Video games have become a ubiquitous form of entertainment and have been enjoyed by people of all ages around the world. The gaming industry has evolved rapidly, with new games being released every year that push the boundaries of technology and creativity. To ensure that video games are not just technically advanced, but also enjoyable and engaging, measuring the gaming experience is essential because it helps game designers understand how players interact with the game and identify areas for its improvement. The objective of this paper is to examine an interplay of gaming experience dimensions in the context of platform video games and to determine the extent to which they contribute to players’ behavioral intentions. To fulfil this objective, an empirical study was undertaken, involving participants with diverse gaming backgrounds. They were requested to engage in the gameplay of the Stranded Away platformer game and subsequently respond to a post-use questionnaire. The psychometric features of the introduced conceptual model were evaluated with the partial least squares structural equation modeling (PLS-SEM) method. The reported findings demonstrate the importance of evaluating different facets of the gaming experience in video games and showcase the potential of the proposed model and measuring instrument as tools for game designers to enhance the overall quality of their products.
{"title":"Evaluating a Conceptual Model for Measuring Gaming Experience: A Case Study of Stranded Away Platformer Game","authors":"Luka Blašković, Alesandro Žužić, T. Orehovački","doi":"10.3390/info14060350","DOIUrl":"https://doi.org/10.3390/info14060350","url":null,"abstract":"Video games have become a ubiquitous form of entertainment and have been enjoyed by people of all ages around the world. The gaming industry has evolved rapidly, with new games being released every year that push the boundaries of technology and creativity. To ensure that video games are not just technically advanced, but also enjoyable and engaging, measuring the gaming experience is essential because it helps game designers understand how players interact with the game and identify areas for its improvement. The objective of this paper is to examine an interplay of gaming experience dimensions in the context of platform video games and to determine the extent to which they contribute to players’ behavioral intentions. To fulfil this objective, an empirical study was undertaken, involving participants with diverse gaming backgrounds. They were requested to engage in the gameplay of the Stranded Away platformer game and subsequently respond to a post-use questionnaire. The psychometric features of the introduced conceptual model were evaluated with the partial least squares structural equation modeling (PLS-SEM) method. The reported findings demonstrate the importance of evaluating different facets of the gaming experience in video games and showcase the potential of the proposed model and measuring instrument as tools for game designers to enhance the overall quality of their products.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76534001","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}
Kanti Singh Sangher, Archana Singh, Hari Mohan Pandey, Vivek Kumar
The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used “secretive spaces” to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements.
{"title":"Towards Safe Cyber Practices: Developing a Proactive Cyber-Threat Intelligence System for Dark Web Forum Content by Identifying Cybercrimes","authors":"Kanti Singh Sangher, Archana Singh, Hari Mohan Pandey, Vivek Kumar","doi":"10.3390/info14060349","DOIUrl":"https://doi.org/10.3390/info14060349","url":null,"abstract":"The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used “secretive spaces” to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88079877","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}
A. Juan, E. Pérez-Bernabeu, Yuda Li, Xabier A. Martin, Majsa Ammouriova, Barry B. Barrios
The popularity of blockchain technology stems largely from its association with cryptocurrencies, but its potential applications extend beyond this. Fungible tokens, which are interchangeable, can facilitate value transactions, while smart contracts using non-fungible tokens enable the exchange of digital assets. Utilizing blockchain technology, tokenized platforms can create virtual markets that operate without the need for a central authority. In principle, blockchain technology provides these markets with a high degree of security, trustworthiness, and dependability. This article surveys recent developments in these areas, including examples of architectures, designs, challenges, and best practices (case studies) for the design and implementation of tokenized platforms for exchanging digital assets.
{"title":"Tokenized Markets Using Blockchain Technology: Exploring Recent Developments and Opportunities","authors":"A. Juan, E. Pérez-Bernabeu, Yuda Li, Xabier A. Martin, Majsa Ammouriova, Barry B. Barrios","doi":"10.3390/info14060347","DOIUrl":"https://doi.org/10.3390/info14060347","url":null,"abstract":"The popularity of blockchain technology stems largely from its association with cryptocurrencies, but its potential applications extend beyond this. Fungible tokens, which are interchangeable, can facilitate value transactions, while smart contracts using non-fungible tokens enable the exchange of digital assets. Utilizing blockchain technology, tokenized platforms can create virtual markets that operate without the need for a central authority. In principle, blockchain technology provides these markets with a high degree of security, trustworthiness, and dependability. This article surveys recent developments in these areas, including examples of architectures, designs, challenges, and best practices (case studies) for the design and implementation of tokenized platforms for exchanging digital assets.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84783315","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}
Data have been fundamental to the scientific practice of medicine since at least the time of Hippocrates around 2500 years ago, relying on the detailed observation of cases and rigorous comparison between cases [...]
{"title":"Data Science in Health Services","authors":"P. Giabbanelli, J. M. Badham","doi":"10.3390/info14060344","DOIUrl":"https://doi.org/10.3390/info14060344","url":null,"abstract":"Data have been fundamental to the scientific practice of medicine since at least the time of Hippocrates around 2500 years ago, relying on the detailed observation of cases and rigorous comparison between cases [...]","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79476372","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}
Olivier Debauche, Jean Bertin Nkamla Penka, Moad Hani, Adriano Guttadauria, Rachida Ait Abdelouahid, Kaouther Gasmi, Ouafae Ben Hardouz, F. Lebeau, J. Bindelle, H. Soyeurt, N. Gengler, P. Manneback, M. Benjelloun, C. Bertozzi
The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms.
{"title":"Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data","authors":"Olivier Debauche, Jean Bertin Nkamla Penka, Moad Hani, Adriano Guttadauria, Rachida Ait Abdelouahid, Kaouther Gasmi, Ouafae Ben Hardouz, F. Lebeau, J. Bindelle, H. Soyeurt, N. Gengler, P. Manneback, M. Benjelloun, C. Bertozzi","doi":"10.3390/info14060345","DOIUrl":"https://doi.org/10.3390/info14060345","url":null,"abstract":"The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79824062","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}
The COVID-19 pandemic spurred older adults to use information and communication technology (ICT) for maintaining connections and engagement during social distancing. This trend raises concerns about privacy and data safety for older individuals with limited technical knowledge who have adopted ICT reluctantly and may be distinct in their susceptibility to scams, fraud, and identity theft. This paper highlights the gap in the literature regarding the increased privacy and data security risks for older adults adopting technology due to isolation during the pandemic (referred to here as quarantine technology initiates (QTIs)). A literature search informed by healthcare experts explored the intersection of older adults, data privacy, online activity, and COVID-19. A thin and geographically diverse literature was found to consider the risk profile of QTIs with the same lens as for older adults who adopted ICT before or independent of COVID-19 quarantines. The mentioned strategies to mitigate privacy risks were broad, including education, transaction monitoring, and the application of international regulatory models, but were undistinguished from those for non-QTI older adults. Future research should pursue the hypothesis that the risk profile of QTIs may differ in character from that of other older adults, referencing by analogy the nuanced distinctions quantified in credit risk scoring. Such studies would examine the primary data on privacy and data safety implications of hesitant ICT adoption by older adults, using COVID-19 as a natural experiment to identify and evaluate this vulnerable group.
{"title":"Navigating Privacy and Data Safety: The Implications of Increased Online Activity among Older Adults Post-COVID-19 Induced Isolation","authors":"John Alagood, Gayle Prybutok, V. Prybutok","doi":"10.3390/info14060346","DOIUrl":"https://doi.org/10.3390/info14060346","url":null,"abstract":"The COVID-19 pandemic spurred older adults to use information and communication technology (ICT) for maintaining connections and engagement during social distancing. This trend raises concerns about privacy and data safety for older individuals with limited technical knowledge who have adopted ICT reluctantly and may be distinct in their susceptibility to scams, fraud, and identity theft. This paper highlights the gap in the literature regarding the increased privacy and data security risks for older adults adopting technology due to isolation during the pandemic (referred to here as quarantine technology initiates (QTIs)). A literature search informed by healthcare experts explored the intersection of older adults, data privacy, online activity, and COVID-19. A thin and geographically diverse literature was found to consider the risk profile of QTIs with the same lens as for older adults who adopted ICT before or independent of COVID-19 quarantines. The mentioned strategies to mitigate privacy risks were broad, including education, transaction monitoring, and the application of international regulatory models, but were undistinguished from those for non-QTI older adults. Future research should pursue the hypothesis that the risk profile of QTIs may differ in character from that of other older adults, referencing by analogy the nuanced distinctions quantified in credit risk scoring. Such studies would examine the primary data on privacy and data safety implications of hesitant ICT adoption by older adults, using COVID-19 as a natural experiment to identify and evaluate this vulnerable group.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77270282","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}
A. Psaltis, Kassiani Zafeirouli, P. Leskovský, S. Bourou, Juan Camilo Vásquez-Correa, Aitor García-Pablos, S. C. Sánchez, A. Dimou, C. Patrikakis, P. Daras
The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process.
{"title":"Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios","authors":"A. Psaltis, Kassiani Zafeirouli, P. Leskovský, S. Bourou, Juan Camilo Vásquez-Correa, Aitor García-Pablos, S. C. Sánchez, A. Dimou, C. Patrikakis, P. Daras","doi":"10.3390/info14060342","DOIUrl":"https://doi.org/10.3390/info14060342","url":null,"abstract":"The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82325544","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}
In this paper, we proposed a new method for image-based grammatical inference of deterministic, context-free L-systems (D0L systems) from a single sequence. This approach is characterized by first parsing an input image into a sequence of symbols and then, using a genetic algorithm, attempting to infer a grammar that can generate this sequence. This technique has been tested using our test suite and compared to similar algorithms, showing promising results, including solving the problem for systems with more rules than in existing approaches. The tests show that it performs better than similar heuristic methods and can handle the same cases as arithmetic algorithms.
{"title":"D0L-System Inference from a Single Sequence with a Genetic Algorithm","authors":"Mateusz Labedzki, O. Unold","doi":"10.3390/info14060343","DOIUrl":"https://doi.org/10.3390/info14060343","url":null,"abstract":"In this paper, we proposed a new method for image-based grammatical inference of deterministic, context-free L-systems (D0L systems) from a single sequence. This approach is characterized by first parsing an input image into a sequence of symbols and then, using a genetic algorithm, attempting to infer a grammar that can generate this sequence. This technique has been tested using our test suite and compared to similar algorithms, showing promising results, including solving the problem for systems with more rules than in existing approaches. The tests show that it performs better than similar heuristic methods and can handle the same cases as arithmetic algorithms.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79874693","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}
Mao-Hsiang Huang, Amir Naderi, Ping Zhu, Xiaolei Xu, H. Cao
Zebrafish is a well-established model organism for cardiovascular disease studies in which one of the most popular tasks is to assess cardiac functions from the heart beating echo-videos. However, current techniques are often time-consuming and error-prone, making them unsuitable for large-scale analysis. To address this problem, we designed a method to automatically evaluate the ejection fraction of zebrafish from heart echo-videos using a deep-learning model architecture. Our model achieved a validation Dice coefficient of 0.967 and an IoU score of 0.937 which attest to its high accuracy. Our test findings revealed an error rate ranging from 0.11% to 37.05%, with an average error rate of 9.83%. This method is widely applicable in any laboratory setting and can be combined with binary recordings to optimize the efficacy and consistency of large-scale video analysis. By facilitating the precise quantification and monitoring of cardiac function in zebrafish, our approach outperforms traditional methods, substantially reducing the time and effort required for data analysis. The advantages of our method make it a promising tool for cardiovascular research using zebrafish.
{"title":"Assessing Cardiac Functions of Zebrafish from Echocardiography Using Deep Learning","authors":"Mao-Hsiang Huang, Amir Naderi, Ping Zhu, Xiaolei Xu, H. Cao","doi":"10.3390/info14060341","DOIUrl":"https://doi.org/10.3390/info14060341","url":null,"abstract":"Zebrafish is a well-established model organism for cardiovascular disease studies in which one of the most popular tasks is to assess cardiac functions from the heart beating echo-videos. However, current techniques are often time-consuming and error-prone, making them unsuitable for large-scale analysis. To address this problem, we designed a method to automatically evaluate the ejection fraction of zebrafish from heart echo-videos using a deep-learning model architecture. Our model achieved a validation Dice coefficient of 0.967 and an IoU score of 0.937 which attest to its high accuracy. Our test findings revealed an error rate ranging from 0.11% to 37.05%, with an average error rate of 9.83%. This method is widely applicable in any laboratory setting and can be combined with binary recordings to optimize the efficacy and consistency of large-scale video analysis. By facilitating the precise quantification and monitoring of cardiac function in zebrafish, our approach outperforms traditional methods, substantially reducing the time and effort required for data analysis. The advantages of our method make it a promising tool for cardiovascular research using zebrafish.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87677552","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}
S. Belim, Svetlana Yuryevna Belim, E. V. Khiryanov
This article proposes an algorithm for recognizing road signs based on a determination of their color and shape. It first searches for the edge segment of the road sign. The boundary curve of the road sign is defined by the boundary of the edge segment. Approximating the boundaries of a road sign reveals its shape. The hierarchical road sign recognition system forms classes in the form of a sign. Six classes are at the first level. Two classes contain only one road sign. Signs are classified by the color of the edge segment at the second level of the hierarchy. The image inside the edge segment is cut at the third level of the hierarchy. The sign is then identified based on a comparison of the pattern. A computer experiment was carried out on two collections of road signs. The proposed algorithm has a high operating speed and a low percentage of errors.
{"title":"Hierarchical System for Recognition of Traffic Signs Based on Segmentation of Their Images","authors":"S. Belim, Svetlana Yuryevna Belim, E. V. Khiryanov","doi":"10.3390/info14060335","DOIUrl":"https://doi.org/10.3390/info14060335","url":null,"abstract":"This article proposes an algorithm for recognizing road signs based on a determination of their color and shape. It first searches for the edge segment of the road sign. The boundary curve of the road sign is defined by the boundary of the edge segment. Approximating the boundaries of a road sign reveals its shape. The hierarchical road sign recognition system forms classes in the form of a sign. Six classes are at the first level. Two classes contain only one road sign. Signs are classified by the color of the edge segment at the second level of the hierarchy. The image inside the edge segment is cut at the third level of the hierarchy. The sign is then identified based on a comparison of the pattern. A computer experiment was carried out on two collections of road signs. The proposed algorithm has a high operating speed and a low percentage of errors.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87202061","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}
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