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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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Study of the transmittance properties for the stacked metallic sub-wavelength slit arrays

Study of the transmittance properties for the stacked metall...

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International Conference on Infrared and Millimeter Waves

作者： M. Nakajima H. Kitahara S. Watanabe K. Sakaguchi K. Akiyama Y. Tokuda Institute of Laser Engineering Osaka University Suita Osaka Japan Faculty of Computer Science and System Engineering Okayama Prefectural University Soja Okayama Japan Advanced Technology R&D Center Mitsubishi Electric Corporation Amagasaki Hyogo Japan

ISBN: (数字)9781728166209

ISBN: (纸本)9781728166216

The transmittance properties for the stacked metallic sub-wavelength slit arrays were experimentally and numerically investigated. The calculated spectra based on the FDTD method well reproduced the experimental spectra. The details of the airgap space dependence were measured, and the characteristic behavior of multiple optical modes are observed.

关键词： Optical variables measurement Finite difference methods

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Sustainability nexus analytics, informatics, and data (AID): Drought

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Sustainability Nexus Forum 2024年第1期32卷 1-12页

作者： Huning, Laurie S. Bateni, Sayed M. Hayes, Michael Ho, Sarah Quynh-Giang Jayasinghe, Susantha Kumar, Rohini Lima, Carlos Love, Charlotte A. Madani, Kaveh Markonis, Yannis Matin, Mir A. Miao, Chiyuan Motagh, Mahdi Naeger, Aaron de Oliveira, Debora Yumi Read, Laura K. Samaniego, Luis Shokri, Nima Shukla, Shraddhanand Soltanian, Reza Stefanski, Robert Trabelsi, Fatma Tsegai, Daniel Vo, Linh U. C. Wanders, Niko Wens, Marthe Zarei, Azin AghaKouchak, Amir Department of Civil Engineering and Construction Engineering Management California State University Long Beach Long Beach USA Department of Civil and Environmental Engineering University of California Irvine Irvine USA Department of Civil Environmental and Construction Engineering and Water Resources Research Center University of Hawaii Honolulu USA UNESCO-UNISA Africa Chair in Nanoscience and Nanotechnology College of Graduates Studies University of South Africa Pretoria South Africa School of Natural Resources University of Nebraska-Lincoln Lincoln USA Karlsruhe Institute of Technology (KIT) Karlsruher Germany Asian Disaster Preparedness Center (ADPC) Bangkok Thailand Helmholtz Centre for Environmental Research - UFZ Leipzig Germany University of Brasilia Brasília Brazil United Nations University Institute for Water Environment and Health (UNU-INWEH) Richmond Hill Canada United Nations University Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES) Dresden Germany Faculty of Environmental Sciences Czech University of Life Sciences Prague Prague Czech Republic Beijing Normal University Beijing China Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Potsdam Germany Institute of Photogrammetry and Geoinformation Leibniz University Hannover Hannover Germany NASA Marshall Space Flight Center Huntsville USA Upstream Tech Alameda USA Institute of Environmental Science and Geography University of Potsdam Potsdam Germany Institute of Geo-Hydroinformatics Hamburg University of Technology Hamburg Germany United Nations University Hub on Engineering to Face Climate Change at the Hamburg University of Technology United Nations University Institute for Water Environment and Health (UNU-INWEH) Hamburg Germany Climate Hazards Center University of California Santa Barbara USA Departments of Geosciences and Environmental Engineering University of Cincinnati Cincinnati USA World Meteorological Organization Geneva

Drought occurs globally and can have deleterious effects on built and natural systems and societies. With the increasing human footprint on our planet, so has increased the anthropogenic influence on drought and water scarcity, leading to the development of notions of “anthropogenic drought” and “water bankruptcy”. Understanding the human dimension of drought is complex and requires a data-driven nexus approach to better understand the involved processes and address the implications of water deficits around the world. Just as it transcends scales and geographical boundaries, drought is neither restricted to a single hydrologic state in the water cycle nor are its effects confined to one sector. Drought impacts the water, energy, and food sectors, ecosystem services, socioeconomics, public policy, politics, etc. from local to regional and global scales. We argue that drought mitigation strategies and policy developments must be addressed with a multidisciplinary perspective that benefits from a nexus approach rooted in analytics, informatics, and data (AID). The United Nations University (UNU) Sustainability AID Programme employs such an approach to aid the monitoring, forecasting, and projection of drought, both from climatic and anthropogenic perspectives, and its multifaceted impacts across a variety of sectors and spatiotemporal scales. After a broad overview of this UNU Programme’s vision, and to support stakeholders and decision-makers, we present a drought resource database for drought-related information, data, and analysis tools. Our aim is not to compile an exhaustive list of all available data and tools. Instead, we prioritize mature datasets and AID tools while actively highlighting opportunities to develop new data and tools, fostering nexus research.

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Face recognition system Using Deep Neural Network with Convolutional Neural Networks 3

Face recognition system Using Deep Neural Network with Convo...

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3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Fernando, Erick Andwiyan, Denny Fitria Murad, Dina Touriano, Derist Irsan, Muhamad Information System Department School of Information Systems Bina Nusantara University Indonesia School of Information Technology Indonesia Information System Department Binus Online Learning Bina Nusantara University Indonesia Information Technology Department Faculty of Computer Science and Engineering University of Adi Wangsa Indonesia

Face recognition has long been a hot topic and challenging research point in areas such as image processing, pattern recognition, and machine vision. The face is a biometric feature with the intrinsic nature of a human. So that the face has self-stability, deep individual differences and can be an ideal basis for verification of an identity. In this research use, Deep Learning Network method uses to perform detection or face recognition. In this study, we present a framework that can be used to detect faces. This research is also able to present a DNN model that is used to study data sources from the data stream in sequence. The most important part of this study is able to adjust the capacity of the model from the simple one. This research uses experimental design method. The first step is a collection of face image data. Then the architecture design starts from the determination of the depth of the network, layout layers, and the selection of layer types that will be used to get the model based on input dataset and label name index. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Employee Recruitment Recommendation Using Profile Matching and Naïve Bayes

Employee Recruitment Recommendation Using Profile Matching a...

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International Conference on Sustainable Information engineering and Technology (SIET)

作者： Fitra A. Bachtiar Fajar Pradana Rizkia Desi Yudiari Faculty of Computer Science Intelligent System Laboratory Brawijaya University Malang Indonesia Faculty of Computer Science Software Engineering Laboratory Brawijaya University Malang Indonesia Faculty of Computer Science Brawijaya University Malang Indonesia

Finding the right employee in the employee recruitment process in an organization is a challenging tasks. For example, enormous candidates participated in the selection, various parameters to be examined from the candidates. Furthermore, the selection process done manually could take long time during the selection process. In addition, there is a case that sometimes the position of the employee's work is not suitable with the strengths and weaknesses of the candidates. Moreover, in some cases using Curriculum Vitae (CV), Interview, and Assessment Test could not give the optimal selection results. There is a need to overcome this problems to create more comprehensive and could suggest the employee placement based on their characteristics. In this study, Profile Matching is used to comprehensively assess the candidates. In addition, Naïve Bayes is used in the next step to determine or predict the employee placement based on their characteristics. Validation is conducted by performing bivariate correlation to evaluate the Profile Matching and accuracy is used to evaluate the suitability of the employee placement. The results shows that there is a positive correlation with the value of 0.93. Meanwhile, the prediction using Naïve Bayes model shows a 100% accuracy to predict employee placement.

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EEG Neurofeedback Training Among Adult with Attention Deficit: A Review Article

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IOP Conference Series: Materials science and engineering 2021年第1期1077卷

作者： Eshrak Alaros Dini Oktarina Dwi Handayani Hamwira Yaacob Muharman Lubis School of Computer Science & Engineering Faculty of Innovation & Technology Taylor's University Selangor Malaysia Computer Science Department Kulliyyah of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia Information System Faculty of Industrial Engineering Telkom University Indonesia

Neurofeedback is considered a promising therapy for boosting cognitive skills, which 10% of the US medical sector is using as a new training method among patients. Attention is a significant mechanism for learning and is essential for life in general. As the person grows up the level of responsibilities also increases, which needs high concentration in doing normal activities. To succeed in any activity, neurofeedback therapy needs to check brain waves to locate the problem in the brain and train it. This paper aims to review the attention deficit treatment based on neurofeedback using a systematic review method. The results found that the common features are the area of EEG (Frontal, central) in the brain, type of tools and software, time of training and the impact upon the trainer. Neurofeedback showed improvement of brain attention-ability for a longer time as well as reduction in recovery time in case of a disturbance interruption.

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Spaces of innovation and venture formation: the case of biotech in the United Kingdom

arXiv

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arXiv 2023年

作者： Marzolla, Francesco Nowak, Przemyslaw Sahasrabuddhe, Rohit Singh, Chakresh Straccamore, Matteo Zhivkoplias, Erik Arcaute, Elsa SONY Computer Science Laboratories Rome Via Panisperna 89A Rome00184 Italy Sapienza Univ. of Rome Physics Dept. Piazzale Aldo Moro 2 Rome00185 Italy Warsaw University of Technology Faculty of Physics ul. Koszykowa 75 Warsaw00-662 Poland Mathematical Institute University of Oxford United Kingdom Université Paris Cité Inserm System Engineering and Evolution Dynamics ParisF-75004 France Centro Ricerche Enrico Fermi Via Panisperna 89/A Rome00184 Italy Stockholm Resilience Centre Stockholm University Stockholm106 91 Sweden Centre for Advanced Spatial Analysis University College London United Kingdom

Patents serve as valuable indicators of innovation and provide insights into the spaces of innovation and venture formation within geographic regions. In this study, we utilise patent data to examine the dynamics of innovation and venture formation in the biotech sector across the United Kingdom (UK). By analysing patents, we identify key regions that drive biotech innovation in the UK. Our findings highlight the crucial role of biotech incubators in facilitating knowledge exchange between scientific research and industry. However, we observe that the incubators themselves do not significantly contribute to the diversity of innovations which might be due to the underlying effect of geographic proximity on the influences and impact of the patents. These insights contribute to our understanding of the historical development and future prospects of the biotech sector in the UK, emphasising the importance of promoting innovation diversity and fostering inclusive enterprise for achieving equitable economic growth. © 2023, CC BY.

关键词： Digital storage

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Hybrid cryptography and steganography method to embed encrypted text message within image 1

Hybrid cryptography and steganography method to embed encryp...

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1st International Conference computer science and engineering, IC2SE 2019

作者： Nassif Jassim, Khider Khudhur Nsaif, Ahmed Kuder Nseaf, Asama Hazidar, Al Hamidy Priambodo, Bagus Naf'An, Emil Masril, Mardhiah Handriani, Inge Pratama Putra, Zico Department of Statistics Faculty of Management and Economics Wasit University Al-Kut Iraq Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Universiti Kebangsaan Malaysia Malaysia Information System Faculty of Computer Science Universitas Mercu Buana Jl. Raya Meruya Selatan Kembangan Jakarta11650 Indonesia Universitas Putra Indonesia YPTK Padang25221 Indonesia School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

The businesses in various fields use the online communication application to gather their data and information with local and global sources. The gathered data may sensitive such as the financial and businesses development information. The hackers or online thief try to stole the valuable data i.e. credit card numbers. The organizations looking for secure online channels in order to transfer their data efficiently and avoid the data thieving. One of the most applicable methods that developed to secure the online transferred data is the cryptography which transfers the original data or information to encrypted formulation. Cryptography still has many drawbacks such as stole and decrypts the original texts using automatic decryption counter. The main aim of this research is to improve the cryptography securing level using supportive method which is Steganography. The Steganography is the processes of hide the data or information in media files such as video, images and audio files. There are four stages represent the methodology of this paper;(1) encrypt the original texts using RSA algorithm, (2) hide the encrypted texts in Image files, (3) extract the encrypted texts from Image files, and (4) decrypt the original texts using decryption key of RSA algorithm. It is expected to improve the security level of the online transferred textual data. The performance of the final results will be evaluated through compare the Image files quality before and after hide the data in these files. The quality of the original and stego Image files need to be same or near in order to maximize the difficulty of detect that there data hide in these files. © Published under licence by IOP Publishing Ltd.

关键词： Cryptography

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Deep learning insights into spatial patterns of stable isotopes in Iran’s precipitation: a novel approach to climatological mapping

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Isotopenpraxis Isotopes in Environmental and Health Studies 2024年第4期60卷

作者： Mojtaba Heydarizad Rogert Sori Masoud Minaei Hamid Ghalibaf Mohammadabadi Elham Mahdipour a State Key Laboratory of Marine Geology Tongji University Shanghai People’s Republic of China b Environmental Physics Laboratory (EPhysLab) Centro de Investigación Mariña Universidade de Vigo Ourense Spain c Department of Geography Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Irand Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab) Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Iran e Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran f Computer Engineering Department Khavaran Institute of Higher Education Mashhad Iran

ABSTRACTStable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran’s precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

关键词： Deep learning DNN isotope hydrology LSTM Iran precipitation stable isotopes simulation

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Antenna Array Enabled Space/Air/Ground Communications and Networking for 6G

arXiv

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arXiv 2021年

作者： Xiao, Zhenyu Han, Zhu Nallanathan, Arumugam Dobre, Octavia A. Clerckx, Bruno Choi, Jinho He, Chong Tong, Wen The School of Electronic and Information Engineering Beihang University Beijing100191 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom The Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada The Communications and Signal Processing Group Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Technology Deakin University GeelongVIC3220 Australia The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The Wireless Advanced System Competency Centre Huawei Technologies Co. Ltd. OttawaONK2K 3J1 Canada

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/ai

关键词： Beamforming

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