the changes in the source model are detected to identify and re-execute the rules which deal with computing the required elements for updating the target model. We evaluated the correctness and performance of our approach by means of a case study. Compared to the standard ETL, the results are promising regarding the correctness of target models as well as faster execution of the transformation.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44508776","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":"Comments on Copyright Aspects of Use of Copyright Works in Digital Teaching","authors":"Martin Boháček","doi":"10.18267/j.aip.178","DOIUrl":"https://doi.org/10.18267/j.aip.178","url":null,"abstract":"In the current COVID-19 pandemic, teachers and students routinely take part in schooling via digital technologies, using their own and other authors' copyrighted works, even without their permission or licence. The aim of this article is to identify these aspects of copyright in online teaching and to offer solutions to them. Some of these aspects are addressed by EU Directive No. 2019/790 (Digital Directive), the implementation of which was reflected in the draft amendment to the Czech Copyright Act, which had not addressed them. To fulfil this goal, the author asked several research questions: What forms of use of copyright works refer to the terms e-learning, online teaching or digital teaching? Is it possible to apply a statutory teaching exemption to the use of copyright works of other authors without their consent under the current wording of the Copyright Act (AutZ)? Is there any case law regarding such an exemption that would be applicable to e-learning? How will the conditions for the use of copyright works in e-learning change with the AutZ amendment following the implementation of EU Directive No. 2019/790 (Digital Directive) and what practical organizational recommendations can be made? To the above goal and questions, we conducted research in Czech law relating to the use of copyright works in digital teaching. For this purpose, we used the method of empirical research of Czech legislation and case law containing the terms e-learning and online or digital teaching. We also employed a comparative method to compare Czech law and EU law. The article is primarily an overview with elements of reflection. It does not comprehensively address all aspects of copyright in teaching, such as plagiarism in general, but identifies copyright aspects specific to e-learning. Copyright: © 2022 by the author(s).","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49643656","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":"Privacy Preservation and Access Control for Sharing Electronic Health Records Using Blockchain Technology","authors":"Insaf Boumezbeur, K. Zarour","doi":"10.18267/j.aip.176","DOIUrl":"https://doi.org/10.18267/j.aip.176","url":null,"abstract":"Sharing of Electronic Health Records (EHRs) is of significant importance in health care. Lately, a cloudbased electronic health record sharing scheme has been used extensively to share patient records among various healthcare organizations. However, cloud centralization may compromise patients’ privacy and security. Due to the special features of blockchain, it is important to see this technology as a promising solution to resolve these issues. This article proposes a privacy-preserving, secure EHR sharing and access control framework based on blockchain technology. The proposal aims to implement EHR blockchain technology and ensure that electronic records are stored safely by specifying user access permissions. We emulate the cryptographic primitives and use smart contracts to describe the relationships between the EHR owner and EHR user through the proposed system on the Ethereum blockchain. We assess the proposal results based on encryption and decryption time and the costs of the smart contract. The encryption and decryption times are proportional to the size of the EHR, which varies from 128 KB to 128 MB. When it comes to encryption, the smallest EHR takes 0.0012 s to encrypt, while the largest EHR, which is 128 MB, takes 1.4149 s. On the other hand, a 128 KB EHR takes 0.0013 s to decrypt, whereas a 128 MB EHR requires 1.6284 s. As a result, performance evaluation and security analysis confirm that the proposal is secure for practical application.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42624858","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}