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NAVAL ENGINEERS JOURNAL 1985年第6期97卷 35-48页

作者： MALKOFF, DB MOY, MC WILLIAMS, HL Dr. Donald B. Malkoff majored in physics as an undergraduate at Harvard University. He received an M.D. degree from the University of Pittsburgh School of Medicine in 1960. This was followed by an internship and residency in neurology at University Hospital in Ann Arbor Michigan. He spent several years at the National Institutes of Health engaged in gerontology research has practiced and taught clinical neurology and in 1983 received an M.S. degree in computer science at the University of California San Diego. Currently Dr. Malkoff is employed by the Navy Personnel Research and Development Center in San Diego California where he is senior investigator in a human factor/computer display-and-control project involving the DDG-51 gas turbine propulsion unit. He is a member of the American Academy of Neurology the Society for Neuroscience the American Association for Artificial Intelligence and the Association for Computing Machinery. Dr. Malkoff is certified by the American Board of Psychiatry and Neurology has taught computer science at UCSD and published in several research areas including magnetoencephalography and electron microscopy. His basic interests are in the areas of artificial intelligence and learning expert systems particularly as they apply to the problems of fault-detection and control. Dr. Melvyn C. Moy received his undergraduate training in mathematics and chemistry at the University of Texas Austin. He studied experimental psychology at the University of Wisconsin Madison receiving his M.S. in 1970 and Ph.D. in 1972. He served as an assistant professor at the University of South Dakota where he taught experimental design and methodology for a year before joining the Navy Personnel Research and Development Center in 1973. His work and research since then spans across many application areas such as the development of manpower planning models for the Navy the design of operational decision aids the human engineering of interactive large-scale war gaming systems and the evaluation o

The ship fire main has undergone considerable development throughout the past 2,000 years, resulting in a system that is critical both for normal ship function and for ship survivability in emergencies. Because of its complexity, the modern firemain system is highly vulnerable to malfunction and to damage during combat. Firemain fault detection and fault recovery are currently conducted manually by damage control teams. The advantages and disadvantages of this method are discussed, and alternative methods of fault detection and recovery are explored. An interactive computer program is introduced which uses central control over remotely situated valves to facilitate fault detection and recovery, significantly reducing recovery-time and manpower requirements. These reductions may result in savings of lives, ship systems, and ships themselves. The computer program is based upon an algorithm which is, in effect, a prescription that can be followed manually by the operator or be completely automated. The color graphic display which is used for monitoring can also be utilized for the training of damage control operators or for the evaluation of other algorithms for firemain control. Alternative firemain hardware and configurations could lead to even more efficient methods of fault detection and recovery as well as improved firemain water supply management in general.

关键词： NAVAL VESSELS

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InfoFlowNet: A Multi-head Attention-based Self-supervised learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity

arXiv

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arXiv 2023年

作者： Chuang, Chun-Hsiang Fang, Shao-Xun Huang, Chih-Sheng Ding, Weiping Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Education and Learning Technology National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Artificial Intelligence Research and Development Elan Microelectronics Corporation Hsinchu Taiwan College of Artificial Intelligence and Green Energy National Yang Ming Chiao Tung University Hsinchu Taiwan College of Electrical Engineering and Computer Science National Taipei University of Technology Hsinchu Taiwan School of Information Science and Technology Nantong University Nantong China Institute of Information Systems and Applications College of Electrical Engineering and Computer Science NTHU Treasure Taipei Chapter of IEEE Computational Intelligence Society Taiwan

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, a measure of brain network dynamics, is particularly useful for investigating the directional influences among different brain regions. In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series. The proposed method estimates the magnitude of directional information flow (dIF) among EEG processes by measuring the loss of model inference resulting from the shuffling of the time order of the original time series. To evaluate the feasibility of InfoFlowNet, we conducted experiments using a synthetic time series and two EEG datasets. The results demonstrate that InfoFlowNet can extract time-varying causal relationships among processes, reflected in the fluctuation of dIF values. Compared with the Granger causality model and temporal causal discovery framework, InfoFlowNet can identify more significant causal edges underlying EEG processes while maintaining an acceptable computation time. Our work demonstrates the potential of InfoFlowNet for analyzing effective connectivity in EEG data. The findings highlight the importance of effective connectivity in understanding the complex dynamics of the brain network. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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A Q-learning-based smart clustering routing method in flying Ad Hoc networks

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Journal of King Saud University - computer and Information sciences 2024年第1期36卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Aurangzeb, Khursheed Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Darwesh, Aso Lee, Sang-Woong Fazlali, Mahmood Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Cybersecurity and Computing Systems Research Group School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire AL10 9AB United Kingdom

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly. © 2024 The Author(s)

关键词： Clustering Flying ad hoc networks (FANETs) Machine learning (ML) Reinforcement learning (RL) Unmanned aerial vehicles (UAVs)

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An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

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Vehicular Communications 2024年 49卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Alanazi, Faisal Aurangzeb, Khursheed Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Rahmani, Amir Masoud Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Computer Engineering College of Computer and Information Sciences King Saud University P. O. Box 51178 Riyadh 11543 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW i

关键词： Artificial Intelligence (AI) Clustering Flying ad hoc networks (FANETs) Nature-inspired algorithms Unmanned aerial vehicles (UAVs)

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Comparison of the Effectiveness of Machine learning Techniques for the Number of Road Traffic Injuries

Comparison of the Effectiveness of Machine Learning Techniqu...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

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MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

arXiv

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arXiv 2024年

作者： Newlin, Nancy R. Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Chen, Jian Yang, Joseph Yuan-Mou Wu, Ye He, Yifei Zhang, Jiawei Zeng, Qingrun Zhang, Fan Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Ewert, Christian Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett A. Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’s Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Pediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA NashvilleTN United States Learning Research and Development Center University of Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathematics Eindhoven University of Technology Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Germany Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxel-wise cross-acquisition relationships was the most effective at harmonizing connectomic, microst

关键词： Diffusion tensor imaging

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Challenges and Best Practices in Ethical Review of Human and Organizational Factors Studies in Health Technology: a Synthesis of Testimonies

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Yearbook of Medical Informatics 2020年第1期29卷 58-70页

作者： Peute, Linda W. Lichtner, Valentina Baysari, Melissa T. Hägglund, Maria Homco, Juell Jansen-Kosterink, Stephanie Jauregui, Ignacio Kaipio, Johanna Kuziemsky, Craig E. Lehnbom, Elin Christina Leite, Francisca Lesselroth, Blake Luna, Daniel Otero, Carlos Pedersen, Rune Pelayo, Sylvia Santos, Raquel Silva, Nuno-André Tyllinen, Mari Van Velsen, Lex Zheng, Wu Yi Jaspers, Monique Marcilly, Romaric Centre for Human Factor Engineering of Health Information technology - Amsterdam UMC University of Amsterdam department of Medical Informatics Amsterdam Netherlands Centre for Medication Safety and Service Quality UCL School of Pharmacy United Kingdom University of Sydney Faculty of Medicine and Health Sydney Australia Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Women's and Children's Health Uppsala University Uppsala Sweden Department of Medical Informatics University of Oklahoma - Tulsa School of Community Medicine United States eHealth group Roessingh Research and Development Enschede Netherlands Health Informatics Department Hospital Italiano de Buenos Aires Argentina Department of Computer Science Aalto University Finland MacEwan University AB Edmonton Canada Department of Pharmacy Faculty of Health Sciences UiT The Arctic University of Norway Norway Department of Health and Caring Sciences Faculty of Health and Life Sciences Linnaeus University Sweden Hospital da Luz Learning Health Portugal Norwegian Centre for E-health Research University Hospital of North Norway HF Norway Department of Clinical Medicine Faculty of Health Sciences UiT The Arctic University of Norway Norway Univ. Lille ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales CHU Lille Lille France

Objective: Human and Organizational Factors (HOF) studies in health technology involve human beings and thus require Institutional Review Board (IRB) approval. Yet HOF studies have specific constraints and methods that may not fit standard regulations and IRB practices. Gaining IRB approval may pose difficulties for HOF researchers. This paper aims to provide a first overview of HOF study challenges to get IRB review by exploring differences and best practices across different countries. Methods: HOF researchers were contacted by email to provide a testimony about their experience with IRB review and approval. Testimonies were thematically analyzed and synthesized to identify and discuss shared themes. Results: Researchers from seven European countries, Argentina, Canada, Australia, and the United States answered the call. Four themes emerged that indicate shared challenges in legislation, IRB inefficiencies and inconsistencies, general regulation and costs, and lack of HOF study knowledge by IRB members. We propose a model for IRB review of HOF studies based on best practices. Conclusion: International criteria are needed that define low and high-risk HOF studies, to allow identification of studies that can undergo an expedited (or exempted) process from those that need full IRB review. Enhancing IRB processes in such a way would be beneficial to the conduct of HOF studies. Greater knowledge and promotion of HOF methods and evidence-based HOF study designs may support the evolving discipline. Based on these insights, training and guidance to IRB members may be developed to support them in ensuring that appropriate ethical issues for HOF studies are considered. © 2020 IMIA and Georg Thieme Verlag KG.

关键词： ergonomics Ethical review human factors organizational factors social sciences

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

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Vehicular Communications 2024年 47卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Ahmad, Husham Jawad Alanazi, Faisal Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Rahmani, Amir Masoud Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet deliver

关键词： Artificial intelligence (AI) Clustering Flying ad hoc networks (FANETs) Optimization algorithms Unmanned aerial vehicles (UAVs)

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MammoFL: Mammographic Breast Density Estimation using Federated learning

arXiv

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arXiv 2022年

作者： Muthukrishnan, Ramya Heyler, Angelina Katti, Keshava Pati, Sarthak Mankowski, Walter Alahari, Aprupa Sanborn, Michael Conant, Emily F. Scott, Christopher Winham, Stacey Vachon, Celine Chaudhari, Pratik Kontos, Despina Bakas, Spyridon Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Massachusetts Institute of Technology CambridgeMA United States Research and Development GSK CambridgeMA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indiana IN United States Research Center for Federated Learning in Medicine Indiana University School of Medicine Indiana IN United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Health Sciences Research Mayo Clinic RochesterMN United States

In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one trained on each single-institution dataset and one trained on the aggregated multi-institution dataset. We demonstrate that training on multi-institution datasets is critical to algorithm generalizability. We further show that federated learning on multi-institutional datasets improves model generalization to unseen data at nearly the same level as centralized training on multi-institutional datasets, indicating that federated learning can be applied to our method to improve algorithm generalizability while maintaining patient privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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