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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Øyvind Meinich-Bache Hannah Håland Anita Yeconia Rafal Paprocki Solveig Haukås Haaland Øystein Gomo Thomas Haukland Siren Irene Rettedal Sara Brunner Esto Mduma Hege Ersdal Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Strategic Research Laerdal Medical Stavanger Norway Haydom Lutheran Hospital Tanzania Stavanger University Hospital Norway Faculty of Health Sciences University of Stavanger Norway

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

For a healthcare provider treating a non-breathing newborn baby, timely ventilation of the lungs is the most critical action to save the newborn’s life. However, the guidelines for newborn resuscitation are challenging to follow and a real-time feedback and guidance system may contribute to more optimal treatment. In this study we utilized deep neural networks for detecting ventilation in the audio signal extracted from video recordings of 25 newborn resuscitation episodes. The proposed ventilation detection system utilizes Log Mel Spectrograms for feature generation, audio augmentation for dataset expansion, and 2D Convolutional Neural Networks (CNNs) for ventilation classification. For the task of image classification, we compared two different pretrained backbones suited for real-time inference and a custom-built efficient CNN, called VD-CNN. We found that using MobilenNetV3Large as the feature extractor yielded the best results, achieving a F1-score of 97.21%. Our efficient VD-CNN, with a parameter count one fifth that of MobilenNetV3Large, still achieved a robust F1-score of 95.37%. To demonstrate that the proposed system can perform ventilation detection and provide immediate feedback during newborn resuscitation in real-time, we integrated the ventilation detection system into a newborn resuscitation station called Liveborn Station.

关键词： Pediatrics Computational modeling Tablet computers Medical services Ventilation Feature extraction Real-time systems Convolutional neural networks Video recording Spectrogram

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Identification of EEG Features of Transcranial Electrical Stimulation (tES) Based on eXplainable Artificial Intelligence (XAI)

Identification of EEG Features of Transcranial Electrical St...

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： Pasquale Arpaia Lidia Ammendola Maria Cropano Matteo De Luca Anna Della Calce Ludovica Gargiulo Giacomo Lus Luigi Maffei Daniela Malangone Nicola Moccaldi Simona Raimo Elisabetta Signoriello Paolo De Blasiis Dep. of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy II Neurological Clinic Multiple Sclerosis Center University of Campania “Luigi Vanvitelli” Naples Italy Dep. of Advanced Medical and Surgery Science Second Division of Neurology University of Campania “Luigi Vanvitelli” Naples Italy Dep. of Medical and Surgical Sciences “Magna Graecia” University of Catanzaro Catanzaro Italy University of Basilicata Potenza Italy University of Campania “Luigi Vanvitelli” Naples Italy

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

Electroencephalographic (EEG) features of transcranial Electrical Stimulation (tES) effects in Multiple Sclerosis (MS) patients were identified. Machine Learning and eXplainable Artificial Intelligence (XAI) algorithms were the used methods for the EEG feature selection. Current tES-based treatments lack of adaptivity to their effects on individuals. Real-time modification of electrical stimulation parameters based on the trend of specific EEG features may represent a new perspective in terms of personalized medicine for MS patients. This preliminary study aimed to identify EEG features reflecting the effect of tES treatment. Ongoing analyses are exploring the correlation between the identified EEG features and the expected clinical outcomes. Five MS patients underwent non-pharmacological treatment combining Theory of Mind (ToM) training with tES or sham treatment. Pre-and post-treatment variation in EEG features were assessed both in Eyes Opened (EO) and Eyes Closed (EC) conditions. In particular, absolute and relative power across six frequency bands, and Posterior Dominant Rhythm (PDR) amplitude and frequency were explored. The Sequential Feature Selection (SFS) algorithm in combination with Support Vector Machine (SVM) classifier identified (i) difference of absolute powers in high beta band in T3 channel, (ii) difference of absolute powers in gamma band in T3 channel, and (iii) difference of absolute powers in gamma band in O2 channel in EO condition, and (i) difference of absolute powers in gamma band in T3 channel, (ii) difference of absolute powers in gamma band in C4 channel, and (iii) difference of PDR amplitude in O2 channel in EC condition as the most discriminating tES from sham treatment (67.5 % accuracy in EO and 83.33 % in EC conditions, respectively). The SHapley Additive exPlanations (SHAP) algorithm highlighted PDR amplitudes in O2 and O1 as most informative features.

关键词： Support vector machines Training Accuracy Machine learning algorithms Explainable AI Precision medicine Electrical stimulation Feature extraction Electroencephalography Classification algorithms

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Smart Human Resource Management System to Maximize Productivity

Smart Human Resource Management System to Maximize Productiv...

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2020 International computer Symposium, ICS 2020

作者： H.a.s.s, Hewage K.u, Hettiarachchi K.m.j.b, Jayarathna K.p.c., Hasintha Senarathne, A.N. Wijekoon, J. Sri Lanka Institute of Information Technology Dep. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dep. of Information Technolgy Malabe Sri Lanka

ISBN: (纸本)9781728192550

Human resource is one of the most valuable assets in an organization. They are bounded to develop the unique and dynamic aspects that strengthen their competitive advantage to persist in an always changing market environment. In order to recruit a quality candidate for an organization, reducing human involvement and verifying details of the candidate is important in recruitment process. Furthermore, having an idea about how well or poor the employees perform, and how likely the employee attrition can occur is vital in human resource management process. This paper is an attempt to introduce smart human resource management system that can maximize the productivity of an organizational environment using machine learning and blockchain technologies. The end goal of this research is a smart human resource management system that reduces human judgment, time in the candidate selection process and predicts employee performance and attrition to motivate current employers to maximize productivity with minimal financial loss in the workplace environment. Skill assessment and resume classification have been done using unsupervised learning algorithms and natural language processing after extracting raw data from employee resumes using Object Character Recognition. Candidate details verification is done by comparing the hashes of the records which are stored in the blockchain. Employee performance and attrition are predicted using supervised machine learning classification techniques with high accuracy and the result of the final performance is generated as a score for each employee considering the multiple attributes that has been standardized and regulated by some specifically considered e-competence frameworks. © 2020 IEEE.

关键词： Human resource management

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Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises

Analyzing Data Efficiency and Performance of Machine Learnin...

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European Conference on Mobile Robots (ECMR)

作者： Aleksa Marusic Louis Annabi Sao Mai Nguyen Adriana Tapus Autonomous Systems and Robotics Lab Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris Palaiseau France Dep. Informatique IMT Atlantique

Physical rehabilitation focuses on the improvement of body functions, usually after injury or surgery. Patients undergoing rehabilitation often need to perform exercises at home without the presence of a physiotherapist. computer-aided assessment of physical rehabilitation can improve patients' performance and help in completing prescribed rehabilitation exercises. In this work, we focus on human motion analysis in the context of physical rehabilitation for Low Back Pain (LBP). As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data acquired from RGB videos and human pose estimation from those. In this work, we provide an analysis of two types of algorithms on a Low Back Pain rehabilitation datasets. One is based on a Gaussian Mixture Model (GMM), with performance metrics based on the log-Likelihood values from GMM. Furthermore, with the recent development of Deep Learning and Graph Neural Networks, algorithms based on Spatio-Temporal Graph Convolutional Networks (STGCN) are taken as a novel approach. We compared the algorithms in terms of data efficiency and performance, with evaluation performed on two LBP rehabilitation datasets: KIMORE and Keraal. Our study confirms that Kinect, OpenPose, and BlazePose data yield similar evaluation scores, and shows that STGCN outperforms GMM in most configurations.

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The search of the path MTU with QUIC

The search of the path MTU with QUIC

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2021 Workshop on the Evolution, Performance and Interoperability of QUIC, EPIQ 2021 - Part of CoNEXT 2021

作者： Völker, Timo Tüxen, Michael Rathgeb, Erwin P. FH Münster Uni. of Appl. Sciences Dep. of Electrical Engineering and Computer Science NRW Steinfurt Germany University of Duisburg-Essen Computer Networking Technology Group NRW Essen Germany

ISBN: (纸本)9781450391351

A data sender in an IP based network is only capable to efficiently use a network path if it knows the packet size limit of the path, i.e., the Path Maximum Transmission Unit (PMTU). The IETF recently specified a PMTU discovery framework for transport protocols like QUIC. This paper complements this specification by presenting a search algorithm. In addition, it defines several metrics and shows results of analyses for the algorithm with various PMTU candidate sequences using these metrics. We integrated the PMTU discovery with our algorithm into a QUIC simulation model. This paper describes the integration and presents measurements obtained by simulations. © 2021 ACM.

关键词： Learning algorithms

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Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring

Speech Emotion Recognition using Supervised Deep Recurrent S...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Nelly Elsayed Zag ElSayed Navid Asadizanjani Murat Ozer Ahmed Abdelgawad Magdy Bayoumi School of Information Technology University of Cincinnati OH United States School of Information Technology University of Cincinnati Ohio United States Dep. of Electrical & Computer Engineering University of Florida Florida United States School of Engineering and Technology Central Michigan University Michigan United States Dep. of Electrical & Computer Engineering University of Louisiana at Lafayette Louisiana Unted States

Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due to uncontrolled mental health, early detection of mental issues is crucial. Nowa-days, the usage of Intelligent Virtual Personal Assistants (IVA) has increased worldwide. Individuals use their voices to control these devices to fulfill requests and acquire different services. This paper proposes a novel deep learning model based on the gated recurrent neural network and convolution neural network to understand human emotion from speech to improve their IVA services and monitor their mental health.

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Blockchain Hybrid-model Scheme for Scalable Cross-domain Authorisation

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Blockchain: Research and Applications 2025年

作者： Munkenyi Mukhandi Jorge Granjal João P. Vilela CISUC Dep. of Informatics Engineering University of Coimbra Portugal CISUC Dep. of Informatics Engineering & CRACS / INESCTEC & Dep. of Computer Science Faculty of Sciences University of Porto Portugal

We introduce a novel blockchain-based inter-ledger authorization scheme, a unique solution designed to support delegated authorization management in cross-domain cloud environments. This innovative approach enables client applications, acting on behalf of a user, to request access to specific resources owned by a different organization. It benefits cross-domain multi-cloud applications, such as large-scale collaboration projects involving multiple cloud service providers. Multi-cloud presents authorization challenges because of complex access control operations in multiple distinct domains. OAuth2 is the state-of-the-art choice for delegated authorization. However, it falls short in handling such complex interactions with multiple authorization servers and resource servers, and this is our motivation. Compared to OAuth2, our approach overcomes the limitations of a centralized model and achieves coordinated distinct delegations among multiple entities without a third-party trusted component. Authorization servers are blockchain participants and designated access token issuers, ensuring minimum client data collection and promoting client privacy. Multiple smart contracts are dep.oyed in our multi-ledger blockchain environment to guarantee the provenance of the authorization operations and facilitate scalable cross-domain authorization. Our ‘hybrid inter-ledger model’, combines security techniques such as blockchain-based device authentication, hashed-time locks (HTCLs), and service discovery. The blockchain-based device authentication ensures secure and trusted interactions, HTCLs enable time-bound transactions, and service discovery simplifies finding and connecting to the required cloud services. Our experimental evaluation provides insights into the scalability of our approach, which achieves decentralized authorization in a multi-cloud environment with acceptable latencies compared to related works and shows stable throughput against concurrent authorization reque

关键词： OAuth2 multi-cloud decentralised coordinated authorisation multi-ledgers blockchain security smart contracts

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Robust machine learning in critical care - Software engineering and medical perspectives 1

Robust machine learning in critical care - Software engineer...

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1st IEEE/ACM Workshop on AI engineering - Software engineering for AI, WAIN 2021

作者： Staron, Miroslaw Herges, Helena Odenstedt Naredi, Silvana Block, Linda El-Merhi, Ali Vithal, Richard Elam, Mikael University of Gothenburg Computer Science and Engineering Chalmers Gothenburg Sweden Sahlgrenska University Hospital Dep. of Anesthesiology and Intensive Care Sahlgrenska Academy and Region Västra Götaland Gothenburg Sweden Sahlgrenska Academy Institute of Neurosc. and Physiology Dep. of Clinical Neuroscience Gothenburg Sweden

ISBN: (纸本)9781665444705

Using machine learning in clinical practice poses hard requirements on explainability, reliability, replicability and robustness of these systems. Therefore, developing reliable software for monitoring critically ill patients requires close collaboration between physicians and software engineers. However, these two different disciplines need to find own research perspectives in order to contribute to both the medical and the software engineering domain. In this paper, we address the problem of how to establish a collaboration where software engineering and medicine meets to design robust machine learning systems to be used in patient care. We describe how we designed software systems for monitoring patients under carotid endarterectomy, in particular focusing on the process of knowledge building in the research team. Our results show what to consider when setting up such a collaboration, how it develops over time and what kind of systems can be constructed based on it. We conclude that the main challenge is to find a good research team, where different competences are committed to a common goal. © 2021 IEEE.

关键词： Software design

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Comparison of Traditional ML Algorithms for Energy Consumption Prediction Models

Comparison of Traditional ML Algorithms for Energy Consumpti...

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2022 IEEE Future Networks World Forum (FNWF)

作者： Rebeca Estrada Víctor Asanza Danny Torres Irving Valeriano Daniel Alvarado Information Technology Center & Electrical Eng. and Computer Science Dep. Escuela Superior Politécnica del Litoral Guayaquil Ecuador Electrical Eng. and Computer Science Dep. Escuela Superior Politécnica del Litoral Guayaquil Ecuador Information Technology Center Escuela Superior Politécnica del Litoral Guayaquil Ecuador Mechanical and Production Sciences Eng. Dep. Escuela Superior Politécnica del Litoral Guayaquil Ecuador

Data centers consume a large amount of energy to meet the increasing demand for IT infrastructure and software due to the IT equipment and cooling infrastructure involved. Therefore, it is necessary to have energy consumption control strategies for DC computer equipment that will allow infrastructure upgrades to reduce energy consumption and to meet the requirement of Green IT. In this way, energy consumption is reduced and the use of technological resources can be optimized. In this paper, we propose to evaluate several traditional Machine Learning algorithms as prediction models using three different temporal windows (i.e. minute, hour and day) taking into account several features such as voltage, energy, frequency, current, power, power factor, and temperature. A comparison of the root square mean error (RMSE) during the validation stage is carried out in order to select the most appropriate algorithm for each time window. In addition, running times are calculated to determine the feasibility of the selected algorithms. Moreover, the suitable predictive model can be the key to the ensure a fair distribution of the workload among the different servers in a Datacenter.

关键词： Energy consumption Data centers Machine learning algorithms Temperature Computational modeling Predictive models Prediction algorithms

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Fabrication and Studies of Dielectric Properties of Nanocomposites Materials Based on Polyurethane

SSRN

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SSRN 2023年

作者： Hardoň, Štefaň Kúdelčík, Jozef Baran, Anton Hornak, Jaroslav Trnka, Pavel Tamus, Adam Zoltan Koltunowicz, Tomasz Norbert Kozáková, Alena Dérer, Tomáš Department of Physics Faculty of Electrical Engineering and Information Technology University of Žilina Žilina010 26 Slovakia Dep. of Physics Fac. of Electrical Engineering and Informatics Technical University of Košice Park Komenského 2 Košice042 00 Slovakia Dep. of Technologies and Measurement Fac. of Electrical Engineering University of West Bohemia at Pilsen Plzeň301 00 Czech Republic Dep. of Electric Power Engineering Fac. of Electrical Engineering and Informatics Budapest University of Technology of Economics Egry J. Street 18. BudapestH-1111 Hungary Dep. of Electrical Devices and High Voltage Technology Fac. of Electrical Engineering and Computer Science Lublin University of Technology Lublin Poland VUKI a.s. Electroinsulating Resins and Varnishes Bratislava Slovakia

This paper addresses the influence of nanoparticles (NPs) on the dielectric properties of polyurethane. In this research, we only investigated the effect of Al2O3, MgO and ZnO NPs for concentrations up to 2% wt and in the temperature range from 25 to 120 °C. During the preparation of nanocomposites, polymer chains bind to NPs, which then limits their mobility. This effect results in a decrease in real permittivity and the course of polarization mechanisms. We observed an increase in permittivity over the entire temperature range only for a 2% MgO concentration. The decrease in the mobility of polymer chains was confirmed by dynamic-mechanical analysis. This method also results in an increase in the glass transition temperature dep.nding on the type and concentration of NPs. The presence of NPs and the limited mobility of the polymer chains results in a shift of the low-frequency local maximum to lower frequencies © 2023, The Authors. All rights reserved.

关键词： Aluminum oxide

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