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SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

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Multiple Kernel Based Regularized System Identification with SURE Hyper-parameter Estimator

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IFAC-PapersOnLine 2018年第15期51卷 13-18页

作者： Hong, Shiying Mu, Biqiang Yin, Feng Andersen, Martin S. Chen, Tianshi School of Science and Engineering and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Hong Kong Department of Electrical Engineering Linköping University Linköping Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark

In this work, we study the multiple kernel based regularized system identification with the hyper-parameter estimated by using the Stein's unbiased risk estimators (SURE). To approach the problem, a QR factorization is first employed to compute SURE's objective function and its gradient in an efficient and accurate way. Then we propose an algorithm to solve the SURE problem, which contains two parts: the outer optimization part and the inner optimization part. For the outer optimization part, the coordinate descent algorithm is used and for the inner optimization part, the projection gradient algorithm is used. Finally, the efficacy of the proposed algorithm is demonstrated by numerical simulations. © 2018

关键词： Parameter estimation

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Literature Review on Wireless Sensing Wi-Fi Signal-Based Recognition of Human Activities

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Tsinghua science and Technology 2018年第2期23卷 203-222页

作者： Chao Wang Siwen Chen Yanwei Yang Feng Hu Fugang Liu Jie Wu Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai 200444 China. Science and Technology Heilongjiang University Harbin 150022 China. Department of Computer and InformationSciences Temple University PA 19122-6096 USA.

With the rapid development and wide deployment of wireless technology, Wi-Fi signals have no longer been confined to the Internet as a communication medium. Wi-Fi signals will be modulated again by human actions when propagating indoors, carrying rich human body state information. Therefore, a novel wireless sensing technology is gradually emerging that can realize gesture recognition, human daily activity detection, identification,indoor localization and human body tracking, vital signs detection, imaging, and emotional recognition by extracting effective feature information about human actions from Wi-Fi signals. Researchers mainly use channel state information or frequency modulated carrier wave in their current implementation schemes of wireless sensing technology, called "Walls have eyes", and these schemes cover radio-frequency technology, signal processing technology, and machine learning. These available wireless sensing systems can be used in many applications such as smart home, medical health care, search-and-rescue, security, and with the high precision and passively device-free through-wall detection function. This paper elaborates the research actuality and summarizes each system structure and the basic principles of various wireless sensing applications in detail. Meanwhile, two popular implementation schemes are analyzed. In addition, the future diversely application prospects of wireless sensing systems are presented.

关键词： channel state information frequency modulated carrier wave human activities recognition wireless sensing applications Wi-Fi

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From data to Evidence: Multi-Source Evidence Association Model Based on Bayesian Network

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Li Zhang Feng Xu Ming Liu Tong Cui School of Public Administration & Communication Beijing Information Science and Technology University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China China Judicial Big Data Research Institute Beijing China

Recent years have witnessed the boom of artificial intelligence. With the advancement of judicial information, using artificial intelligence (AI) technologies to mine judicial big data is of great significance in smart courts. However, the reasoning of the evidence chain mainly relies on the judge's manual work in the litigation process. How to model the multi-source evidence association (MSEA) and reason credible evidence chains (CEC) automatically is largely unexplored. In this paper, we propose an MSEA model based on the Bayesian network. Firstly, we construct an MSEA network in which each evidence element serves as a node, and the node correlation probability is calculated via the association relationship among the evidence elements. Subsequently, with the guidance of the event judgment chain, the MSEA model is constructed based on Bayesian networks. In the end, we use a genetic algorithm to optimize the Bayesian network and select credible evidence chains. To the best of our knowledge, this is the first time that using a probability graph model to mining the association of multi-source evidence. Experiments and the case analysis prove the effectiveness of our method.

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FAIR Principles:Interpretations and Implementation Considerations

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data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

arXiv

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： data mining

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Privacy-preserving Artificial Intelligence Techniques in Biomedicine

arXiv

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arXiv 2020年

作者： Torkzadehmahani, Reihaneh Nasirigerdeh, Reza Blumenthal, David B. Kacprowski, Tim List, Markus Matschinske, Julian Späth, Julian Wenke, Nina Kerstin Bihari, Béla Frisch, Tobias Hartebrodt, Anne Hauschild, Anne-Christin Heider, Dominik Holzinger, Andreas Hötzendorfer, Walter Kastelitz, Markus Mayer, Rudolf Nogales, Cristian Pustozerova, Anastasia Röttger, Richard Schmidt, Harald H.H.W. Schwalber, Ameli Tschohl, Christof Wohner, Andrea Baumbach, Jan Chair of Experimental Bioinformatics Technical University of Munich Freising Germany Division of Data Science in Biomedicine Peter L. Reichertz Institute for Medical Informatics TU Braunschweig and Hannover Medical School Braunschweig Germany Gnome Design SRL Sfântu Gheorghe Romania Institute of Mathematics and Computer Science University of Southern Denmark Odense Denmark Department of Mathematics and Computer Science Philipps-University of Marburg Marburg Germany Institute for Medical Informatics/Statistics Medical University Graz Graz Austria Research Institute AG & Co KG Vienna Austria SBA Research Gemeinnützige GmbH Vienna Austria Department of Pharmacology and Personalised Medicine MeHNS FHML Maastricht University Maastricht Netherlands Concentris Research Management GmbH Fürstenfeldbruck Germany

Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals’ privacy. This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead. © 2020, CC BY.

关键词： Artificial intelligence

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Sunrise iii: Overview of Observatory and Instruments

arXiv

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arXiv 2025年

作者： Korpi-Lagg, Andreas Gandorfer, Achim Solanki, Sami K. del Toro Iniesta, Jose Carlos Katsukawa, Yukio Bernasconi, Pietro Berkefeld, Thomas Feller, Alex Riethmüller, Tino L. Álvarez-Herrero, Alberto Kubo, Masahito Pillet, Valentín Martínez Smitha, H.N. Suárez, David Orozco Grauf, Bianca Carpenter, Michael Bell, Alexander Álvarez-Alonso, María-Teresa García, Daniel Álvarez del Moral, Beatriz Aparicio Ayoub, Daniel Bailén, Francisco Javier Martínez, Eduardo Bailón Jiménez, Maria Balaguer Barthol, Peter Laguna, Montserrat Bayon Rubio, Luis Bellot R. Bergmann, Melani Rodríguez, Julian Blanco Bochmann, Jan Borrero, Juan Manuel Campos-Jara, Antonio Durán, Juan Sebastián Castellanos Cebollero, María Rodríguez, Aitor Conde Deutsch, Werner Eaton, Harry Fernández-Medina, Ana Belen Fernandez-Rico, German Ferreres, Agustin García, Andrés Alarcia, Ramón María García Parejo, Pilar García Garranzo-García, Daniel Blesa, José Luis Gasent Gerber, Karin Germerott, Dietmar Palmer, David Gilabert Gizon, Laurent Sánchez-Tirado, Miguel Ángel Gómez Gonzalez, David Melchor, Alejandro Gonzalo Goodyear, Sam Hara, Hirohisa Harnes, Edvarda Heerlein, Klaus Heidecke, Frank Heinrichs, Jan Expósito, David Hernández Hirzberger, Johann Hoelken, Johannes Hyun, Sangwon Iglesias, Francisco A. Ishikawa, Ryohtaroh T. Jeon, Minwoo Kawabata, Yusuke Kolleck, Martin Laguna, Hugo Lomas, Julian Jiménez, Antonio López C. Manzano, Paula Matsumoto, Takuma Turrado, David Mayo Meierdierks, Thimo Meining, Stefan Monecke, Markus Morales-Fernández, José Miguel Mantas, Antonio Jesús Moreno Vacas, Alejandro Moreno Müller, Marc Ferenc Müller, Reinhard Naito, Yoshihiro Nakai, Eiji Peral, Armonía Núñez Oba, Takayoshi Palo, Geoffrey Pérez-Grande, Isabel Carreño, Javier Piqueras Preis, Tobias Przybylski, Damien Noda, Carlos Quintero Ramanath, Sandeep Más, Jose Luis Ramos Raouafi, Nour Rivas-Martínez, María-Jesús Martínez, Pedro Rodríguez Valido, Manuel Rodríguez Cobo, Basilio Ruiz Rodríguez, Antonio Sánchez Gómez, Antonio Sánchez Kilders, Esteban Sanchis Sant, Kamal Guerrero Max-Planck-Institut für Sonnensystemforschung Justus-von-Liebig-Weg 3 Göttingen37077 Germany Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía s/n Granada18008 Spain National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Johns Hopkins Applied Physics Laboratory 11100 Johns Hopkins Road LaurelMD United States Georges-Köhler-Allee 401a Freiburg79110 Germany National Solar Observatory 3665 Discovery Drive BoulderCO80303 United States Spain Ctra. de Ajalvir km. 4 Torrejón de ArdozE-28850 Spain Universitat de Valencia Catedrático José Beltrán 2 Paterna ValenciaE-46980 Spain Plaza Cardenal Cisneros 3 MadridE-28040 Spain Instituto de Astrofísica de Canarias Vía Láctea s/n La LagunaE-38205 Spain Universidad de La Laguna La LagunaE-38205 Spain Grupo de Estudios en Heliofísica de Mendoza CONICET Universidad de Mendoza Boulogne sur Mer 683 Mendoza5500 Argentina Department of Earth and Planetary Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Centre for Integrated Data Science Institute for Space-Earth Environmental Research Nagoya University Furocho Chikusa-ku Aichi Nagoya464-8601 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo-ku Kanagawa Sagamihara252-5210 Japan Aalto University Department of Computer Science Konemiehentie 2 Espoo02150 Finland Dept. Optical Instrumentation Development Korea Basic Science Institute 169-148 Gwahak-ro Yuseong-gu Daejeon Korea Republic of National Institute for Fusion Science 322-6 Oroshi-cho Toki City509-5292 Japan Institut für Astrophysik und Geophysik Georg-August-Universität Göttingen Göttingen37077 Germany 2-21-1 Osawa Mitaka Tokyo1818588 Japan IRIS-2 Instrument Development Team Spain Technical University of Munich Department of Aerospace and Geodesy Lise-Meitner-Straße 9 Ottobrunn85521 Germany Satlantis Microsats S.A. Edif. Sede 2ª planta Par

In July 2024, Sunrise completed its third successful science flight. The Sunrise iii observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013, to tackle the most recent science challenges concerning the solar atmosphere. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit-based spectropolarimeters, the Sunrise UV Spectropolarimeter and Imager (SUSI) and the Sunrise Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet (309–417 nm) and the near-infrared (765–855 nm) regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TuMag) simultaneously obtains maps of the full field-of-view of 46 × 46 Mm2 in the photosphere and the chromosphere in the visible (525 and 517 nm). The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLiD), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005". A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. Sunrise iii was launched successfully on July 10, 2024, from the Esrange Space Center of the Swedish Space Corporation near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of t

关键词： Polarimeters

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Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

arXiv

引用

arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial networks (GAN), we propose a Semi-supervised Classification using Limited Labelled data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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An Improved Association Rule Mining Algorithm Based on Ant Lion Optimizer Algorithm and FP-Growth

An Improved Association Rule Mining Algorithm Based on Ant L...

引用

IEEE International Conference on Intelligent data Acquisition and Advanced Computing Systems

作者： Dawei Dong Zhiwei Ye Yu Cao Shiwei Xie Fengwen Wang Wei Ming School of Computer Science Hubei University of Technology China Fujian Provincial Big Data Research Institute of Intelligent Manufacturing Wuhan Fiberhome Technical Services Co. Ltd. Wuhan Hubei China

Discovering knowledge from the amount of data plays an important role in the era of big data and FP-Growth algorithm is one of the most successful methods for learning association rules. Though the FP-Growth algorithm only needs scan two times, it has a poor efficiency for large datasets. There are already efforts have been made to solve the problem by using some Meta-heuristic optimization algorithms, such as particle swarm optimization algorithm (PSO), immune algorithms etc, which outperform the traditional FP-Growth algorithm and shows strong performance. However, PSO is easy to trap in the local optimums. A novel algorithm ant lion optimizer (ALO) was proposed and with the advantages of global optimization, good robustness, and high convergence accuracy, which was applied to many engineering fields like antenna array synthesis, integrated process planning, scheduling and so on. In the paper, a novel association rule extraction algorithm is put forward based on the ant lion optimization algorithm. A new fitness schema based on confidence and support has been used in this approach, which avoids part of unnecessary searching processes of the FP-Growth algorithm and leads the method of searching the optimization solution more effectively. In order to evaluate the effectiveness of our approach, experiments on various datasets are carried out and experimental results are compared with some other classical meta-heuristic algorithms, experimental results testify the performance of the proposed method.

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