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Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking

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Optik 2023年 272卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College Of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Vietnam

Recently, biometric technology has been extensively embedded in mobile devices to enhance security of mobile devices. With rise of financial technology (FinTech) that employs mobile applications as well as devices as promotional platforms, biometrics has a significant role in strengthening the detection of this privacy application. This manuscript offers the design of salp swarm optimization with auto-encoder based biometric authentication (SSOAE-BMA) model for the recognition of abnormal activities in the Fintech banking applications based on wireless communication. The major aim of the SSOAE-BMA model lies in the proper authentication of persons via biometric matching process. Initially, the presented SSOAE-BMA model makes use of stacked ResNet-50 model for deriving feature vectors. Next, the SSOAE-BMA model utilizes AE for biometric verification and the performance of the AE model is adjusted using the Social Spider Optimization (SSO) Algorithm which in turn enhances the recognition outcomes. To demonstrate the improved performance of SSOAE-BMA model, a series of simulations were carried out. The experimental outcomes signified the enhancements of the SSOAE-BMA model over existing models.

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Retraction notice to “Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking” [Optik 272 (2023) 170294]

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Optik 2023年 288卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Viet Nam

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Exploring the metabolic landscape of lung adenocarcinoma and squamous cell carcinoma: a total-body [18F]FDG PET/CT approach

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European journal of nuclear medicine and molecular imaging 2025年 2025 Jun 3页

作者： Xuetong Tao Haiyan Wang Jiaxiang Qu Zixiang Chen Yaping Wu Ruohua Chen Jianjun Liu Na Zhang Hairong Zheng Dong Liang Meiyun Wang Zhanli Hu Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China. Key Laboratory of Biomedical Imaging Science and System State Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences Shenzhen 518055 China. Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China. Biomedical Imaging Laboratory (BIG) Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau 999078 Macau SAR China. Department of Medical Imaging Henan Provincial People's Hospital & People's Hospital of Zhengzhou University Zhengzhou 450003 China. Department of Nuclear Medicine Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai 201807 China. Key Laboratory of Biomedical Imaging Science and System State Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences Shenzhen 518055 China. zl.hu@***. Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China. zl.hu@***.

PURPOSE:Cancer is increasingly recognized not just as a localized disease but as a systemic condition with profound impacts on metabolism at both cellular and whole-body levels. This study seeks to unveil the systemic metabolic disruptions in early-stage, untreated lung cancer patients, specifically adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), using a novel network-based approach with total-body static and dynamic PET/CT imaging. By analyzing inter-organ metabolic dependencies, we aim to uncover how lung cancer induces whole-body metabolic reprogramming, providing insights into potential biomarkers for monitoring disease progression and treatment response. METHODS:This retrospective study included 32 early-stage untreated lung cancer patients and 20 healthy volunteers. Static and dynamic total-body PET/CT scans were performed to assess glucose consumption across the body. Twenty-five regions of interest (ROIs) representing major organs were selected, and metabolic status was quantified using the average SUV and Ki values for each ROI. Inter-organ metabolic dependencies were quantified using mutual information (MI), which measures the amount of shared information between two variables, capturing both linear and nonlinear relationships, followed by Bonferroni correction to control for multiple comparisons. Population-level metabolic networks were constructed to visualize alterations in interregional connectivity for ADC, SqCC, and healthy cohorts. Furthermore, to capture personalized metabolic deviations, individual networks were constructed for each cancer patient. RESULTS:The analysis revealed distinct metabolic network patterns in ADC and SqCC patients compared to healthy controls. ADC patients exhibited selective enhancements in metabolic connectivity, particularly between the central nervous system and peripheral organs such as the adrenal glands and pancreas, suggesting activation of compensatory neuroendocrine mechanisms. In contrast, SqCC patients

关键词： Adenocarcinoma Lung cancer Lung cancer metabolism Metabolic network Squamous cell carcinoma Total-body imaging [18F]FDG PET/CT

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Analysis of smart city environment by artificial intelligent techniques

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AIP Conference Proceedings 2023年第1期2831卷

作者： Sujay Mugaloremutt Jayadeva A. K. Gnanasekar Prashant Sunagar N. Harshith Sahil Sanjeev Salvi Ashok Kumar Department of Health System Management Studies JSS Academy of Higher Education & Research (Faculty of Management Studies) Mysuru-570015 India Department of Electronics & Communication Engineering Rajalakshmi Institute of Technology Chembarambakkam Chennai Tamil Nadu-600124 India Department of Civil Engineering M.S. Ramaiah Institute of Technology MSR Nagar Bengaluru Karnataka-560054 India Department of Civil Engineering Pimpri Chinchwad College of Engineering and Research Ravet Pune Maharashtra-412101 India Department of Computer Science Banasthali Vidyapith Banasthali Rajasthan-304022 India

The expectation of artificial intelligence is happened to realize in such a way that it essentially and most peculiarly supports the sustainable enhancement of fore coming smart environment surrounding the cities. The contribution towards the different developing fields of artificial intelligence have been made in determining the transformation towards traditional cities with conventional facilities into fully built smart environmental cities. The knowledge of producing better standards of living by rebuilding technology into daily activities of human is the primary requirements of the smart city emergence with esteemed technologies. In this proposed study, the concepts are enlightened towards few enhanced and developed technologies and identification of solutions to different problems that has been faced by people due to absence of digital environment. It illustrates certain problems that has been related to the smart city infrastructure, security, public safety, which lends suitable solutions for the denoted problems. It not only creates the emphasis on the technique of artificial intelligence but also on the Internet of Things (IoT) applications, pattern recognitions, deep and machine learning, analytics of big data and infrastructures associated with cloud computing in establishing the development as a completely equipped smart city.

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FPGA-based educational system cooperating with mobile application for a learning computer architecture

FPGA-based educational system cooperating with mobile applic...

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作者： Maeda, Hiroyuki Yasutake, Yoshihiro Iihoshi, Azumi Tanaka, Koichiro Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Faculty of Science and Engineering Kyushu Sangyo University Fukuoka Japan

The training of embedded software engineers in educational institutions is required for embedded systems companies. We focus on computer architecture education for learning about the operations and architectures of processors. We use the Kyutech Easily Reconstructive Naked Educational (KERNEL) system, which was designed for computer architecture education. The KERNEL system enables users to observe the internal architecture and states of processors during execution. It helps beginners to learn the basics of a computer architecture by writing their programs into the KERNEL memory so they can observe the processor's execution. This paper describes a field-programmable gate array (FPGA)-based educational system cooperating with mobile applications, and offers a performance evaluation of its wireless communications. The system consists of an FPGA board implemented with one's own K16 model processor that designed for computer hardware educations in the KERNEL system and mobile applications that become a console for setting and displaying the internal state of the processor. It is possible for users to write their programs, set registers in the processor, and observe the internal state of the processor during execution by using a mobile application. Communications between the FPGA and the mobile application uses classic Bluetooth and Bluetooth Low Energy technology. © 2018 The institute of Electronics and Information Engineers.

关键词： computer architecture

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Online robust and adaptive learning from data streams

arXiv

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arXiv 2020年

作者： Fukushima, Shintaro Nitanda, Atsushi Yamanishi, Kenji Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Toyota Motor Corporation 1-6-1 Otemachi Chiyoda-ku Tokyo Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka-shi Fukuoka Japan

In online learning from non-stationary data streams, it is necessary to learn robustly to outliers and to adapt quickly to changes in the underlying data generating mechanism. In this paper, we refer to the former attribute of online learning algorithms as robustness and to the latter as adaptivity. There is an obvious tradeoff between the two attributes. It is a fundamental issue to quantify and evaluate the tradeoff because it provides important information on the data generating mechanism. However, no previous work has considered the tradeoff quantitatively. We propose a novel algorithm called the stochastic approximation-based robustness-adaptivity algorithm (SRA) to evaluate the tradeoff. The key idea of SRA is to update parameters of distribution or sufficient statistics with the biased stochastic approximation scheme, while dropping data points with large values of the stochastic update. We address the relation between the two parameters: one is the step size of the stochastic approximation, and the other is the threshold parameter of the norm of the stochastic update. The former controls the adaptivity and the latter does the robustness. We give a theoretical analysis for the non-asymptotic convergence of SRA in the presence of outliers, which depends on both the step size and threshold parameter. Because SRA is formulated on the majorization-minimization principle, it is a general algorithm that includes many algorithms, such as the online EM algorithm and stochastic gradient descent. Empirical experiments for both synthetic and real datasets demonstrated that SRA was superior to previous methods. As SRA is based on assumptions that an abrupt change occurs in parameters or sufficient statistics of true distribution and that noisy distribution obeys the uniform distribution, it would be expected for SRA to be theoretically guaranteed in more general settings, such as incremental (gradual) changes and other noisy distributions. Copyright © 2020, The Authors.

关键词： Approximation theory

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Author Correction: On the longevity and inherent hermeticity of silicon-ICs: evaluation of bare-die and PDMS-coated ICs after accelerated aging and implantation studies

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Nature communications 2025年第1期16卷 1907页

作者： Kambiz Nanbakhsh Ahmad Shah Idil Callum Lamont Csaba Dücső Ömer Can Akgun Domonkos Horváth Kinga Tóth Domokos Meszéna István Ulbert Federico Mazza Timothy G Constandinou Wouter Serdijn Anne Vanhoestenberghe Nick Donaldson Vasiliki Giagka Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands. Department of Medical Physics and Biomedical Engineering University College London London UK. Department of Electrical & Electronic Engineering Imperial College London London UK. UK Dementia Research Institute Care Research and Technology Centre London UK. Mint Neurotechnologies Ltd London UK. Centre for Energy Research HUN-REN Budapest Hungary. Nikhef - Dutch National Institute for Subatomic Physics Amsterdam the Netherlands. Research Centre for Natural Sciences Institute of Cognitive Neuroscience and Psychology HUN-REN Budapest Hungary. Pazmany Peter Catholic University Faculty of Information Technology and Bionics Budapest Hungary. Department of Neuroscience Erasmus Medical Center Rotterdam The Netherlands. School of Biomedical Engineering & Imaging Sciences King's College London London UK. Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands. v.giagka@tudelft.nl. Department of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany. v.giagka@tudelft.nl.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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