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Guest Editorial Software Defined Internet of Vehicles

Zhihan Lv, Jaime Lloret, Houbing Song
2021 IEEE transactions on intelligent transportation systems (Print)  
data on the platform.  ...  The system will control every vehicle involved in the traffic and control every road in real time to provide users with traffic efficiency and safety.  ...  In "Traffic Flow Prediction Based on Deep Learning in Internet of Vehicles," a traffic flow prediction framework for urban road network based on deep learning is proposed.  ... 
doi:10.1109/tits.2021.3080875 fatcat:tvd6ubmtmfe73mta3iorxzncju

D5.1 Key Technologies for IoT Data Management Benchmark

Gino Ciccone, Giuseppina Carpentieri, Cosimo Zotti, Alexandr Tardo, Marek Bednarczyk, Tadeusz Puźniakowski, Paweł Czapiewski, Stefan Köpsell, Kumar Sharad, José Luis Cárcel, Joan Meseguer, Ahmad Nimr (+9 others)
2021 Zenodo  
The deliverable describes the state of the current technologies and the planned innovations applied to internet-of-things (IoT) data management and applications.  ...  This document describes the approach of iNGENIOUS to develop an interoperable layer, aggregating data coming from different existing and forthcoming IoT technologies.  ...  The main steps associated with predicting the course of the ongoing voyage of a vessel include classification of the current destination of the vessel, predicting the trajectory or route that the vessel  ... 
doi:10.5281/zenodo.5084813 fatcat:ssllddbo7rda7fadthu26ajrzu

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Shreshth Tuli, Nipam Basumatary, Sukhpal Singh Gill, Mohsen Kahani, Rajesh Chand Arya, Gurpreet Singh Wander, Rajkumar Buyya
2019 Future generations computer systems  
We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis.  ...  Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both.  ...  We would also like to thank Samodha Pallewatta, Shashikant Ilager (CLOUDS Lab, University of Melbourne) and Shikhar Tuli (Indian Institute of Technology, Delhi) for their valuable comments on improving  ... 
doi:10.1016/j.future.2019.10.043 fatcat:eqhosiszbvafzhy7wjkr3poiwe

6G Internet of Things: A Comprehensive Survey

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor
2021 IEEE Internet of Things Journal  
In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT.  ...  We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater  ...  We believe our timely work will shed valuable light on the research of the 6G-IoT integration topics as well as motivate researchers and stakeholders to augment the research efforts in this promising area  ... 
doi:10.1109/jiot.2021.3103320 fatcat:fgm4ndqp6napjlt3z4ikthdsfy

Towards Smart Port Infrastructures: Enhancing Port Activities using Information and Communications Technology

Kok-Lim Alvin Yau, Shuhong Peng, Junaid Qadir, Yeh-Ching Low, Mee Hong Ling
2020 IEEE Access  
This article aims to offer a review of the research literature on smart ports, including Internet of Things platform, greenhouse gases emission reduction, energy efficiency enhancement, and so on.  ...  Smart ports, as high performing ports, utilize information and communications technology (ICT) to provide a wide range of smart applications, resulting in vastly improved vessels and container management  ...  He is a Researcher, a Lecturer, and a Consultant in cognitive radio, wireless networks, applied artificial intelligence, applied deep learning, and reinforcement learning.  ... 
doi:10.1109/access.2020.2990961 fatcat:7vll62tndzf2za5tfuk6gpuvzu

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

Khaleel Husain, Mohd Soperi Mohd Zahid, Shahab Ul Hassan, Sumayyah Hasbullah, Satria Mandala
2021 Electronics  
The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment  ...  This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives.  ...  Deep Learning Deep learning (DL) is a subset of ML. It is where ML extends its ability to learn inspired by the human brain using an artificial neural network (ANN).  ... 
doi:10.3390/electronics10020105 fatcat:l5eykpr3xncepbq6spoqjyit44

Data Analysis Methods for Software Systems

Jolita Bernatavičienė
2021 Vilnius University Proceedings  
DAMSS-2021 is the 12th international conference on data analysis methods for software systems, organized in Druskininkai, Lithuania. The same place and the same time every year.  ...  The exception was 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year's break, the Conference is back on track.  ...  P2Y12 Inhibitors (Ticagrelor) Adverse Scalable Trust Region Bayesian Effect Prediction Depending on Clinical Optimization with Product of Experts Data and Genetic Factors in Patients after Saulius Tautvaišas  ... 
doi:10.15388/damss.12.2021 fatcat:iefv6bz3drcrfpcwxoaqmu3gra

BigDataStack - D2.1 State of the art and Requirements analysis - I

Orlando Avila-García, Paula Ta-Shma, Yosef Moatti, Everton Luís Berz, Ana Juan Ferrer, Ana Belén González Méndez, Bernat Quesada, Alberto Soler, Stathis Plitsos, Konstantinos Giannakakis, Amaryllis Raouzaiou, Pavlos Kranas (+13 others)
2020 Zenodo  
This is the first version of the state-of-the-art and requirements analysis to drive the architecture and research effort in BigDataStack.  ...  User requirements have been collected through the BigDataStack's use case providers and complemented with emerging technical requirements.  ...  Description The framework needs to facilitate the implementation of complex machine learning and deep learning models, that will be able to learn the intricate relationships between data points, to  ... 
doi:10.5281/zenodo.4004048 fatcat:dbxtbfjenvenlecpjz37lf6ksy

Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs [article]

Lauren M. Sanders
2021 arXiv   pre-print
, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space.  ...  Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which  ...  Learn. Res. 22, 1–90 (2021). 109. Ribeiro, M. T., Singh, S. & Guestrin, C. ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier. arXiv [cs.LG] (2016). 110.  ... 
arXiv:2112.12582v1 fatcat:qelzg32unnhd3j6ku7gdmrcbem

Handling of advanced persistent threats and complex incidents in healthcare, transportation and energy ICT infrastructures

Spyridon Papastergiou, Haralambos Mouratidis, Eleni-Maria Kalogeraki
2020 Evolving Systems  
Despite the availability of various advanced incident handling techniques and tools, there is still no easy, structured, standardized and trusted way to manage and forecast interrelated cybersecurity incidents  ...  and respond to security threats and risks and and it guides them to handle effectively cyber incidents.  ...  been supported by the European Union's Horizon 2020 Project "CyberSANE" under Grant Agreement No. 833683, the European Union's Horizon 2020 Project "CyberSec4Europe" under Grant Agreement No. 830929 and  ... 
doi:10.1007/s12530-020-09335-4 fatcat:fx76tetjofdkjapu6ymdrabtdq

Supporting the Wellness at Work and Productivity of Ageing Employees in Industrial Environments: The sustAGE Project

Maria Pateraki, Manolis Lourakis, Leonidas Kallipolitis, Frank Werner, Petros Patias, Christos Pikridas
2019 Zenodo  
Supporting the Wellness at Work and Productivity of Ageing Employees in Industrial Environments: The sustAGE Project  ...  Deep Learning algorithms usually need a large amount of data.  ...  flexibility and scalability.  ... 
doi:10.5281/zenodo.4294256 fatcat:ovybcpqny5eppniw7hb5vcsevy

Defense Advanced Research Projects Agency (Darpa) Fiscal Year 2016 Budget Estimates

Department Of Defense Comptroller's Office
2015 Zenodo  
SONNET will demonstrate a scalable, power efficient prototype of such a graph processor and quantify performance for DoD-relevant applications.  ...  SONNET will demonstrate a scalable, power efficient prototype of such a graph processor and quantify performance for DoD-relevant applications.  ... 
doi:10.5281/zenodo.1215366 fatcat:cqn5tyfixjanzp5x3tgfkpedri

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mohammed Najah Mahdi, Abdul Rahim Ahmad, Qais Saif Qassim, Hayder Natiq, Mohammed Ahmed Subhi, Moamin Mahmoud
2021 Applied Sciences  
This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore.  ...  In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning.  ...  More equipment and software applications will be required on the grid, including as sensors, faster processors, and stronger algorithms, in order to achieve greater efficiency and dependability in the  ... 
doi:10.3390/app11178117 fatcat:4vtzn5cae5eqtnzobtvzysi6mm

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Mohammed Aledhari, Rehma Razzak, Reza M. Parizi, Fahad Saeed
2020 IEEE Access  
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases.  ...  FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1109/access.2020.3013541 pmid:32999795 pmcid:PMC7523633 fatcat:zyl5kcmi6fdvzarq5p6reha224

Data Management in Industry 4.0: State of the Art and Open Challenges [article]

Theofanis P. Raptis, Andrea Passarella, Marco Conti
2019 arXiv   pre-print
from the field level deep in the physical deployments, up to the cloud and applications level.  ...  on practical applications.  ...  In [307] , the authors introduce an efficient deep learning model to predict cloud virtual machines workload for industrial NCS deployments.  ... 
arXiv:1902.06141v2 fatcat:4uvwquinx5h65dy4udx24bhrmm
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