Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284399
Aristeidis Mystakidis, Christos Tjortjis
This paper provides an analysis and proposes a methodology for predicting traffic congestion. Several machine learning algorithms and approaches are compared to select the most appropriate one. The methodology was implemented using Data Mining and Big Data techniques along with Python, SQL, and GIS technologies and was tested on data originating from one of the most problematic, regarding traffic congestion, streets in Thessaloniki, the 2nd most populated city in Greece. Evaluation and results have shown that data quality and size were the most critical factors towards algorithmic accuracy. Result comparison showed that Decision Trees were more accurate than Logistic Regression.
{"title":"Big Data Mining for Smart Cities: Predicting Traffic Congestion using Classification","authors":"Aristeidis Mystakidis, Christos Tjortjis","doi":"10.1109/IISA50023.2020.9284399","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284399","url":null,"abstract":"This paper provides an analysis and proposes a methodology for predicting traffic congestion. Several machine learning algorithms and approaches are compared to select the most appropriate one. The methodology was implemented using Data Mining and Big Data techniques along with Python, SQL, and GIS technologies and was tested on data originating from one of the most problematic, regarding traffic congestion, streets in Thessaloniki, the 2nd most populated city in Greece. Evaluation and results have shown that data quality and size were the most critical factors towards algorithmic accuracy. Result comparison showed that Decision Trees were more accurate than Logistic Regression.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130103201","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284410
Athanasios Christopoulos, Nikolaos Pellas
A significant body of literature documents the numerous benefits that Virtual (VR) and Augmented Reality (AR) supported interventions have brought to the educational scenery, especially with regards to the attainment of the underpinning objectives. At the same time, the vast evolution of Information and Communication Technology (ICT) has led to the emergence of a newly formed discipline, the so-called Learning Analytics (LA), which suggests the collection of big data’ for the assessment and the evaluation of the educational practices. However, by examining the relevant literature it became apparent that the attempts to blend these topics are limited. Motivated by this shortcoming, we propose a theoretical framework which accounts the elements that influence the educational processes and the unique features that immersive technologies have. On the grounds of this framework, an effort will be made to design and develop a universal LA system which will support the conduct and evaluation of such interventions in different educational contexts and scientific fields.
{"title":"Theoretical Foundations of Virtual and Augmented Reality-Supported Learning Analytics","authors":"Athanasios Christopoulos, Nikolaos Pellas","doi":"10.1109/IISA50023.2020.9284410","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284410","url":null,"abstract":"A significant body of literature documents the numerous benefits that Virtual (VR) and Augmented Reality (AR) supported interventions have brought to the educational scenery, especially with regards to the attainment of the underpinning objectives. At the same time, the vast evolution of Information and Communication Technology (ICT) has led to the emergence of a newly formed discipline, the so-called Learning Analytics (LA), which suggests the collection of big data’ for the assessment and the evaluation of the educational practices. However, by examining the relevant literature it became apparent that the attempts to blend these topics are limited. Motivated by this shortcoming, we propose a theoretical framework which accounts the elements that influence the educational processes and the unique features that immersive technologies have. On the grounds of this framework, an effort will be made to design and develop a universal LA system which will support the conduct and evaluation of such interventions in different educational contexts and scientific fields.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130190907","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284381
K. Kalovrektis, Apostolos Xenakis, A. Gotsinas, Ioannis (John) Korinthios, G. Stamoulis
Oxygen levels and heart rate joint monitoring is based on specialized, and most of the times, expensive oximetry devices. According to literature, many studies highlight the necessity of connecting oximetry devises, under well-known wireless protocols, such as Bluetooth, Wifi, ZigBee and others, for more efficient real time monitoring of human bio signals. However, most of the studies indicate a gap regarding an energy efficient and cost - effective end to end system for wireless monitoring of oxygen and heart rates, in IoT medical applications. To this end, our work, focuses on the design of a wireless oximeter device that bases its operation on the integrated and power efficient MAX30100 circuitry, and a customized lightweight ZigBee – based protocol for wireless bio signals transfer.
{"title":"802.15.4 - based Efficient Wireless Sensor System Design for Monitoring Blood Oxygen and Heart Rate in IoT Medical Applications","authors":"K. Kalovrektis, Apostolos Xenakis, A. Gotsinas, Ioannis (John) Korinthios, G. Stamoulis","doi":"10.1109/IISA50023.2020.9284381","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284381","url":null,"abstract":"Oxygen levels and heart rate joint monitoring is based on specialized, and most of the times, expensive oximetry devices. According to literature, many studies highlight the necessity of connecting oximetry devises, under well-known wireless protocols, such as Bluetooth, Wifi, ZigBee and others, for more efficient real time monitoring of human bio signals. However, most of the studies indicate a gap regarding an energy efficient and cost - effective end to end system for wireless monitoring of oxygen and heart rates, in IoT medical applications. To this end, our work, focuses on the design of a wireless oximeter device that bases its operation on the integrated and power efficient MAX30100 circuitry, and a customized lightweight ZigBee – based protocol for wireless bio signals transfer.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130232880","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}
Pub Date : 2020-07-15DOI: 10.1109/iisa50023.2020.9284398
{"title":"IISA 2020 Index","authors":"","doi":"10.1109/iisa50023.2020.9284398","DOIUrl":"https://doi.org/10.1109/iisa50023.2020.9284398","url":null,"abstract":"","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"44 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116538849","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284395
N. Spyropoulou, A. Kameas
This work investigates the role of educator in STE(A)M Education. The study compares the outcomes of previous research with the results of a survey on 59 Greek educators, who have implemented STE(A)M-related courses. Based to the responses through the specially designed closeended questionnaire, the educators’ perceptions are identified and analyzed. Based on these, we assessed the importance of different traits and competences that a STE(A)M educator should have. Furthermore, our research showed evidence that there are some differences on educators’ perceptions depending on their background, mostly regarding both teaching and professional development aspects.
{"title":"Investigating the role of STE(A)M Educators: a case study in Greece","authors":"N. Spyropoulou, A. Kameas","doi":"10.1109/IISA50023.2020.9284395","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284395","url":null,"abstract":"This work investigates the role of educator in STE(A)M Education. The study compares the outcomes of previous research with the results of a survey on 59 Greek educators, who have implemented STE(A)M-related courses. Based to the responses through the specially designed closeended questionnaire, the educators’ perceptions are identified and analyzed. Based on these, we assessed the importance of different traits and competences that a STE(A)M educator should have. Furthermore, our research showed evidence that there are some differences on educators’ perceptions depending on their background, mostly regarding both teaching and professional development aspects.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124723620","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284371
{"title":"IISA 2020 Breaker Page","authors":"","doi":"10.1109/IISA50023.2020.9284371","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284371","url":null,"abstract":"","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128813086","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284345
P. Groumpos, Vassiliki Mpelogianni
Energy consumed by buildings represents a large part of the world’s total energy consumption with a total share of 40%. This is the reason why energy efficiency of buildings has become a very important scientific field. For the purpose of this paper a critical review of old and new methods of controlling the parts of a building’s automation and thus achieving energy savings are compared, analyzed and presented. The method of Fuzzy Cognitive Maps (FCM) and its significant impact on the improvement of the management of a building is being presented. FCMs is a new soft computing method which combine neural networks and Fuzzy Logic. They have been used with very promising results in many fields such as medicine, transportation, manufacturing agriculture, food industry and energy. In this paper the use of FCMs is exploited and specifically used in issues of energy efficiency of buildings. Obtained results, simulation and experimental, for case studies where FCMs were used in buildings, of residential and commercial use, in Southern Greece will be presented. Software tools based on the aforementioned applications will be briefly presented. In the near future these tools are going to be integrated in even more buildings thus giving us real data which can and will be used in future research for moving from high energy consumption to Net-Zero Energy Buildings (NZEB).
{"title":"New Advanced Technology Methods for Energy Efficiency of Buildings","authors":"P. Groumpos, Vassiliki Mpelogianni","doi":"10.1109/IISA50023.2020.9284345","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284345","url":null,"abstract":"Energy consumed by buildings represents a large part of the world’s total energy consumption with a total share of 40%. This is the reason why energy efficiency of buildings has become a very important scientific field. For the purpose of this paper a critical review of old and new methods of controlling the parts of a building’s automation and thus achieving energy savings are compared, analyzed and presented. The method of Fuzzy Cognitive Maps (FCM) and its significant impact on the improvement of the management of a building is being presented. FCMs is a new soft computing method which combine neural networks and Fuzzy Logic. They have been used with very promising results in many fields such as medicine, transportation, manufacturing agriculture, food industry and energy. In this paper the use of FCMs is exploited and specifically used in issues of energy efficiency of buildings. Obtained results, simulation and experimental, for case studies where FCMs were used in buildings, of residential and commercial use, in Southern Greece will be presented. Software tools based on the aforementioned applications will be briefly presented. In the near future these tools are going to be integrated in even more buildings thus giving us real data which can and will be used in future research for moving from high energy consumption to Net-Zero Energy Buildings (NZEB).","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127825651","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284402
A. Spanias
This paper accompanies the keynote speech at IISA2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These programs operate under the auspices of the Sensor Signal and Information Processing (SenSIP) center which is also an Industry University Cooperative Research Center (I/UCRC) sponsored by the National Science Foundation (NSF) and I/UCRC industry members. The first program is an NSF REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a Research Experiences for Teachers (RET) program that started in June 2020. This program embeds teachers and community college faculty in SenSIP machine learning projects. Another state funded program in which SenSIP is a partner is MedTech ventures. Our partner MedTech works on training medical technology students, entrepreneurs and engineers to create smart medical solutions for preventive healthcare. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.
{"title":"Machine Learning Workforce Development Programs on Health and COVID-19 Research","authors":"A. Spanias","doi":"10.1109/IISA50023.2020.9284402","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284402","url":null,"abstract":"This paper accompanies the keynote speech at IISA2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These programs operate under the auspices of the Sensor Signal and Information Processing (SenSIP) center which is also an Industry University Cooperative Research Center (I/UCRC) sponsored by the National Science Foundation (NSF) and I/UCRC industry members. The first program is an NSF REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a Research Experiences for Teachers (RET) program that started in June 2020. This program embeds teachers and community college faculty in SenSIP machine learning projects. Another state funded program in which SenSIP is a partner is MedTech ventures. Our partner MedTech works on training medical technology students, entrepreneurs and engineers to create smart medical solutions for preventive healthcare. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"36 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120823316","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284372
Marc Jermaine Pontiveros, Geoffrey A. Solano, C. Tee, M. Tee
Systemic lupus erythematosus (SLE) is a type of autoimmune disease that affects multiple organ systems. The exact cause is unknown, but it is believed that predisposition to SLE is caused by multiple genetic factors. In this work we explored approaches to exploration and explanation of machine learning models for quantifying the risk of an individual to SLE using single nucleotide polymorphism (SNP) as features. Various model-agnostic explanation techniques were applied to further understand the factors that drive model predictions and allow comparison of the models. A web-based dashboard was developed to facilitate exploration and comparison of the models. The user can identify which features are important for predictions of each model, as well as to understand how a model comes up with a prediction for a given observation. The best performing model is the random forest model with AUC of 92.26% and AUCPR of 93.70g%.
{"title":"Explainable Machine Learning applied to Single-Nucleotide Polymorphisms for Systemic Lupus Erythematosus Prediction","authors":"Marc Jermaine Pontiveros, Geoffrey A. Solano, C. Tee, M. Tee","doi":"10.1109/IISA50023.2020.9284372","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284372","url":null,"abstract":"Systemic lupus erythematosus (SLE) is a type of autoimmune disease that affects multiple organ systems. The exact cause is unknown, but it is believed that predisposition to SLE is caused by multiple genetic factors. In this work we explored approaches to exploration and explanation of machine learning models for quantifying the risk of an individual to SLE using single nucleotide polymorphism (SNP) as features. Various model-agnostic explanation techniques were applied to further understand the factors that drive model predictions and allow comparison of the models. A web-based dashboard was developed to facilitate exploration and comparison of the models. The user can identify which features are important for predictions of each model, as well as to understand how a model comes up with a prediction for a given observation. The best performing model is the random forest model with AUC of 92.26% and AUCPR of 93.70g%.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121176527","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}
Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284389
Alexandros-Stavros S. Karabinakis, Georgios D. Styliaras, N. Avouris
This paper presents the implementation and evaluation of a web-based mobile application for supporting the communication and content storage needs of an excavation process by employing a spatial interface. Daily content-based operations in an excavation such as saving artifact-related photos, dimensions and notes are supported as well as the communication of archaeologists and the coordination of their daily tasks. The application is built over a solid content structure suitable for these operations and is executed in modern smartphones by exploiting fully their spatial display and interface capabilities. Related work is examined and compared to the presented application. Following, the design philosophy and implementation details of the application are presented along with evaluation results and usage scenarios.
{"title":"Excavations go mobile: A web-based mobile application for archaeological excavations","authors":"Alexandros-Stavros S. Karabinakis, Georgios D. Styliaras, N. Avouris","doi":"10.1109/IISA50023.2020.9284389","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284389","url":null,"abstract":"This paper presents the implementation and evaluation of a web-based mobile application for supporting the communication and content storage needs of an excavation process by employing a spatial interface. Daily content-based operations in an excavation such as saving artifact-related photos, dimensions and notes are supported as well as the communication of archaeologists and the coordination of their daily tasks. The application is built over a solid content structure suitable for these operations and is executed in modern smartphones by exploiting fully their spatial display and interface capabilities. Related work is examined and compared to the presented application. Following, the design philosophy and implementation details of the application are presented along with evaluation results and usage scenarios.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115363537","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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