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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Shahbakhti, Mohammad Beiramvand, Matin Krycinska, Roza Nasiri, Erfan Chen, Wei Sole-Casals, Jordi Wierzchon, Michal Broniec-Wojcik, Anna Augustyniak, Piotr Marozas, Vaidotas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Tampere University Faculty of Information Technology and Communication Tempere Finland Jagiellonian University Medical College Doctoral School in Medical and Health Sciences Krakow Poland Jagiellonian University Collegium Medicum Department of Interdisciplinary Intensive Care Krakow Poland Allameh Tabataba'i University Faculty of Statistics Mathematics and Computer Tehran Iran Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Shanghai China University of Vic-Central University of Catalonia Data and Signal Processing Research Group Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Agh University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland

ISBN: (纸本)9781665493840

Objective: Monitoring the depth of anesthesia (DoA) plays an important role for administering the drug injection during a surgery, i.e., preventing undesired awareness and inordinate anesthetic depth. Although the bispectral index (BIS) monitor is the golden standard system for the DoA monitoring, it is still not affordable for the developing countries. Alternatively, a low-cost electroencephalogram (EEG) headband can be used. The objective of this paper is to present a new algorithm for estimating the BIS values using a single frontal EEG channel. Method: In the first step, the EEG signal is filtered for the elimination of artifacts and is split into its sub-bands. In the second step, several linear and nonlinear features are extracted from each sub-band and fed to a random forest regression model in order to estimate the BIS. The performance of the proposed algorithm is assessed using EEG data recorded from twenty-four subjects during the general anesthesia and is validated in terms of correlation coefficient (CC) and absolute error (AE) between the reference and estimated BIS values. Results: The proposed algorithm achieved the mean CC of 0.83 and AE of 6.5 for intra subject variability and mean CC of 0.87 and AE of 5.5 for inter subject variability. Significance: Given the similar results for both intra and inter subject variability, the proposed algorithm has the potential to be used in the real-world scenario. © 2023 IEEE.

关键词： Electroencephalography

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Covid-19 Identification with Chest X-Ray Images merging Handcrafted and Automated Features for Enhanced Feature Generalization

Covid-19 Identification with Chest X-Ray Images merging Hand...

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IEEE International Conference on Computing, Communication and Security (ICCCS)

作者： Rik Das Mohammad Arshad P.K. Manjhi Sudeep D. Thepade Post Graduate Programme in Information Technology Xavier Institute of Social Service Ranchi India Faculty of Science (Computer Applications) Vinoba Bhave University Hazaribag India Vinoba Bhave University Hazaribag India Pimpri Chinchwad College of Engineering Pune Maharashtra

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

Outbreak of COVID-19 pandemic has imposed a major threat to the existence of human lives. High rate of infection due to the lethal virus has caused major sufferings and premature deaths to many budding possibilities. Nevertheless, quick identification of the disease can lead to faster recovery with medical help. Unfortunately, the ratio of COVID infection to methods of early medical identification of disease is quite feeble. This has motivated researchers in designing computer aided diagnosis (CAD) systems which can identify the disease from easily available chest X-Ray images of patients. However, the success rate of identification of COVID cases is not satisfactory since the proposed systems are emphasizing on identifying pneumonia rather than separating the cause of it as COVID or non COVID. In this paper, the authors have investigated the cause of such misclassification and have identified feature generalization as one of the reasons. A feature fusion based approach is proposed by the authors to encourage generalization of input features to CAD for better identification of COVID 19. The reason for proposing the fusion based feature formation is encompassing diverse X-Ray image information by extracting descriptors with assorted techniques and uniting them with fusion. The classification results with fusion based approach have reported substantial improvements compared to individual techniques. This in turn indicates better generalization of input features with feature fusion.

关键词： Feature extraction COVID-19 Lung X-ray imaging Support vector machines Histograms Convolutional neural networks

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

arXiv

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arXiv 2024年

作者： Li, Zongrui Lu, Zhan Yan, Haojie Shi, Boxin Pan, Gang Zheng, Qian Jiang, Xudong Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised/unsupervised NaUPS methods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP. Copyright © 2024, The Authors. All rights reserved.

关键词： Photometry

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Joint VNF Decomposition and Migration for Cost-Efficient VNF Forwarding Graph Embedding

Joint VNF Decomposition and Migration for Cost-Efficient VNF...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Seyedeh Negar Afrasiabi Amin Ebrahimzadeh Azadeh Azhdari Carla Mouradian Wubin Li Róbert Szabó Roch H. Glitho Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montréal QC Canada Ericsson Research Montréal QC Canada Ericsson Research Budapest Hungary Computer Science Programme University of Western Cape Capetown South Africa

Network Function Virtualization (NFV) enables the decoupling of network functions from dedicated hardware to run them as software instances on commodity servers through virtualization, replacing hardware-based network functions with software-based Virtual Network Functions (VNFs). In this paper, we study the joint problem of VNF decomposition and migration to address VNF embedding in NFV resource allocation (NFV-RA). More specifically, we investigate how VNF migration and VNF decomposition can be mutually beneficial to minimize the embedding cost of network services. After presenting a novel formulation of the problem as an integer linear programming (ILP), we validate it by CPLEX and show that our joint VNF decomposition and migration approach can outperform the VNF decomposition-only approach by 20% in terms of embedding cost.

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things

Intrusion Detection using Deep Neural Network Algorithm on t...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Syariful Ikhwan Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Computer Science Informatics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia Statistics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small.

关键词： Deep learning Training Satellites Neural networks Intrusion detection Training data Feature extraction

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Investigating Neural Network-Based Deep Learning Strategies for Real-Time Data Analysis in Machine Learning

Investigating Neural Network-Based Deep Learning Strategies ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Suchita Walke Mounika Nalluri Lavanya R Aruna Sri Rongali Hrishikesh Bhanudas Vanjari Ritu Computer Engineering pillai collage of Engg and Technology Mumbai University India Masters in Computer Information Systems Murray State University USA ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US E &TC Engineering Dept BVCOE Lavale SPPU Pune Maharashtra India Management Maharishi Markandeshwar(deemed to be university) Ambala Haryana

there are numerous distinct strategies and techniques that fall under the huge class of neural network-primarily based deep gaining knowledge of in device getting to know. those strategies revolve around developing and schooling synthetic neural networks, that are computational models that mimic the capabilities and structure of the human brain, to analyze huge datasets and make predictions or decisions based totally on the *** of the key challenges in actual-time information analysis is being capable of method incoming information fast and efficiently, as well as adapt to converting data styles or sudden occasions. traditional device learning algorithms are frequently restricted in their ability to deal with these demanding situations, that is in which deep gaining knowledge of techniques are available *** getting to know techniques contain education neural networks with a couple of layers, every of which strategies one-of-a-kind aspects of the data. This lets in for greater complex and accurate evaluation of the facts, in addition to the potential to handle larger and more numerous datasets. One famous method in deep studying is known as convolutional neural networks (CNNs). Those networks are specifically properly-suitable for studying visual information, including snap shots, by means of breaking down the data into smaller parts and studying them at one of a kind ranges of abstraction. This permits for the identification of patterns and features in the data which could then be used for category or prediction responsibilities.

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The research and design on hardware in the loop of bus-based automotive body control with rule description

The research and design on hardware in the loop of bus-based...

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作者： Han, Jiang-Hong Liu, Lei Wei, Xing Xia, Wei School of Computer and Information Hefei University of Technology Hefei 230009 China Computer Science and Technology Post-doctoral Research Center Hefei University of Technology Hefei 230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei 230009 China

ISBN: (纸本)9789881725592

Oriented the design procedures of bus-based automotive body control system, rules are used to describe the control intention of prototype model first. This paper represents the approach of modeling bus-based automotive body control system with rules description, and designed hardware-in-the-loop system based on distributed control, which merges virtual reality into physical simulation on vehicle. The HILS can verify the reliability and safety in the system modeling phase. We made HIL simulation using typical case, and the results shows that HIL approach could quantify the system time delay which affects the reliability, and improve the robustness of body control system. © 2011 Chinese Assoc of Automati.

关键词： Hardware-in-the-loop simulation

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Replacement of parameter tuning with simple calculation in adaptive control using “Sigmoid generated fixed point transformation”

Replacement of parameter tuning with simple calculation in a...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Adrienn Dineva József K. Tar Annamária Várkonyi-Kóczy Vincenzo Piuri Department of Information Technologies Università degli Studi di Milano Doctoral School of Computer Science Crema Italy Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary Obudai Egyetem Budapest HU Institute of Mechatronics and Vehicle Engineering Obuda University Budapest Hungary Department of Mathematics and Informatics J. Selye University Komarno Slovakia Department of Information Technologies Università degli Studi di Milano Crema Italy

Lately a systematic method was presented for the generation of whole families of Fixed Point Transformations that can be used in nonlinear adaptive control of Single Input - Single Output (SISO) as well as Multiple Input - Multiple Output (MIMO) systems as alternatives of Lyapunov's direct method. This transformation was called Sigmoid Generated Fixed Point Transformation (SGFPT). This paper introduces new improvements on this alternative approach. It is shown that in contrast to the original Robust Fixed Point Transformation (RFPT), for guaranteeing the global stability of the control, instead of tuning, a simple estimation can be done for one of the adaptive control parameters. The novel method is demonstrated by the adaptive control of a 2 Degree of Freedom (DoF) TORA system. Simulation results validate that the suggested approach is beneficial and ensures satisfactory performance.

关键词： Tuning Adaptive control Trajectory Robots Mathematical model

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Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

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Journal of Biomedical Semantics 2011年第5期2卷 1-12页

作者： Rebholz-Schuhmann, Dietrich Yepes, Antonio Jimeno Li, Chen Kafkas, Senay Lewin, Ian Kang, Ning Corbett, Peter Milward, David Buyko, Ekaterina Beisswanger, Elena Hornbostel, Kerstin Kouznetsov, Alexandre Witte, René Laurila, Jonas B Baker, Christopher JO Kuo, Cheng-Ju Clematide, Simone Rinaldi, Fabio Farkas, Richárd Móra, György Hara, Kazuo Furlong, Laura I Rautschka, Michael Neves, Mariana Lara Pascual-Montano, Alberto Wei, Qi Collier, Nigel Chowdhury, Md Faisal Mahbub Lavelli, Alberto Berlanga, Rafael Morante, Roser Van Asch, Vincent Daelemans, Walter Marina, José Luís van Mulligen, Erik Kors, Jan Hahn, Udo European Bioinformatics Institute EMBL Outstation Hinxton Cambridge CB10 1SD United Kingdom Erasmus University Medical Center Dept. of Medical Informatics Rotterdam Netherlands St. John's Innovation Centre Linguamatics Ltd Cambridge United Kingdom Friedrich-Schiller-Universität Language and Information Engineering (JULIE) Lab Jena Germany University of New Brunswick Dept. of Computer Science and Applied Statistics Canada Concordia University Dept. of Computer Science and Software Engineering Montreal Canada Academia Sinica Institute of Information Science Taipei 115 Taiwan University of Zürich Switzerland Hungarian Academy of Sciences Research Group on Artificial Intelligence Hungary Nara Institute of Science and Technology Nara Japan IMIM (Hospital del Mar Research Institute) Universitat Pompeu Fabra Research Programme on Biomedical Informatics (GRIB) Barcelona Spain National Center for Biotechnology-CSIC Madrid Spain National Institute of Informatics Tokyo Japan Fondazione Bruno Kessler Trento Italy Universitat Jaume I Spain University of Antwerp CLiPS Belgium Complutense University of Madrid Spain

Background: Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions. Results: All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I. The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants' solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and P

关键词： Chemical Entity Cosine Similarity Semantic Type Semantic Group Annotate Corpus

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Enhancing Supervised Classifications with Metamorphic Relations

Enhancing Supervised Classifications with Metamorphic Relati...

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IEEE/ACM International Workshop on Metamorphic Testing (MET)

作者： Liming Xu Dave Towey Andrew P. French Steve Benford Zhi Quan Zhou Tsong Yueh Chen International Doctoral Innovation Centre University of Nottingham Ningbo China Ningbo Zhejiang China AIOP Group University of Nottingham Ningbo China Ningbo Zhejiang China School of Computer Science University of Nottingham Nottingham UK School of Computing and Information Technology University of Wollongong Wollongong NSW Australia Dept. of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia

We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enhancements to an image classification problem of images containing hidden visual markers (“Artcodes”). Working on an original classifier, and using the characteristics of two different categories of images, two MRs, based on separation and occlusion, were used to improve the performance of the classifier. Our experimental results show that the MR-augmented classifier achieves better performance than the original classifier, algorithms, and extending the use of MRs beyond the context of software testing.

关键词： Machine learning Testing Software Forestry Histograms Visualization computer science

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