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arXiv 2020年

作者： Hemdan, Ezz El-Din Shouman, Marwa A. Karar, Mohamed Esmail Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Computer Engineering College of Computing and Information Technology Shaqra University Saudi Arabia

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework;namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. The worst classification performance is obtained for the InceptionV3 model with f1-scores of 0.67 for normal cases and 0.00 for COVID-19 cases. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work. Copyright © 2020, The Authors. All ri

关键词： Coronavirus

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KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation

arXiv

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arXiv 2021年

作者： Khiarak, Jalil Nourmohammadi Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given with MobileNetV2 for the ImageNet dataset and fine-tune it on the iris recognition and localization domain. Further, we have introduced a new dataset, called KartalOl, to better evaluate detectors in iris recognition scenarios. To provide domain adaptation, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also augment the data by performing left-right flips, rotation, zoom, and brightness. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed method is tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Investigation of the hyperchaos and control in the fractional order financial system with profit margin

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Partial Differential Equations in Applied Mathematics 2024年 9卷

作者： Johansyah, Muhamad Deni Sambas, Aceng Qureshi, Sania Zheng, Song Abed-Elhameed, Tarek M. Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Department of Mathematics Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Department of Mathematics Near East University Mersin 99138 Turkey Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro 72060 Pakistan School of Data Science Zhejiang University of Finance and Economics Hangzhou 310018 China Institute of Mathematics and Interdisciplinary Sciences Zhejiang University of Finance and Economics 310018 China Department of Mathematics Faculty of Science Assiut University Assiut 71516 Egypt Centre for Control Systems Vel Tech University Avadi Tamil Nadu Chennai 600062 India Institute of Strategic Industrial Decision Modelling School of Quantitative Sciences Universiti Utara Malaysia Sintok Bukit Kayu Hitam Kedah 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman

This research study proposes a novel hyperchaotic finance system with profit margin and then utilizes the Adomian Decomposition Method (ADM) to tackle the solution of the associated Caputo-derivative based fractional-order hyperchaotic finance system with profit margin. Numerical simulations and analyses, such as the evaluation of Lyapunov exponents (LE), the creation of bifurcation diagrams (BD), the complexity analysis (CA), and the 0-1 test, are used to get a full picture of the system. The results of our study demonstrate the occurrence of periodic, chaotic, and hyperchaotic dynamics in the system. Furthermore, the overall criteria for the existence and the uniqueness of the exact solutions for Caputo fractional-order models are presented. In addition, we present a control methodology for the fractional-order hyperchaotic financial system utilizing the Laplace transform and linear feedback control. Significantly, our research showcases a noteworthy correlation between the analytical results and numerical simulations, emphasizing the soundness and effectiveness of our methodology. © 2024 The Author(s)

关键词： Chaotic system Financial system Fractional order Hyperchaos

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Classification of hand movements of stroke patients using combination of statistical features and artificial neural network

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AIP Conference Proceedings 2023年第1期2564卷

作者： S. K. Narudin Noorhamizah Mohamed Nasir Ariffuddin Joret N. Fuad B. S. K. K. Ibrahim M. S. Huq Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia Mechatronic and Control Research Group (MCRG) Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia Automation and Robotic Mechanical Automotive and Manufacturing (MAM) Faculty of Engineering Environment and Computing Coventry University Priory Street Coventry CV1 5FB United Kingdom Robotics Engineering School of Computing and Digital Media London Metropolitan University London United Kingdom

The data used in this study is collected using EEG and the movements are extracted from a stroke subject. In this research, a stroke patient’s brainwave activity with left and right hand movements is used. A statistical feature based system for hand movement classification by using electroencephalogram (EEG) is proposed in this paper. The mean, variance, and standard deviation of the beta band of the stroke patients are used as the input of the classification of hand movement. This work aims to recognize and classify hand movement activities using statistical features. The classification algorithm used in this work is the Artificial Neural Network (ANN). The accuracy value will be obtained from the subject using 10 hidden nodes. Based on the results, the combination of mean with standard deviation and variance with standard deviation yield the highest accuracy value of 78% while the combination of all feature components resulting only 72% accuracy.

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Prototype Model of an IoT-based Digital and Smart Door Locking System with Enhanced Security

Prototype Model of an IoT-based Digital and Smart Door Locki...

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International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： M. Adil Khan Kashif Nisar Ehtisham Lodhi Jawad Khan Muhammad Imran Khan Tariq Rahim Soomro Sana Nisar Department of Computer Science and Technology Chang'an University Xi'an China Victorian Institute of Technology (VIT) Adelaide South Australia The SKL for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department Computer Science Abbottabad University of Science and Technology Havelian KP Pakistan Department of Computer Science Abdul Wali Khan University Mardan Pakistan CCSIS Institute of Business Management Karachi Pakistan Faculty of Computing and Informatics University Malaysia Sabah Jalan UMS Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9781665460729

Biometric-based digital door locking system plays a significant role in providing authentication, reducing the workforce in smart homes, and building automation scenarios. This paper proposes a prototype model of an IoT-based Smart Door Locking System (SDLS) with reliable double-access authentication. The IoT and biometric technology authentication mechanism is adopted to make SDLS more secure and reachable with the alert mechanism on unauthorized access. The SDLS contains a biometric scanner, and a thumb impression is made either from a mobile or a biometric-mounted device on the door and both are connected to the centralized cloud-hosted real-time database. The proposed system was tested in real-time and has shown competitive results compared to other Radio-Frequency Identification (RFID) and password-based door locks. Hence, the overall performance efficiency of SDLS is satisfactory and provides biometric-based remote access to control and monitor door locks with the alert system on unauthorized access.

关键词： Biometrics (access control) Biological system modeling Authentication Prototypes Smart homes Real-time systems Security

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Erratum to: Expanded Personality as the Main Entity and Subject of Philosophical Analysis: Implications for Education

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Doklady Mathematics 2024年第1期109卷 100-100页

作者： Semenov, A. L. Ziskin, K. E. Faculty of Mechanics and Mathematics Moscow State University Moscow Russia Axel Berg Institute of Cybernetics and Educational Computing Federal Research Center “Computer Science and Control” Russian Academy of Sciences Moscow Russia National Research University Higher School of Economics Moscow Russia Autonomous Nonprofit Organization of Additional Professional Education Higher School of Economics Moscow Russia

An Erratum to this paper has been published: https://***/10.1134/S1064562424010010

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Effect of CO2 increase on ecological parameters of plant ecosystems of Central and South America countries

Effect of CO2 increase on ecological parameters of plant eco...

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2019 International Conference on Advances in Energy Systems and Environmental Engineering, ASEE 2019

作者： Tarko, Alexander Kurbatova, Anna Llerena, Silvia Dorodnitsyn Computing Center of the Federal Research Center Computer Science and Control Russian Academy of Sciences Vavilova Street 40 Moscow119991 Russia Faculty of Ecology 6 Miklukho-Maklaya Street Moscow117198 Russia

The carbon sink capacity of the forest makes forest conservation a mitigation mechanism against climate change. Therefore, carbon sequestration studies allow to assess forest productivity and control carbon budgets. Thus, the changes in environmental parameters (carbon amount in humus and phytomass) as effect of the increase of CO2 emissions, deforestation and erosion were calculated for countries of the Central and South between 2000 and 2060 through the Spatial Mathematical Model of the Global Carbon Dioxide Cycle in the biosphere. The majority of countries show a decrease in total carbo of humus and phytomass between 2000 and 2010, which is attributed to the extreme deforestation that they suffered. Subsequently, there is a moderate increase in countries that has better polices in favour of environmental protection. Finally, in all countries an exponential increase until 2060 is observed, which is expected thanks to forest conservation, reforestation policies and the compensatory effect between the atmosphere and forests. The highest values of total carbon belong to one country in the Caribbean (Belize with 21,5%). This analysis is useful for planning environmental practices in order to increase carbon storage. © The Authors, published by EDP Sciences, 2019.

关键词： Deforestation

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Modeling the global biogeochemical cycle of carbon and nitrogen in the system atmosphere-plant-soil

Modeling the global biogeochemical cycle of carbon and nitro...

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2019 International Conference on Advances in Energy Systems and Environmental Engineering, ASEE 2019

作者： Kurbatova, A.I. Tarko, A.M. Kozhevnikova, P.V. Faculty of Ecology 6 Miklukho-Maklaya Street Moscow117198 Russia Dorodnitsyn Computing Center of the Federal Research Center Computer Science and Control Russian Academy of Sciences Vavilova Street 40 Moscow119991 Russia

The modeling of the global biogeochemical cycle of carbon and nitrogen against the background of global warming and other anthropogenic impacts on the biosphere is carried out. It is shown that in the process of absorption of anthropogenic CO2 emissions into the atmosphere during global warming, the nitrogen cycle is significantly intensified and contributes to more efficient absorption of emissions. The greater the dependence of productivity on nitrogen nutrition, the bigger the degree of carbon absorption and, thus, the larger the degree of compensation of the investigated effects. Generally, soil absorbs a significant share of carbon dioxide emissions and therefore holds much significance in absorption. The role of litter is relevant in absorbing emissions. Having 10 times less mass than humus, it absorbs more carbon than humus. © The Authors, published by EDP Sciences, 2019.

关键词： Global warming

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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Chinese Automation Congress (CAC)

作者： Qing Zhang Tingyang Jiao Yinxing Ma Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China The Faculty of Computing Science The University of Alberta Edmonton Alberta Canada Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’hai China Xi’an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system.

关键词： Analytical models Temperature distribution Brushes Neural networks Predictive models Market research Generators

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