2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)最新文献

{"title":"Primary school teachers’ attitudes towards digital educational games: Preliminary findings from the Multiplication Game evaluation","authors":"Angeliki Leonardou, Maria Rigou, J. Garofalakis","doi":"10.1109/IISA52424.2021.9555513","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555513","url":null,"abstract":"The COVID-19 pandemic among other radical changes it imposed onto our typical way of living, working, and interacting, established online education in both synchronous and asynchronous forms. This in many cases has given alternative forms of education like digital games, more practical potential. Teachers during this period have proposed digital games as a part of the teaching and learning procedure more often. The digital games that managed to prove their value in more effective learning will probably gain a place in the classroom and teachers’ role is significant towards this change. This study presents the results of an online survey with the participation of 184 primary school teachers in Greece. The purpose of the survey is to investigate teachers’ attitudes towards digital games in general and the Multiplication Game (MG) in particular and the findings are positive as teachers’ opinion for both MG and digital games is high.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114994006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Covid-19 Mobile Tracking Application Utilizing Smart Sensors","authors":"Efthymios Alepis, M. Virvou, Polychronis Kontomaris","doi":"10.1109/IISA52424.2021.9555548","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555548","url":null,"abstract":"Covid-19 Pandemic continues to spread and a lot of techniques have already been used to help confine it. One of the measures, that has been proven of some efficiency is the use of mobile tracking apps. These apps are addressing the unmet needs of healthcare and public health system, including contact tracing, health information dissemination, symptom checking and providing tools for training healthcare providers. In this paper we are presenting an application that focuses on basic smartphone’s sensors in order to provide information to users about areas of high risk and their corresponding heatmaps and an indoor positioning system using Bluetooth and Wi-Fi, as a form of contact tracing for covid patients.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124908332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Bone Metastasis Localisation in Nuclear Imaging data of Breast Cancer Patients","authors":"S. Moustakidis, A. Siouras, Nikolaos I. Papandrianos, C. Ntakolia, E. Papageorgiou","doi":"10.1109/IISA52424.2021.9555561","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555561","url":null,"abstract":"Bone scintigraphy is a popular method for the diagnosis of bone metastasis that typically occurs when cancer cells from the primary tumor relocate to the bone. In bone scintigraphy, the whole patient’s body is scanned and the generated bone scan visualization provides a valuable source of information for the evaluation of various bone-related pathologies, including bone inflammation and fractures, nonmalignant bone lesions, bone infections, or even the spread of cancer to the bone. ?n particular, bone cancer is among the most frequently appeared diseases to patients suffering from metastatic cancer such as breast cancer patients. However, hot spots in bone scans indicating inflammations or cancer metastasis can be misleading. Accurate detection of pathological hot spots can be a very challenging procedure, with the experience of clinicians playing a critical role in the interpretation of the images. Artificial intelligence has emerged as a key enabler in the interpretation of medical imaging being able to model the aforementioned uncertainties and providing a reliable automated solution. So far, a number of convolutional neural networks (CNN)-based techniques have been proposed in the recent literature coping with the problem of bone metastasis classification. To the best of our knowledge, localization of pathological and degenerative hot spots in scintigraphy images is a scientific area that has not been explored. This paper contributes to the first ever deployment of advanced deep learning networks for bone metastasis localization in nuclear imaging data of breast cancer patients. The methodology relies on the latest advances of object detection via the use of two powerful and recent models (scaled YOLO v4 and Detectron2). The efficacy of the proposed methodology was demonstrated utilizing an extensive experimentation setup. The proposed methodology demonstrates unique potential in bone metastasis localization therefore facilitating the clinical interpretation of bone scintigraphy scans.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123704931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection","authors":"Sunil Rao, V. Narayanaswamy, Michael Esposito, Jayaraman J. Thiagarajan, A. Spanias","doi":"10.1109/IISA52424.2021.9555564","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555564","url":null,"abstract":"As the COVID-19 pandemic continues, rapid non-invasive testing has become essential. Recent studies and benchmarks motivates the use of modern artificial intelligence (AI) tools that utilize audio waveform spectral features of coughing for COVID-19 diagnosis. In this paper, we describe the system we developed for COVID-19 cough detection. We utilize features directly extracted from the coughing audio and use deep learning algorithms to develop automated diagnostic tools for COVID-19. In particular, we develop a unique modification of the VGG13 deep learning architecture for audio analysis that uses log-mel spectrograms and a combination of binary cross entropy and focal losses. This unique modification enabled the model to achieve highly robust classification of the DiCOVA 2021 COVID-19 data. We also explore the use of data augmentation and an ensembling strategy to further improve the performance on the validation and the blind test datasets. Our model achieved an average validation AUROC of 82.23% and a test AUROC of 78.3% at a sensitivity of 80.49%.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122748838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Five-Factor Musical Preference Prediction for Solving New User Cold-Start Problem in Content-Based Music Recommender System","authors":"Keisuke Okada, Tan Phan-Xuan, E. Kamioka","doi":"10.1109/IISA52424.2021.9555546","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555546","url":null,"abstract":"Recent years witness a boom in music recommender systems due to the success of online streaming services. Even though such systems have brought relatively high-quality recommendations to the users, they are still facing the cold-start problem, especially for new user case. This problem happens when the system does not have information about the new user’s preferences to provide recommendations. Therefore, effectively predicting musical preferences for the new user becomes vital. In this paper, we leverage a five-factor MUSIC model which is characterized by Mellow, Unpretentious, Sophisticated, Intense, and Contemporary to represent the user’s preference. Then, towards solving the new user cold-start problems in the content-based music recommender system, we propose a method to predict the five-factor preference profile of the novel user. We consider an early-stage scenario when there are no and few rating data of the user available in the system. Accordingly, we first use the information of age and brain type extracted from questionnaires to build regression models. These models are used to predict the first five-factor musical preference profile for initial recommendations. We then estimate the second five-factor profile based on the user’s rating data and linearly combine it with the first profile for improving recommendations. The results demonstrated the effectiveness of the proposed method in predicting the musical preference of the new user in the assumed scenario.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122945755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Medical Big-Data collected using IoT Devices","authors":"Eirini Zoumi, Emmanouil Skondras, A. Michalas, D. Vergados","doi":"10.1109/IISA52424.2021.9555504","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555504","url":null,"abstract":"Modern medical systems manipulate a large number of clinical data collected using Internet of Things (IoT) devices. In this environment 5G network architectures provide low latency communication in order to support the strict constraints of real time medical services. Furthermore, the collected data need to be classified so their retrieval and manipulation to be immediate. This paper focuses on collecting big data from three types of sensors, namely body sensors, indoor and outdoor sensors. The collected data are stored in a Cloud infrastructure. A classification algorithm is proposed. For each type of sensor the algorithm classifies the received data into classes, to efficiently organize them into cluster of Virtual Machines (VMs). Specifically, taking into consideration the alternative solutions available in the literature, the proposed algorithm manipulates heterogeneous, large quantities of data and stores them in the cloud so that they can be retrieved in a short time, which is an important factor in cases where health data are involved.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124729860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Analysis of a New SDM Submarine Optical Network for Greece","authors":"Charalampos Papapavlou, Konstantinos Paximadis, Giannis Tzimas","doi":"10.1109/IISA52424.2021.9555537","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555537","url":null,"abstract":"Submarine networks are the main interconnecting infrastructure between all continents. As so, they transport an enormous volume of traffic being an active part of the global backbone network topology. As Space Division Multiplexing (SDM) promises to provide the so wanted extra bandwidth in optical communications, its potentials regarding submarine networks have to be investigated. Apart from connecting far away located continents or regions, submarine networks can be also utilized as a backup to terrestrial networks especially in closed seas or long coastlines. In this paper we discuss all new technologies applied in optical submarine networks and we design an all new SDM-based submarine optical network for Greece. The proposed design is based on well known redundancy and performance objectives applied in backbone networks. Moreover, we extend in the economics region and conduct a detailed cost analysis of the proposed design. The economic analysis enables us to spot on design issues in which costs can be significantly reduced.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123445424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Renewable Energy Sources and Impact on GDP Growth","authors":"Maria D. Karasimou, Olga Mousiari, L. Tsoukalas","doi":"10.1109/IISA52424.2021.9555574","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555574","url":null,"abstract":"Iinvestments in renewable energy sources (RES) appear to boost GDP growth especially in middle-income countries. By investing in RES, countries meet commitments for Net-Zero by 2050 and accelerate growth in ways that produce broader benefits to an economy. In Greece, the primary energy from RES doubled during the decade 2006-2016 thus contributing to a growing share of RES in the production of electricity. RES’ contribution tripled as a percentage of the total electricity produced. Using statistical tools, the relation of RES to GDP during this period points to positive associations between RES and important macro-economic variables and reveals easurable impact on overall GDP growth.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114069522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-based Prediction-as-a-Service Model for Estimating Machine Bearing Health Status in Industry 4.0 5G Applications","authors":"Dimitrios Batistakis, Apostolos Xenakis, Georgios Papastergiou, P. Chatzimisios, V. Gerogiannis","doi":"10.1109/IISA52424.2021.9555538","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555538","url":null,"abstract":"Intelligent Machine Condition Monitoring (MCM) and Prediction for machine bearings is very important for efficient Industrial 5G applications. Common fault diagnosis and other classification methods usually extract time domain and frequency features or try to decrease noise from raw time sensory data. Afterwards, features are sought in time domain and statistical classifiers can be applied do the diagnosis. However, these methods suffer from expertise of feature selection and robustness in real time condition monitoring. In this paper, we present a prediction-as-a-service model for estimating machine bearing health status in industry 4.0 5G applications based on Deep Neural Networks (DNN). The proposed model constructs 3D grayscale images from raw time series data and performs prediction more efficiently. The paper also presents testing and evaluation of the model’s prediction and categorization capacity.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132232410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of validity indices on estimating the optimal number of clusters","authors":"Aikaterini Karanikola, C. M. Liapis, S. Kotsiantis","doi":"10.1109/IISA52424.2021.9555497","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555497","url":null,"abstract":"In clustering, finding the optimal number of clusters is usually one of the most crucial steps in the whole partitioning process. The decision about the optimal number of clusters, however, is not easy to make. In addition, the term ”optimal” is rather vague. In general, determining the optimal number of clusters is directly dependent on the method used to measure similarities and the parameter selection of the partition method. Moreover, certain inherent characteristics of the datasets, such as clusters that overlap with each other or clusters that contain subclusters, may, most often, increase the task’s level of difficulty. Given the above, in order to tackle the problem of estimating such an optimal in each distinct clustering case, different kind of indicators have over the years been proposed. In this study, a large number of such indicators, called validity indices, based on the approach of the so-called relative criteria, are examined comparatively. Specifically, a total of 26 validity indices are examined in two separate study cases: one in real-world and one in artificially generated data. Every index is utilized under the schemes of 9 different clustering methods which incorporate a total of 5 different distance metrics. The results are presented in various explanatory forms.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134161100","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}