In this paper, we propose a multi-beam integrated radar and communication scheme using phased-array antenna, in which the same LFM-BPSK integrated waveform is used for both the radar and the communication beams. In the integrated beam design, the radar beam is periodically scanned in different directions for detection, and the communication beam is periodically manipulated in one direction for communication. The system’s beamforming uses adaptive beamforming technology to achieve radar echoes and communication reception. For the LFM-BPSK integrated waveform used by the system, we propose a method for estimating parameters during communication reception. Through simulation, the proposed beam-pattern design, adaptive beamforming, and parameter estimation scheme can achieve radar and communication functions using phased-array antennas.
{"title":"Multi-Beam Radar Communication Integrated System Design","authors":"Hao Ma, Jun Wang, Xin Sun, Wenxin Jin","doi":"10.3390/info14100566","DOIUrl":"https://doi.org/10.3390/info14100566","url":null,"abstract":"In this paper, we propose a multi-beam integrated radar and communication scheme using phased-array antenna, in which the same LFM-BPSK integrated waveform is used for both the radar and the communication beams. In the integrated beam design, the radar beam is periodically scanned in different directions for detection, and the communication beam is periodically manipulated in one direction for communication. The system’s beamforming uses adaptive beamforming technology to achieve radar echoes and communication reception. For the LFM-BPSK integrated waveform used by the system, we propose a method for estimating parameters during communication reception. Through simulation, the proposed beam-pattern design, adaptive beamforming, and parameter estimation scheme can achieve radar and communication functions using phased-array antennas.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767021","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}
Timotej Jagrič, Daniel Zdolšek, Robert Horvat, Iztok Kolar, Niko Erker, Jernej Merhar, Vita Jagrič
Financial innovation, green investments, or climate change are changing insurers’ business ecosystems, impacting their business behaviour and financial vulnerability. Supervisors and other stakeholders are interested in identifying the path toward deterioration in the insurance company’s financial health as early as possible. Suptech tools enable them to discover more and to intervene in a timely manner. We propose an artificial intelligence approach using Kohonen’s self-organizing maps. The dataset used for development and testing included yearly financial statements with 4058 observations for European composite insurance companies from 2012 to 2021. In a novel manner, the model investigates the behaviour of insurers, looking for similarities. The model forms a map. For the obtained groupings of companies from different geographical origins, a common characteristic was discovered regarding their future financial deterioration. A threshold defined using the solvency capital requirement (SCR) ratio being below 130% for the next year is applied to the map. On the test sample, the model correctly identified on average 86% of problematic companies and 79% of unproblematic companies. Changing the SCR ratio level enables differentiation into multiple map sections. The model does not rely on traditional methods, or the use of the SCR ratio as a dependent variable but looks for similarities in the actual insurer’s financial behaviour. The proposed approach offers grounds for a Suptech tool of predictive generation to support early detection of the possible future financial distress of an insurance company.
{"title":"New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market","authors":"Timotej Jagrič, Daniel Zdolšek, Robert Horvat, Iztok Kolar, Niko Erker, Jernej Merhar, Vita Jagrič","doi":"10.3390/info14100565","DOIUrl":"https://doi.org/10.3390/info14100565","url":null,"abstract":"Financial innovation, green investments, or climate change are changing insurers’ business ecosystems, impacting their business behaviour and financial vulnerability. Supervisors and other stakeholders are interested in identifying the path toward deterioration in the insurance company’s financial health as early as possible. Suptech tools enable them to discover more and to intervene in a timely manner. We propose an artificial intelligence approach using Kohonen’s self-organizing maps. The dataset used for development and testing included yearly financial statements with 4058 observations for European composite insurance companies from 2012 to 2021. In a novel manner, the model investigates the behaviour of insurers, looking for similarities. The model forms a map. For the obtained groupings of companies from different geographical origins, a common characteristic was discovered regarding their future financial deterioration. A threshold defined using the solvency capital requirement (SCR) ratio being below 130% for the next year is applied to the map. On the test sample, the model correctly identified on average 86% of problematic companies and 79% of unproblematic companies. Changing the SCR ratio level enables differentiation into multiple map sections. The model does not rely on traditional methods, or the use of the SCR ratio as a dependent variable but looks for similarities in the actual insurer’s financial behaviour. The proposed approach offers grounds for a Suptech tool of predictive generation to support early detection of the possible future financial distress of an insurance company.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766153","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}
Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.
{"title":"Neural Network Applications in Polygraph Scoring—A Scoping Review","authors":"Dana Rad, Nicolae Paraschiv, Csaba Kiss","doi":"10.3390/info14100564","DOIUrl":"https://doi.org/10.3390/info14100564","url":null,"abstract":"Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135854234","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}
Giseok Jeong, Kookjin Kim, Sukjoon Yoon, Dongkyoo Shin, Jiwon Kang
As the world undergoes rapid digitalization, individuals and objects are becoming more extensively connected through the advancement of Internet networks. This phenomenon has been observed in governmental and military domains as well, accompanied by a rise in cyber threats consequently. The United States (U.S.), in response to this, has been strongly urging its allies to adhere to the RMF standard to bolster the security of primary defense systems. An agreement has been signed between the Republic of Korea and the U.S. to collaboratively operate major defense systems and cooperate on cyber threats. However, the methodologies and tools required for RMF implementation have not yet been fully provided to several allied countries, including the Republic of Korea, causing difficulties in its implementation. In this study, the U.S. RMF process was applied to a specific system of the Republic of Korea Ministry of National Defense, and the outcomes were analyzed. Emphasis was placed on the initial two stages of the RMF: ‘system categorization’ and ‘security control selection’, presenting actual application cases. Additionally, a detailed description of the methodology used by the Republic of Korea Ministry of National Defense for RMF implementation in defense systems is provided, introducing a keyword-based overlay application methodology. An introduction to the K-RMF Baseline, Overlay, and Tailoring Tool is also given. The methodologies and tools presented are expected to serve as valuable references for ally countries, including the U.S., in effectively implementing the RMF. It is anticipated that the results of this research will contribute to enhancing cyber security and threat management among allies.
{"title":"Exploring Effective Approaches to the Risk Management Framework (RMF) in the Republic of Korea: A Study","authors":"Giseok Jeong, Kookjin Kim, Sukjoon Yoon, Dongkyoo Shin, Jiwon Kang","doi":"10.3390/info14100561","DOIUrl":"https://doi.org/10.3390/info14100561","url":null,"abstract":"As the world undergoes rapid digitalization, individuals and objects are becoming more extensively connected through the advancement of Internet networks. This phenomenon has been observed in governmental and military domains as well, accompanied by a rise in cyber threats consequently. The United States (U.S.), in response to this, has been strongly urging its allies to adhere to the RMF standard to bolster the security of primary defense systems. An agreement has been signed between the Republic of Korea and the U.S. to collaboratively operate major defense systems and cooperate on cyber threats. However, the methodologies and tools required for RMF implementation have not yet been fully provided to several allied countries, including the Republic of Korea, causing difficulties in its implementation. In this study, the U.S. RMF process was applied to a specific system of the Republic of Korea Ministry of National Defense, and the outcomes were analyzed. Emphasis was placed on the initial two stages of the RMF: ‘system categorization’ and ‘security control selection’, presenting actual application cases. Additionally, a detailed description of the methodology used by the Republic of Korea Ministry of National Defense for RMF implementation in defense systems is provided, introducing a keyword-based overlay application methodology. An introduction to the K-RMF Baseline, Overlay, and Tailoring Tool is also given. The methodologies and tools presented are expected to serve as valuable references for ally countries, including the U.S., in effectively implementing the RMF. It is anticipated that the results of this research will contribute to enhancing cyber security and threat management among allies.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014474","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}
Rafael Navas-González, Óscar Oballe-Peinado, Julián Castellanos-Ramos, Daniel Rosas-Cervantes, José A. Sánchez-Durán
This work presents examples of practice sessions to teach and learn digital electronics using an FPGA-based development platform, accessible either through the on-campus laboratory or online using a remote laboratory developed by the authors. The main tasks proposed in the practice sessions are to design specific modules that will be included as a main block in more complex projects. Each project is adapted and ready once the student modules to be implemented, debugged, and/or tested in the FPGA-based platform are added using the aforementioned accessibility methods. The proposal suggests the use of a web-based remote laboratory to complement (rather than replace) on-campus teaching in response to the growing need for access to laboratory resources beyond regular teaching hours. The paper introduces the main topics on implementing and using the tool, sets out how to adapt regular projects to be executed in the remote lab, and describes several practice projects proposed to students in the final three academic years. The paper concludes with an analysis and evaluation of the user experience taken from surveys conducted with students at the end of the semester.
{"title":"Practice Projects for an FPGA-Based Remote Laboratory to Teach and Learn Digital Electronics","authors":"Rafael Navas-González, Óscar Oballe-Peinado, Julián Castellanos-Ramos, Daniel Rosas-Cervantes, José A. Sánchez-Durán","doi":"10.3390/info14100558","DOIUrl":"https://doi.org/10.3390/info14100558","url":null,"abstract":"This work presents examples of practice sessions to teach and learn digital electronics using an FPGA-based development platform, accessible either through the on-campus laboratory or online using a remote laboratory developed by the authors. The main tasks proposed in the practice sessions are to design specific modules that will be included as a main block in more complex projects. Each project is adapted and ready once the student modules to be implemented, debugged, and/or tested in the FPGA-based platform are added using the aforementioned accessibility methods. The proposal suggests the use of a web-based remote laboratory to complement (rather than replace) on-campus teaching in response to the growing need for access to laboratory resources beyond regular teaching hours. The paper introduces the main topics on implementing and using the tool, sets out how to adapt regular projects to be executed in the remote lab, and describes several practice projects proposed to students in the final three academic years. The paper concludes with an analysis and evaluation of the user experience taken from surveys conducted with students at the end of the semester.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969643","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}
Stemming from the overlap of objects and undertraining due to few samples, road dense object detection is confronted with poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects on the road has been proposed. Firstly, the Darknet-53 network structure is adopted to obtain a pre-trained YOLOv3 model. Then, the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles. In the proposed model, one random function is adapted to initialize and optimize the weights of the transfer training model, which is separately designed from the pre-trained YOLOv3. The object detection classifier replaces the fully connected layer, which further improves the detection effect. The reduced size of the network model can further reduce the training and detection time. As a result, it can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the YOLOv5 by 4% and 0.59%, respectively. Additionally, the test was carried out using UA-DETRAC, a public road vehicle detection dataset. The object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing new object detection algorithm YOLOv7, the speed is 1.5 Fps/s faster. The proposed YOLOv3 performs 67.36 Bn of floating point operations per second in detecting video, which is obviously less than the traditional YOLOv3 and the newer object detection algorithm YOLOv5.
{"title":"Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection","authors":"Chunhua Zhu, Jiarui Liang, Fei Zhou","doi":"10.3390/info14100560","DOIUrl":"https://doi.org/10.3390/info14100560","url":null,"abstract":"Stemming from the overlap of objects and undertraining due to few samples, road dense object detection is confronted with poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects on the road has been proposed. Firstly, the Darknet-53 network structure is adopted to obtain a pre-trained YOLOv3 model. Then, the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles. In the proposed model, one random function is adapted to initialize and optimize the weights of the transfer training model, which is separately designed from the pre-trained YOLOv3. The object detection classifier replaces the fully connected layer, which further improves the detection effect. The reduced size of the network model can further reduce the training and detection time. As a result, it can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the YOLOv5 by 4% and 0.59%, respectively. Additionally, the test was carried out using UA-DETRAC, a public road vehicle detection dataset. The object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing new object detection algorithm YOLOv7, the speed is 1.5 Fps/s faster. The proposed YOLOv3 performs 67.36 Bn of floating point operations per second in detecting video, which is obviously less than the traditional YOLOv3 and the newer object detection algorithm YOLOv5.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014258","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 growth of structured, semi-structured, and unstructured data produced by the new applications is a result of the development and expansion of social networks, the Internet of Things, web technology, mobile devices, and other technologies. However, as traditional databases became less suitable to manage the rapidly growing quantity of data and variety of data structures, a new class of database management systems named NoSQL was required to satisfy the new requirements. Although NoSQL databases are generally schema-less, significant research has been conducted on their design. A literature review presented in this paper lets us claim the need to create modeling techniques to describe how to structure data in NoSQL databases. Key-value is one of the NoSQL families that has received too little attention, especially in terms of its design methodology. Most studies have focused on the other families, like column-oriented and document-oriented. This paper aims to present a design approach named KVMod (key-value modeling) specific to key-value databases. The purpose is to provide to the scientific community and engineers with a methodology for the design of key-value stores using the maximum automation and therefore the minimum human intervention, which equals the minimum number of errors. A software tool called KVDesign has been implemented to automate the proposed methodology and, thus, the most time-consuming database modeling tasks. The complexity is also discussed to assess the efficiency of our proposed algorithms.
{"title":"KVMod—A Novel Approach to Design Key-Value NoSQL Databases","authors":"Ahmed Dourhri, Mohamed Hanine, Hassan Ouahmane","doi":"10.3390/info14100563","DOIUrl":"https://doi.org/10.3390/info14100563","url":null,"abstract":"The growth of structured, semi-structured, and unstructured data produced by the new applications is a result of the development and expansion of social networks, the Internet of Things, web technology, mobile devices, and other technologies. However, as traditional databases became less suitable to manage the rapidly growing quantity of data and variety of data structures, a new class of database management systems named NoSQL was required to satisfy the new requirements. Although NoSQL databases are generally schema-less, significant research has been conducted on their design. A literature review presented in this paper lets us claim the need to create modeling techniques to describe how to structure data in NoSQL databases. Key-value is one of the NoSQL families that has received too little attention, especially in terms of its design methodology. Most studies have focused on the other families, like column-oriented and document-oriented. This paper aims to present a design approach named KVMod (key-value modeling) specific to key-value databases. The purpose is to provide to the scientific community and engineers with a methodology for the design of key-value stores using the maximum automation and therefore the minimum human intervention, which equals the minimum number of errors. A software tool called KVDesign has been implemented to automate the proposed methodology and, thus, the most time-consuming database modeling tasks. The complexity is also discussed to assess the efficiency of our proposed algorithms.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968659","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}
Abderahman Rejeb, Karim Rejeb, Steve Simske, John G. Keogh
Blockchain technology has emerged as a tool with the potential to enhance transparency, trust, security, and decentralization in supply chain management (SCM). This study presents a comprehensive review of the interplay between blockchain technology and SCM. By analyzing an extensive dataset of 943 articles, our exploration utilizes the Latent Dirichlet Allocation (LDA) method to delve deep into the thematic structure of the discourse. This investigation revealed ten central topics ranging from blockchain’s transformative role in supply chain finance and e-commerce operations to its application in specialized areas, such as the halal food supply chain and humanitarian contexts. Particularly pronounced were discussions on the challenges and transformations of blockchain integration in supply chains and its impact on pricing strategies and decision-making. Visualization tools, including PyLDAvis, further illuminated the interconnectedness of these themes, highlighting the intertwined nature of blockchain adoption challenges with aspects such as traceability and pricing. Despite the breadth of topics covered, the paper acknowledges its limitations due to the fast-evolving nature of blockchain developments during and after our analysis period. Ultimately, this review provides a holistic academic snapshot, emphasizing both well-developed and nascent research areas and guiding future research in the evolving domain of blockchain in SCM.
{"title":"Exploring Blockchain Research in Supply Chain Management: A Latent Dirichlet Allocation-Driven Systematic Review","authors":"Abderahman Rejeb, Karim Rejeb, Steve Simske, John G. Keogh","doi":"10.3390/info14100557","DOIUrl":"https://doi.org/10.3390/info14100557","url":null,"abstract":"Blockchain technology has emerged as a tool with the potential to enhance transparency, trust, security, and decentralization in supply chain management (SCM). This study presents a comprehensive review of the interplay between blockchain technology and SCM. By analyzing an extensive dataset of 943 articles, our exploration utilizes the Latent Dirichlet Allocation (LDA) method to delve deep into the thematic structure of the discourse. This investigation revealed ten central topics ranging from blockchain’s transformative role in supply chain finance and e-commerce operations to its application in specialized areas, such as the halal food supply chain and humanitarian contexts. Particularly pronounced were discussions on the challenges and transformations of blockchain integration in supply chains and its impact on pricing strategies and decision-making. Visualization tools, including PyLDAvis, further illuminated the interconnectedness of these themes, highlighting the intertwined nature of blockchain adoption challenges with aspects such as traceability and pricing. Despite the breadth of topics covered, the paper acknowledges its limitations due to the fast-evolving nature of blockchain developments during and after our analysis period. Ultimately, this review provides a holistic academic snapshot, emphasizing both well-developed and nascent research areas and guiding future research in the evolving domain of blockchain in SCM.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968948","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}
Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a formidable challenge, especially amidst constrained participant numbers. The challenge escalates when dealing with small datasets, a common scenario in preliminary medical research. While recent advances have ushered in few-shot learning algorithms adept at handling sparse data, this paper pioneers a distinctive methodology involving a visualization-centric approach to navigating the small-data challenge in diagnosing stroke survivors based on gait-analysis-derived biomechanical data. Employing Siamese neural networks (SNNs), our method transforms a biomechanical time series into visually intuitive images, facilitating a unique analytical lens. The kinematic data encapsulated comprise a spectrum of gait metrics, including movements of the ankle, knee, hip, and center of mass in three dimensions for both paretic and non-paretic legs. Following the visual transformation, the SNN serves as a potent feature extractor, mapping the data into a high-dimensional feature space conducive to classification. The extracted features are subsequently fed into various machine learning (ML) models like support vector machines (SVMs), Random Forest (RF), or neural networks (NN) for classification. In pursuit of heightened interpretability, a cornerstone in medical AI applications, we employ the Grad-CAM (Class Activation Map) tool to visually highlight the critical regions influencing the model’s decision. Our methodology, though exploratory, showcases a promising avenue for leveraging visualized biomechanical data in stroke diagnosis, achieving a perfect classification rate in our preliminary dataset. The visual inspection of generated images elucidates a clear separation of classes (100%), underscoring the potential of this visualization-driven approach in the realm of small data. This proof-of-concept study accentuates the novelty of visual data transformation in enhancing both interpretability and performance in stroke diagnosis using limited data, laying a robust foundation for future research in larger-scale evaluations.
{"title":"Innovative Visualization Approach for Biomechanical Time Series in Stroke Diagnosis Using Explainable Machine Learning Methods: A Proof-of-Concept Study","authors":"Kyriakos Apostolidis, Christos Kokkotis, Evangelos Karakasis, Evangeli Karampina, Serafeim Moustakidis, Dimitrios Menychtas, Georgios Giarmatzis, Dimitrios Tsiptsios, Konstantinos Vadikolias, Nikolaos Aggelousis","doi":"10.3390/info14100559","DOIUrl":"https://doi.org/10.3390/info14100559","url":null,"abstract":"Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a formidable challenge, especially amidst constrained participant numbers. The challenge escalates when dealing with small datasets, a common scenario in preliminary medical research. While recent advances have ushered in few-shot learning algorithms adept at handling sparse data, this paper pioneers a distinctive methodology involving a visualization-centric approach to navigating the small-data challenge in diagnosing stroke survivors based on gait-analysis-derived biomechanical data. Employing Siamese neural networks (SNNs), our method transforms a biomechanical time series into visually intuitive images, facilitating a unique analytical lens. The kinematic data encapsulated comprise a spectrum of gait metrics, including movements of the ankle, knee, hip, and center of mass in three dimensions for both paretic and non-paretic legs. Following the visual transformation, the SNN serves as a potent feature extractor, mapping the data into a high-dimensional feature space conducive to classification. The extracted features are subsequently fed into various machine learning (ML) models like support vector machines (SVMs), Random Forest (RF), or neural networks (NN) for classification. In pursuit of heightened interpretability, a cornerstone in medical AI applications, we employ the Grad-CAM (Class Activation Map) tool to visually highlight the critical regions influencing the model’s decision. Our methodology, though exploratory, showcases a promising avenue for leveraging visualized biomechanical data in stroke diagnosis, achieving a perfect classification rate in our preliminary dataset. The visual inspection of generated images elucidates a clear separation of classes (100%), underscoring the potential of this visualization-driven approach in the realm of small data. This proof-of-concept study accentuates the novelty of visual data transformation in enhancing both interpretability and performance in stroke diagnosis using limited data, laying a robust foundation for future research in larger-scale evaluations.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013520","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}
Inspired by the migration and reproduction of species in nature to explore suitable habitats, this paper proposed a new swarm intelligence algorithm called the Migration and Reproduction Algorithm (MARA). This new algorithm discusses how to transform the behavior of an organism looking for a suitable habitat into a mathematical model, which can solve optimization problems. MARA has some common features with other optimization methods such as particle swarm optimization (PSO) and the fireworks algorithm (FWA), which means MARA can also solve the optimization problems that PSO and FWA are used to, namely, high-dimensional optimization problems. MARA also has some unique features among biology-based optimization methods. In this paper, we articulated the structure of MARA by correlating it with natural biogeography; then, we demonstrated the performance of MARA on sets of 12 benchmark functions. In the end, we applied it to optimize a practical problem of power dispatching in a multi-microgrid system that proved it has certain value in practical applications.
{"title":"A New Migration and Reproduction Intelligence Algorithm: Case Study in Cloud-Based Microgrid","authors":"Renwu Yan, Yunzhang Liu, Ning Yu","doi":"10.3390/info14100562","DOIUrl":"https://doi.org/10.3390/info14100562","url":null,"abstract":"Inspired by the migration and reproduction of species in nature to explore suitable habitats, this paper proposed a new swarm intelligence algorithm called the Migration and Reproduction Algorithm (MARA). This new algorithm discusses how to transform the behavior of an organism looking for a suitable habitat into a mathematical model, which can solve optimization problems. MARA has some common features with other optimization methods such as particle swarm optimization (PSO) and the fireworks algorithm (FWA), which means MARA can also solve the optimization problems that PSO and FWA are used to, namely, high-dimensional optimization problems. MARA also has some unique features among biology-based optimization methods. In this paper, we articulated the structure of MARA by correlating it with natural biogeography; then, we demonstrated the performance of MARA on sets of 12 benchmark functions. In the end, we applied it to optimize a practical problem of power dispatching in a multi-microgrid system that proved it has certain value in practical applications.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968673","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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