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2019 World Congress on engineering, WCE 2019

作者： Vasilache, Simona The University of Tsukuba Graduate School of Systems and Information Engineering Dept. of Computer Science Japan

ISBN: (纸本)9789881404862

Multicultural instruction poses numerous challenges to educators and students alike. While these challenges have been traditionally recognized in the field of social sciences, they have been neglected by the natural sciences. We developed a pilot study aimed at highlighting the importance of cultural differences in science and engineering education. Our study presents engineering students' perceptions of these differences and how they affect the teaching and learning process. © 2019 Newswood Limited. All rights reserved.

关键词： Students

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models*

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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Data science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

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Services system between citizens and the E-government in the iraqi ministry of migration and displaced 3

Services system between citizens and the E-government in the...

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3rd International Conference on engineering Technology and its Applications, IICETA 2020

作者： Hayder, Israa M. Younis, Hussain A. Abed, Issa A. Younis, Hameed A. Qurna Technique Institute Dept. of Computer Systems Techniques Basrah Iraq College of Education for Women Basrah Iraq Engineering Technical College Southern Technical University Basrah Iraq College of Computer Science and Information Technology University of Basrah Basrah Iraq

ISBN: (纸本)9781728182315

In this paper, online application has been created using the Internet, which is a real means of communication between citizens and e-government. The main objectives of this application are: facilitate transactions;communication between the government and the citizen;reduce the problems and how to solve;know the real numbers of the displaced and the migrating. As the citizen can communicate with the Ministry of Migration and Displaced Persons then all the documents can be sent and received. It is also used to complete the information (modifying the names plus delete the change of places and exit) in the database system using the full name and number of the family ration card to enter the system. The user can send their request to the electronic public distribution system directly and the electronic center can take the answer and reply, the system or application is considered direct solutions for citizens and answer their inquiries, know where they are, where they move, update their numbers by the employees, modify, view and add database. The system gives the name of entry and the password of traffic. It can be seen by using Microsoft Office Access, Tomcats server, JSP, and HTML the results are very satisfactory and accurate statistics. © 2020 IEEE.

关键词： Windows operating system

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Healthier and Independent Living of the Elderly: Interoperability in a Cross-Project Pilot

Healthier and Independent Living of the Elderly: Interoperab...

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2022 Interoperability for Enterprise systems and Applications Workshops, I-ESA Workshops 2022

作者： Agostinho, Carlos Pimenta, Andreia Marques, Maria Tsiouris, Kostas M. Kalatzis, Fanis Nikitas, Christos Iliadou, Eleftheria Occhipinti, Mattia Kouris, Ioannis Koutsouris, Dimitrios Basdekis, Ioannis Koloutsou, Konstantina Gallo, Luigi de Pietro, Giueppe Jardim-Goncalves, Ricardo FCT-Campus Caparica2829-516 Portugal NOVA School of Science and Technology Caparica2829-516 Portugal Secretaria Regional de Saúde e Ptroteção Civil Rua da Carreira nº107 Funchal9000-042 Portugal Unit of Medical Technology and Intelligent Information Systems Dept. of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece National Kapodistrian University of Athens First Department of Otolaryngology-Head and Neck Surgery ‘Hippokrateio’ General Hospital Athens Greece Università degli Studi di Milano Computer Science Department Milan Italy Biomedical Engineering Laboratory Institute of Communication and Computer Systems National Technical University of Athens Heroon Polytechneiou 9 15780 Greece SPHYNX Technology Solutions AG Zug Switzerland National Research Council of Italy Via P. Bucci cubo 8/9C Rende87036 Italy

The ageing of the population creates new heterogeneous challenges for age-friendly living. The progressive decline in physical and cognitive skills tends to prevent elderly people from performing basic instrumental activities of daily living and there is a growing interest in technology for aging support. Digital health today can be exercised by anyone owning a smartphone and parameters such as heart rate, step counts, calorie intake, sleep quality, can be collected and used not only to monitor and improve the individual’s health condition but also to prevent illnesses. However, for the benefits of e-health to take place, digital health data, either Electronic Health Records (EHR) or sensor data from the IoMT, must be shared, maintaining privacy and security requirements but unlocking the potential for research an innovation throughout EU. This paper demonstrates the added value of such interoperability requirements, and a form of accomplishing them through a cross-project pilot. © 2022 CEUR-WS. All rights reserved.

关键词： Interoperability

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IEEE Workshop NEXTA 2021: The Next Level of Test Automation - Welcome Message

Proceedings - 2021 IEEE 14th International Conference on Sof...

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Proceedings - 2021 IEEE 14th International Conference on software Testing, Verification and Validation Workshops, ICSTW 2021 2021年 XIX页

作者： Eldh, Sigrid Garousi, Vahid Tahvili, Sahar Felderer, Michael Sandahl, Kristian Business Unit Networks Standards and Technology Software Computer Science Ericsson AB Stockholm Sweden School of EEECS Queen's University Belfast Bahar Software Engineering Consulting Corporation Belfast United Kingdom Group Function Technology Global AI Accelerator Ericsson AB Stockholm Sweden Dept. of Computer Science University of Insbruck Innsbrück Austria Dept. of Computer and Information Science Linköping University Linköping Sweden

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A new smart-cropping pipeline for prostate segmentation using deep learning networks

arXiv

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arXiv 2021年

作者： Zaridis, Dimitrios G. Mylona, Eugenia Tachos, Nikolaos S. Marias, Kostas Papanikolaou, Nikolaos Tsiknakis, Manolis Fotiadis, Dimitrios I. The Dept. of Biomedical Research FORTH-IMBB Ioannina Greece The Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece The Institute of Computer Science FORTH Heraklion Greece The Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Greece The Centre for the Unknown Champalimaud Foundation Clinical Computational Imaging Group Lisbon Portugal Crete Heraklion Greece The Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina Greece The Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR 45110 Greece

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of these models has achieved promising results, there is still room for improvement before these models can be used safely and effectively in clinical practice. One of the major challenges in prostate MR image segmentation is the presence of class imbalance in the image labels where the background pixels dominate over the prostate. In the present work we propose a DL-based pipeline for cropping the region around the prostate from MRI images to produce a more balanced distribution of the foreground pixels (prostate) and the background pixels and improve segmentation accuracy. The effect of DL-cropping for improving the segmentation performance compared to standard center-cropping is assessed using five popular DL networks for prostate segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net. The proposed smart-cropping outperformed the standard center cropping in terms of segmentation accuracy for all the evaluated prostate segmentation networks. In terms of Dice score, the highest improvement was achieved for the Unet+ and ResU-net++ architectures corresponding to 8.9% and 8%, respectively. © 2021, CC BY.

关键词： Image segmentation

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The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

arXiv

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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

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Proceedings Introduction

2021 IEEE Conference on Norbert Wiener in the 21st Century: ...

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2021 IEEE Conference on Norbert Wiener in the 21st Century: Being Human in a Global Village, 21CW 2021 2021年

作者： Love, Heather Adamson, Greg Gopal, T.V. Department of English Language and Literature University of Waterloo Canada Computing and Information Systems University of Melbourne MelbourneVIC Australia Dept. of Computer Science Engineering CEG Campus Anna University Chennai India

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V-Edge: Virtual edge computing as an enabler for novel microservices and cooperative computing

arXiv

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arXiv 2021年

作者： Dressler, Falko Chiasserini, Carla Fabiana Fitzek, Frank H.P. Karl, Holger Cigno, Renato Lo Capone, Antonio Casetti, Claudio Malandrino, Francesco Mancuso, Vincenzo Klingler, Florian Rizzo, Gianluca The School of Electrical Engineering and Computer Science TU Berlin Berlin Germany The Dept. of Electronics and Telecommunications Politecnico di Torino Torino Italy The School of Engineering Sciences as well as the Excellence Cluster CeTI TU Dresden Dresden Germany The Hasso-Plattner-Institute Potsdam Germany The Dept. of Information Engineering University of Brescia Brescia Italy The Dept. of Electronics Information and Bioengineering Politecnico di Milano Milano Italy The Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy The Institute of Electronics Computer and Communication Engineering of the National Research Council of Italy Torino Italy IMDEA Networks Madrid Spain The Dept. of Computer Science Paderborn University Paderborn Germany The Research Institute of Information Systems HES-SO Valais-Wallis Switzerland University of Foggia Italy

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays that are just owed to limited propagation speed, edge servers deployed in close proximity to the user, e.g., at some 5G gNB, serve as proxy for the cloud. Yet this promising idea is hampered by the limited availability of such edge servers. In this paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge bridges the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available using well-defined interfaces. V-Edge also acts as an enabler for novel microservices as well as cooperative computing solutions. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome, in order to enable wide-spread and even more powerful edge services. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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Evaluation of Feature engineering on Wearable Sensor-based Fall Detection

Evaluation of Feature Engineering on Wearable Sensor-based F...

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International Conference on information Networking

作者： Anita Ramachandran Adarsh Ramesh Anupama Karuppiah Dept. of Computer Science & Information Systems BITS Pilani Bangalore India Dept. of Electrical & Electronics Engineering BITS Pilani India

ISBN: (数字)9781728141992

ISBN: (纸本)9781728142005

Internet of Things (IoT) enabled geriatric healthcare systems have gained importance in the recent years due to an increase in the number of elderly people living alone. The application of machine learning (ML) in areas of geriatric healthcare such as fall detection, has, consequently been an area of active research. Wearable systems for fall detection has the advantage of being light-weight and low power-consuming, yet reasonably accurate without being overly intrusive. However, the accuracy of fall detection using wearable systems depends, among other factors, on the types of sensors embedded in them. The use of inertial motion unit (IMU) sensors for fall detection, combined with machine learning classifiers applied on datasets collected from IMU sensors is an area of active research. In this paper, we analyze the impact of using IMU sensor parameters in combination with vital signs parameters, on the performance of ML algorithms for fall detection. We compare the performance of various ML algorithms on the dataset we collected for this purpose. We also perform statistical analysis to examine the relative importance of the various features on the behavior of ML classifiers.

关键词： Heart rate Feature extraction Accelerometers Forestry Machine learning Sensor fusion

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