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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Belal Alsinglawi Fady Alnajjar Omar Mubin Mauricio Novoa Mohammed Alorjani Ola Karajeh Omar Darwish School of Computer Data and Mathematical Sciences Western Sydney University Penrith NSW Australia College of Information Technology United Arab Emirates University Al Ain 15551 UAE Department of Pathology and Microbiology King Abdullah University Hospital Faculty of Medicine Jordan University of Science and Technology Irbid Jordan Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA United States Department of Computer Information Systems Ferrum College Ferrum Virginia United States

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently, few studies examined the length of stay (LOS) predictive analytics for cardiovascular inpatients in ICU. However, there are almost scarcely real attempts utilized machine learning models to predict the likelihood of heart failure patients length of stay in ICU hospitalization. This paper introduces a predictive research architecture to predict Length of Stay (LOS) for heart failure diagnoses from electronic medical records using the state-of-art- machine learning models, in particular, the ensembles regressors and deep learning regression models. Our results showed that the gradient boosting regressor (GBR) outweighed the other proposed models in this study. The GBR reported higher R-squared value followed by the proposed method in this study called Staking Regressor. Additionally, The Random forest Regressor (RFR) was the fastest model to train. Our outcomes suggested that deep learning-based regressor did not achieve better results than the traditional regression model in this study. This work contributes to the field of predictive modelling for electronic medical records for hospital management systems.

关键词： Predictive models Heart Machine learning Hospitals Biological system modeling Mathematical model Tuning

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An efficient internet traffic classification system using deep learning for IoT

arXiv

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arXiv 2021年

作者： Umair, Muhammad Basit Iqbal, Zeshan Bilal, Muhammad Almohamad, Tarik Adnan Nebhen, Jamel Mehmood, Raja Majid Department of Computer Science University of Engineering and Technology Taxila47050 Pakistan Division of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Yongin-si Korea Republic of Electrical-Electronics Engineering Department Faculty of Engineering Karabük University Karabük78050 Turkey Department of Electronics and Communication Engineering A'Sharqiyah University Ibra400 Oman Prince Sattam bin Abdulaziz University College of Computer Engineering and Sciences P.O. Box 151 Alkharj 11942 Saudi Arabia Information and Communication Technology Department School of Electrical and Computer Engineering Xiamen University Malaysia Sepang43900 Malaysia

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the preprocessed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification *** Codes 62M45, 68T07 © 2021, CC BY.

关键词： Internet of things

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Foreword

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2020年 1017卷 v-vi页

作者： Abawajy, Jemal H. Choo, Kim-Kwang Raymond Islam, Rafiqul Xu, Zheng Atiquzzaman, Mohammed Faculty of Science Deakin University GeelongVIC Australia Department of Information Systems and Cyber Security The University of Texas at San Antonio San AntonioTX United States School of Computing and Mathematics Charles Sturt University AlburyNSW Australia Shanghai University Shanghai China School of Computer Science University of Oklahoma NormanOK United States

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Packaging research artefacts with RO-Crate

arXiv

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arXiv 2021年

作者： Soiland-Reyes, Stian Sefton, Peter Crosas, Mercè Castro, Leyla Jael Coppens, Frederik Fernández, José M. Garijo, Daniel Grüning, Björn Rosa, Marco La Leo, Simone Carragáin, Eoghan Ó. Portier, Marc Trisovic, Ana Groth, Paul Goble, Carole Department of Computer Science University of Manchester United Kingdom Informatics Institute University of Amsterdam Netherlands Faculty of Science University Technology Sydney Australia Institute for Quantitative Social Science Harvard University CambridgeMA United States ZB MED Information Centre for Life Sciences Cologne Germany VIB-UGent Center for Plant Systems Biology Gent Belgium Barcelona Supercomputing Center Barcelona Spain Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Bioinformatics Group Department of Computer Science Albert-Ludwigs-University Freiburg Freiburg Germany PARADISEC Melbourne Australia Italy University College Cork Ireland Vlaams Instituut voor de Zee Oostende Belgium Institute for Quantitative Social Science Harvard University CambridgeMA United States Informatics Institute University of Amsterdam Netherlands Department of Computer Science University of Manchester United Kingdom

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on *** annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying"just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. © 2021, CC BY.

关键词： Metadata

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Roadmap for Unconventional Computing with Nanotechnology

arXiv

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arXiv 2023年

作者： Finocchio, Giovanni Incorvia, Jean Anne C. Friedman, Joseph S. Yang, Qu Giordano, Anna Grollier, Julie Yang, Hyunsoo Ciubotaru, Florin Chumak, Andrii Naeemi, Azad J. Cotofana, Sorin D. Tomasello, Riccardo Panagopoulos, Christos Carpentieri, Mario Lin, Peng Pan, Gang Joshua Yang, J. Todri-Sanial, Aida Boschetto, Gabriele Makasheva, Kremena Sangwan, Vinod K. Trivedi, Amit Ranjan Hersam, Mark C. Camsari, Kerem Y. McMahon, Peter L. Datta, Supriyo Koiller, Belita Aguilar, Gabriel H. Temporão, Guilherme P. Rodrigues, Davi R. Sunada, Satoshi Everschor-Sitte, Karin Tatsumura, Kosuke Goto, Hayato Puliafito, Vito Åkerman, Johan Takesue, Hiroki Ventra, Massimiliano Di Pershin, Yuriy V. Mukhopadhyay, Saibal Roy, Kaushik Wang, I-Ting Kang, Wang Zhu, Yao Kaushik, Brajesh Kumar Hasler, Jennifer Ganguly, Samiran Ghosh, Avik W. Levy, William Roychowdhury, Vwani Bandyopadhyay, Supriyo Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Chandra Family Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Engineering University of Messina Messina98166 Italy Laboratoire Albert Fert CNRS Thales Université Paris-Saclay Palaiseau91767 France IMEC Leuven3001 Belgium Faculty of Physics University of Vienna Vienna1090 Austria School of Electrical & Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Quantum and Computer Engineering Department Delft University of Technology DelftCD 2628 Netherlands Department of Electrical and Information Engineering Politecnico di Bari Bari70125 Italy Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore639798 Singapore College of Computer Science and Technology Zhejiang University Hangzhou310013 China Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Electrical Engineering Department Eindhoven University of Technology AZ Eindhoven5612 Netherlands Microelectronics Department LIRMM Université de Montpellier Montpellier34095 France Laplace CNRS UT3 INPT University of Toulouse Toulouse31062 France Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering University of Illinois at Chicago ChicagoIL60607 United States Department of Electrical and Computer Engineering University of California at Santa Barbara Santa BarbaraCA93106 United States School of Applied and

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries. © 2023, CC BY-NC-SA.

关键词： Nanotechnology

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Algorithmic Support for an Adaptive Automatic Monitoring System

Algorithmic Support for an Adaptive Automatic Monitoring Sys...

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Evgeniy M. Antonyuk Ilyas E. Varshavskiy Natalya V. Orlova Petr E. Antonyuk Department Information-Measuring Systems and Technologies (IMST) Saint Petersburg Electrotechnical University "LETI" St. Petersburg Russian Federation Department of Applied Mathematics and Computer Science St. Petersburg State University of Industrial Technologies and Design St. Petersburg Russian Federation

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

Modern automatic monitoring systems (AMS) solve the problems of analyzing the flows of measured data coming from the object under study. Solving the problems of regulation and monitoring requires a timely response of the devices to changing parameters of the input signals. Adaptive automatic monitoring systems correspond to these requirements, the construction of which is feasible based on programmable logic circuits, microcontrollers, real-time computers, neural network and expert technologies. At the heart of any of these systems is algorithmic support that describes the principle of action, which determines the operations and the sequence of their execution during the operation of the AMS. One type of adaptation is a parallel-serial analysis of deviations of input values from nominal values, which allows to increase the reliability of monitoring due to parallel calculations of the maximum difference and sequential detection of an allowable deviation.

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Enhancing with EV charging station functions a residential RES based network 4

Enhancing with EV charging station functions a residential R...

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4th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2018

作者： Marinescu, Corneliu Barote, Luminita Munteanu, Daniel Komasilovs, Vitalijs Zacepins, Aleksejs Kviesis, Armands Department of Electrical Engineering and Applied Physics Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department of Computer Systems Faculty of Information Technologies Latvia University of Agriculture Jelgava Latvia

ISBN: (纸本)9789897582936

The emergence of Electric Vehicles is creating a possible congestion of the electric grid. The switch in transportation, especially in cities (future Smart Cities are considered) is asking for the utilization of Renewable Energy Sources, RES, to decrease pollution. To address these two demands the paper proposes a solution based on a Residential Charging station architecture for Urban Electric Vehicles. The theoretical structure is presented and then the practical solution, as Smart Residential MicroGrid based on RES, is shown. In order to make an implementation more economically and technically affordable and be able to address in the very near future the growing need of EV Charging stations, the presented solution starts from the existing equipment used in millions of homes, mainly for solar energy. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Smart city

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Comprehensive Approach for Porous Materials Analysis Using a Dedicated Preprocessing Tool for Mass and Heat Transfer Modeling

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Journal of Thermal science 2018年第5期27卷 479-486页

作者： MADEJSKI Pawel KRAKOWSKA Paulina HABRAT Magdalena PUSKARCZYK Edyta JEDRYCHOWSKI Mariusz Department of Power Systems and Environmental Protection Facilities Faculty of Mechanical Engineering and Ro-botics AGH University of Science and TechnologyKraków 30-059 Poland Department of Geophysics Faculty of Geology Geophysics and Environmental Protection AGH University of Sci-ence and Technology Kraków 30-059 Poland Department of Geoinformatics and Applied Computer Science Faculty of Geology Geophysics and Environmental Protection AGH University of Science and TechnologyKraków 30-059 Poland Department of Condensed Matter Physics Faculty of Physics and Applied Computer Science AGH University of Science and TechnologyKraków 30-059 Poland

The paper presents a comprehensive, newly developed software – poROSE(poROus materials examination SoftwarE) for the qualitative and quantitative assessment of porous materials and analysis methodologies developed by the authors as a solution for emerging challenges. A low porosity rock sample was analyzed and thanks to the developed and implemented methodologies in poROSE software, the main geometrical properties were calculated. A tool was also used in preprocessing part of the computational analysis to prepare a geometrical representation of the porous material. The basic functions as elimination of blind pores in the geometrical model were completed and the geometrical model was exported for CFD software. As a result, it was possible to carry out calculations of the basic properties of the analyzed porous material sample. The developed tool allows to carry out quantitative and qualitative analysis to determine the most important properties characterized porous materials. In presented tool the input data can be images from X-ray computed tomography(CT), scanning electron microscope(SEM) or focused ion beam with scanning electron microscope(FIB-SEM) in grey level. A geometric model developed in the proper format can be used as an input to modeling mass, momentum and heat transfer, as well as, in strength or thermo-strength analysis of any porous materials. In this example, thermal analysis was carried out on the skeleton of rock sample. Moreover, thermal conductivity was estimated using empirical equations.

关键词： porous materials rock software thermal properties preprocessing

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An Optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) for Cryptocurrency Forecasting

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Procedia computer science 2019年 163卷 427-433页

作者： Nor Azizah Hitam Amelia Ritahani Ismail Faisal Saeed College of Computer Science and Engineering Taibah University Medina Saudi Arabia Information Systems Department Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia

Forecasting accurate future price is very important in financial sector. An optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) is introduced in forecasting the cryptocurrency future price. It is part of Artificial Intelligence (AI) that uses previous experience to forecast future price. Analysts and investors generally combine fundamental and technical analysis prior to decide the best price to execute their trades. Some may use Machine Learning Algorithms to execute their trades. However, forecasting result using basic SVM algorithms does not really promising. On the other hands, Particle Swarm Optimization (PSO) is known as a better algorithm for a static and simple optimization problem. Therefore, PSO is introduced to optimize the algorithms of SVM in cryptocurrency forecasting. The experiment of selected cryptocurrencies is conducted for this classifier. The experimental result demonstrates that an optimized SVM-PSO algorithm can effectively forecast the future price of cryptocurrency thus outperforms the single SVM algorithms.

关键词： Support Vector Machines Cryptocurrency Artificial Intelligence Machine Learning Particle Swarm Optimization

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Correction to: Real-time ontology-based context-aware situation reasoning framework in pervasive computing

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Multimedia Tools and Applications 2022年第11期81卷 14959-14959页

作者： Lakehal, Abderrahim Alti, Adel Roose, Philippe LRSD Laboratory Department of Computer Science Faculty of Sciences University of Ferhat Abbas SETIF-1 Sétif Algeria Department of Management Information Systems College of Business & Economics Qassim University Buraidah Kingdom of Saudi Arabia LIUPPA University of PAU Anglet France

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