Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics. Evidence-driven policymaking has been facilitated through the proficient application of AI and natural language processing (NLP) methodologies to analyse the vast landscape of social media discussions. However, recent research works have failed to demonstrate a methodology to discern the underlying factors influencing COVID-19-related discussion topics. In this scholarly endeavour, an innovative AI- and NLP-based framework is deployed, incorporating translation, sentiment analysis, topic analysis, logistic regression, and clustering techniques to meticulously identify and elucidate the factors that are relevant to any discussion topics within the social media corpus. This pioneering methodology is rigorously tested and evaluated using a dataset comprising 152,070 COVID-19-related tweets, collected between 15th July 2021 and 20th April 2023, encompassing discourse in 58 distinct languages. The AI-driven regression analysis revealed 37 distinct observations, with 20 of them demonstrating a higher level of significance. In parallel, clustering analysis identified 15 observations, including nine of substantial relevance. These 52 AI-facilitated observations collectively unveil and delineate the factors that are intricately linked to five core discussion topics that are prevalent in the realm of COVID-19 discourse on Twitter. To the best of our knowledge, this research constitutes the inaugural effort in autonomously identifying factors associated with COVID-19 discussion topics, marking a pioneering application of AI algorithms in this domain. The implementation of this method holds the potential to significantly enhance the practice of evidence-based policymaking pertaining to matters concerning COVID-19.
{"title":"A New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics","authors":"Fahim Sufi","doi":"10.3390/info14100545","DOIUrl":"https://doi.org/10.3390/info14100545","url":null,"abstract":"Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics. Evidence-driven policymaking has been facilitated through the proficient application of AI and natural language processing (NLP) methodologies to analyse the vast landscape of social media discussions. However, recent research works have failed to demonstrate a methodology to discern the underlying factors influencing COVID-19-related discussion topics. In this scholarly endeavour, an innovative AI- and NLP-based framework is deployed, incorporating translation, sentiment analysis, topic analysis, logistic regression, and clustering techniques to meticulously identify and elucidate the factors that are relevant to any discussion topics within the social media corpus. This pioneering methodology is rigorously tested and evaluated using a dataset comprising 152,070 COVID-19-related tweets, collected between 15th July 2021 and 20th April 2023, encompassing discourse in 58 distinct languages. The AI-driven regression analysis revealed 37 distinct observations, with 20 of them demonstrating a higher level of significance. In parallel, clustering analysis identified 15 observations, including nine of substantial relevance. These 52 AI-facilitated observations collectively unveil and delineate the factors that are intricately linked to five core discussion topics that are prevalent in the realm of COVID-19 discourse on Twitter. To the best of our knowledge, this research constitutes the inaugural effort in autonomously identifying factors associated with COVID-19 discussion topics, marking a pioneering application of AI algorithms in this domain. The implementation of this method holds the potential to significantly enhance the practice of evidence-based policymaking pertaining to matters concerning COVID-19.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483294","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}
Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the VR visit experience, notably those related to telepresence, a key concept in tourism through VR. Furthermore, the study aims to elucidate how telepresence influences mental imagery, attitudes towards tourist destinations, and actual visit intentions. To do this, qualitative data were gathered between February and June 2022 from 34 semi-structured interviews with respondents who viewed a VR video of the destination. A second study collected quantitative data from 400 participants through face-to-face questionnaires after a VR video view between June and August 2022. The findings reveal that telepresence comprises three dimensions: realism of the virtual environment, immersion, and the sense of presence in the virtual environment. Telepresence, in turn, both directly and indirectly affects actual visit intentions, with mental imagery and attitude toward tourist destinations partially mediating those relationships. This study provides methodological, theoretical, and tourism management implications to enhance our comprehension of telepresence’s facets, its measurement, and the process by which VR influences real visit intentions.
{"title":"The Impact of Virtual Reality (VR) Tour Experience on Tourists’ Intention to Visit","authors":"Chourouk Ouerghemmi, Myriam Ertz, Néji Bouslama, Urvashi Tandon","doi":"10.3390/info14100546","DOIUrl":"https://doi.org/10.3390/info14100546","url":null,"abstract":"Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the VR visit experience, notably those related to telepresence, a key concept in tourism through VR. Furthermore, the study aims to elucidate how telepresence influences mental imagery, attitudes towards tourist destinations, and actual visit intentions. To do this, qualitative data were gathered between February and June 2022 from 34 semi-structured interviews with respondents who viewed a VR video of the destination. A second study collected quantitative data from 400 participants through face-to-face questionnaires after a VR video view between June and August 2022. The findings reveal that telepresence comprises three dimensions: realism of the virtual environment, immersion, and the sense of presence in the virtual environment. Telepresence, in turn, both directly and indirectly affects actual visit intentions, with mental imagery and attitude toward tourist destinations partially mediating those relationships. This study provides methodological, theoretical, and tourism management implications to enhance our comprehension of telepresence’s facets, its measurement, and the process by which VR influences real visit intentions.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483307","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 smart management of water resources is an increasingly important topic in today’s society. In this context, the paradigm of Smart Water Grids (SWGs) aims at a constant monitoring through a network of smart nodes deployed over the water distribution infrastructure. This facilitates a continuous assessment of water quality and the state of health of the pipeline infrastructure, enabling early detection of leaks and water contamination. Acoustic-wave-based technology has arisen as a viable communication technique among the nodes of the network. Such technology can be suitable for replacing traditional wireless networks in SWGs, as the acoustic channel is intrinsically embedded in the water supply network. However, the fluid-filled pipe is one of the most challenging media for data communication. Existing works proposing in-pipe acoustic communication systems are promising, but a comparison between the different implementations and their performance has not yet been reported. This paper reviews existing works dealing with acoustic-based communication networks in real large-scale urban water supply networks. For this purpose, an overview of the characteristics, trends and design challenges of existing works is provided in the present work as a guideline for future research.
{"title":"From Radio to In-Pipe Acoustic Communication for Smart Water Networks in Urban Environments: Design Challenges and Future Trends","authors":"Markeljan Fishta, Erica Raviola, Franco Fiori","doi":"10.3390/info14100544","DOIUrl":"https://doi.org/10.3390/info14100544","url":null,"abstract":"The smart management of water resources is an increasingly important topic in today’s society. In this context, the paradigm of Smart Water Grids (SWGs) aims at a constant monitoring through a network of smart nodes deployed over the water distribution infrastructure. This facilitates a continuous assessment of water quality and the state of health of the pipeline infrastructure, enabling early detection of leaks and water contamination. Acoustic-wave-based technology has arisen as a viable communication technique among the nodes of the network. Such technology can be suitable for replacing traditional wireless networks in SWGs, as the acoustic channel is intrinsically embedded in the water supply network. However, the fluid-filled pipe is one of the most challenging media for data communication. Existing works proposing in-pipe acoustic communication systems are promising, but a comparison between the different implementations and their performance has not yet been reported. This paper reviews existing works dealing with acoustic-based communication networks in real large-scale urban water supply networks. For this purpose, an overview of the characteristics, trends and design challenges of existing works is provided in the present work as a guideline for future research.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135644214","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}
Juan Fernando Flórez Marulanda, Cesar A. Collazos, Julio Ariel Hurtado
Previous research has explored different models of synchronous remote learning environments supported by videoconferencing and virtual reality platforms. However, few studies have evaluated the preference and acceptance of synchronous remote learning in a course streamed in an immersive or augmented reality platform. This case study uses ANOVA analysis to examine engineering students’ preferences for receiving instruction during the COVID-19 pandemic in three classroom types: face-to-face, conventional virtual (mediated by videoconferencing) and an immersive virtual classroom (IVC). Likewise, structural equation modeling was used to analyze the acceptance of the IVC perceived by students, this includes four latent factors: ease of receiving a class, perceived usefulness, attitude towards IVC and IVC use. The findings showed that the IVC used in synchronous remote learning has a similar level of preference to the face-to-face classroom and a higher level than the conventional virtual one. Despite the high preference for receiving remote instruction in IVC, aspects such as audio delays that affect interaction still need to be resolved. On the other hand, a key aspect for a good performance of these environments is the dynamics associated with the teaching–learning processes and the instructor’ qualities.
{"title":"Evaluating an Immersive Virtual Classroom as an Augmented Reality Platform in Synchronous Remote Learning","authors":"Juan Fernando Flórez Marulanda, Cesar A. Collazos, Julio Ariel Hurtado","doi":"10.3390/info14100543","DOIUrl":"https://doi.org/10.3390/info14100543","url":null,"abstract":"Previous research has explored different models of synchronous remote learning environments supported by videoconferencing and virtual reality platforms. However, few studies have evaluated the preference and acceptance of synchronous remote learning in a course streamed in an immersive or augmented reality platform. This case study uses ANOVA analysis to examine engineering students’ preferences for receiving instruction during the COVID-19 pandemic in three classroom types: face-to-face, conventional virtual (mediated by videoconferencing) and an immersive virtual classroom (IVC). Likewise, structural equation modeling was used to analyze the acceptance of the IVC perceived by students, this includes four latent factors: ease of receiving a class, perceived usefulness, attitude towards IVC and IVC use. The findings showed that the IVC used in synchronous remote learning has a similar level of preference to the face-to-face classroom and a higher level than the conventional virtual one. Despite the high preference for receiving remote instruction in IVC, aspects such as audio delays that affect interaction still need to be resolved. On the other hand, a key aspect for a good performance of these environments is the dynamics associated with the teaching–learning processes and the instructor’ qualities.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135597042","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}
Maria Carmela Groccia, Rosita Guido, Domenico Conforti, Corrado Pelaia, Giuseppe Armentaro, Alfredo Francesco Toscani, Sofia Miceli, Elena Succurro, Marta Letizia Hribal, Angela Sciacqua
Chronic heart failure (CHF) is a clinical syndrome characterised by symptoms and signs due to structural and/or functional abnormalities of the heart. CHF confers risk for cardiovascular deterioration events which cause recurrent hospitalisations and high mortality rates. The early prediction of these events is very important to limit serious consequences, improve the quality of care, and reduce its burden. CHF is a progressive condition in which patients may remain asymptomatic before the onset of symptoms, as observed in heart failure with a preserved ejection fraction. The early detection of underlying causes is critical for treatment optimisation and prognosis improvement. To develop models to predict cardiovascular deterioration events in patients with chronic heart failure, a real dataset was constructed and a knowledge discovery task was implemented in this study. The dataset is imbalanced, as it is common in real-world applications. It thus posed a challenge because imbalanced datasets tend to be overwhelmed by the abundance of majority-class instances during the learning process. To address the issue, a pipeline was developed specifically to handle imbalanced data. Different predictive models were developed and compared. To enhance sensitivity and other performance metrics, we employed multiple approaches, including data resampling, cost-sensitive methods, and a hybrid method that combines both techniques. These methods were utilised to assess the predictive capabilities of the models and their effectiveness in handling imbalanced data. By using these metrics, we aimed to identify the most effective strategies for achieving improved model performance in real scenarios with imbalanced datasets. The best model for predicting cardiovascular events achieved mean a sensitivity 65%, a mean specificity 55%, and a mean area under the curve of 0.71. The results show that cost-sensitive models combined with over/under sampling approaches are effective for the meaningful prediction of cardiovascular events in CHF patients.
{"title":"Cost-Sensitive Models to Predict Risk of Cardiovascular Events in Patients with Chronic Heart Failure","authors":"Maria Carmela Groccia, Rosita Guido, Domenico Conforti, Corrado Pelaia, Giuseppe Armentaro, Alfredo Francesco Toscani, Sofia Miceli, Elena Succurro, Marta Letizia Hribal, Angela Sciacqua","doi":"10.3390/info14100542","DOIUrl":"https://doi.org/10.3390/info14100542","url":null,"abstract":"Chronic heart failure (CHF) is a clinical syndrome characterised by symptoms and signs due to structural and/or functional abnormalities of the heart. CHF confers risk for cardiovascular deterioration events which cause recurrent hospitalisations and high mortality rates. The early prediction of these events is very important to limit serious consequences, improve the quality of care, and reduce its burden. CHF is a progressive condition in which patients may remain asymptomatic before the onset of symptoms, as observed in heart failure with a preserved ejection fraction. The early detection of underlying causes is critical for treatment optimisation and prognosis improvement. To develop models to predict cardiovascular deterioration events in patients with chronic heart failure, a real dataset was constructed and a knowledge discovery task was implemented in this study. The dataset is imbalanced, as it is common in real-world applications. It thus posed a challenge because imbalanced datasets tend to be overwhelmed by the abundance of majority-class instances during the learning process. To address the issue, a pipeline was developed specifically to handle imbalanced data. Different predictive models were developed and compared. To enhance sensitivity and other performance metrics, we employed multiple approaches, including data resampling, cost-sensitive methods, and a hybrid method that combines both techniques. These methods were utilised to assess the predictive capabilities of the models and their effectiveness in handling imbalanced data. By using these metrics, we aimed to identify the most effective strategies for achieving improved model performance in real scenarios with imbalanced datasets. The best model for predicting cardiovascular events achieved mean a sensitivity 65%, a mean specificity 55%, and a mean area under the curve of 0.71. The results show that cost-sensitive models combined with over/under sampling approaches are effective for the meaningful prediction of cardiovascular events in CHF patients.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135743780","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 power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.
{"title":"Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System","authors":"Yasmin Nasser Mohamed, Serhat Seker, Tahir Cetin Akinci","doi":"10.3390/info14100540","DOIUrl":"https://doi.org/10.3390/info14100540","url":null,"abstract":"The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739421","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}
Pummy Dhiman, Anupam Bonkra, Amandeep Kaur, Yonis Gulzar, Yasir Hamid, Mohammad Shuaib Mir, Arjumand Bano Soomro, Osman Elwasila
Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models. In this study, we examined global developments in empirical XAI research in the medical field. The bibliometric analysis tools VOSviewer and Biblioshiny were used to examine 171 open access publications from the Scopus database (2019–2022). Our findings point to several prospects for growth in this area, notably in areas of medicine like diagnostic imaging. With 109 research articles using XAI for healthcare classification, prediction, and diagnosis, the USA leads the world in research output. With 88 citations, IEEE Access has the greatest number of publications of all the journals. Our extensive survey covers a range of XAI applications in healthcare, such as diagnosis, therapy, prevention, and palliation, and offers helpful insights for researchers who are interested in this field. This report provides a direction for future healthcare industry research endeavors.
{"title":"Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis","authors":"Pummy Dhiman, Anupam Bonkra, Amandeep Kaur, Yonis Gulzar, Yasir Hamid, Mohammad Shuaib Mir, Arjumand Bano Soomro, Osman Elwasila","doi":"10.3390/info14100541","DOIUrl":"https://doi.org/10.3390/info14100541","url":null,"abstract":"Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models. In this study, we examined global developments in empirical XAI research in the medical field. The bibliometric analysis tools VOSviewer and Biblioshiny were used to examine 171 open access publications from the Scopus database (2019–2022). Our findings point to several prospects for growth in this area, notably in areas of medicine like diagnostic imaging. With 109 research articles using XAI for healthcare classification, prediction, and diagnosis, the USA leads the world in research output. With 88 citations, IEEE Access has the greatest number of publications of all the journals. Our extensive survey covers a range of XAI applications in healthcare, such as diagnosis, therapy, prevention, and palliation, and offers helpful insights for researchers who are interested in this field. This report provides a direction for future healthcare industry research endeavors.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135738828","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}
One of the WHO’s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver’s cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%.
{"title":"ECG-Based Driving Fatigue Detection using Heart Rate Variability Analysis with Mutual Information","authors":"Junartho Halomoan, Kalamullah Ramli, Dodi Sudiana, Teddy Surya Gunawan, Muhammad Salman","doi":"10.3390/info14100539","DOIUrl":"https://doi.org/10.3390/info14100539","url":null,"abstract":"One of the WHO’s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver’s cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135895136","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}
Ioanna Anastasaki, George Drosatos, George Pavlidis, Konstantinos Rantos
Immersive technologies are revolutionary technological advancements that offer users unparalleled experiences of immersion in a virtual or mixed world of virtual and real elements. In such technology, user privacy, security, and anonymity are paramount, as users often share private and sensitive information. Therefore, user authentication is a critical requirement in these environments. This paper presents a systematic literature review of recently published research papers on immersive technology-based user authentication mechanisms. After conducting the literature search in September 2023 using Scopus, the selection process identified 36 research publications that were further analyzed. The analysis revealed three major types of authentications related to immersive technologies, consistent with previous works: knowledge-based, biometric, and multi-factor methods. The reviewed papers are categorized according to these groups, and the methods used are scrutinized. To the best of our knowledge, this systematic literature review is the first that provides a comprehensive consolidation of immersive technologies for user authentication in virtual, augmented, and mixed reality.
{"title":"User Authentication Mechanisms Based on Immersive Technologies: A Systematic Review","authors":"Ioanna Anastasaki, George Drosatos, George Pavlidis, Konstantinos Rantos","doi":"10.3390/info14100538","DOIUrl":"https://doi.org/10.3390/info14100538","url":null,"abstract":"Immersive technologies are revolutionary technological advancements that offer users unparalleled experiences of immersion in a virtual or mixed world of virtual and real elements. In such technology, user privacy, security, and anonymity are paramount, as users often share private and sensitive information. Therefore, user authentication is a critical requirement in these environments. This paper presents a systematic literature review of recently published research papers on immersive technology-based user authentication mechanisms. After conducting the literature search in September 2023 using Scopus, the selection process identified 36 research publications that were further analyzed. The analysis revealed three major types of authentications related to immersive technologies, consistent with previous works: knowledge-based, biometric, and multi-factor methods. The reviewed papers are categorized according to these groups, and the methods used are scrutinized. To the best of our knowledge, this systematic literature review is the first that provides a comprehensive consolidation of immersive technologies for user authentication in virtual, augmented, and mixed reality.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135831361","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}
Farzad Nikfam, Raffaele Casaburi, Alberto Marchisio, Maurizio Martina, Muhammad Shafique
Machine learning (ML) is widely used today, especially through deep neural networks (DNNs); however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks has emerged called spiking neural networks (SNNs), which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting them. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 and AlexNet models, widely-used convolutional architectures, are used for both DNN and SNN models based on their respective architectures, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time steps.
{"title":"A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks","authors":"Farzad Nikfam, Raffaele Casaburi, Alberto Marchisio, Maurizio Martina, Muhammad Shafique","doi":"10.3390/info14100537","DOIUrl":"https://doi.org/10.3390/info14100537","url":null,"abstract":"Machine learning (ML) is widely used today, especially through deep neural networks (DNNs); however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks has emerged called spiking neural networks (SNNs), which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting them. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 and AlexNet models, widely-used convolutional architectures, are used for both DNN and SNN models based on their respective architectures, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time steps.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135458740","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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