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Collaborative City Digital Twin for the COVID-19 Pandemic:A Federated Learning Solution

Tsinghua science and Technology 2021年第5期26卷 759-771页

作者： Junjie Pang Yan Huang Zhenzhen Xie Jianbo Li Zhipeng Cai the College of Computer Science and Technology Qingdao UniversityQingdao 266000China the Business School Qingdao UniversityQingdao 266000China the College of Computing and Software Engineering Kennesaw State UniversityAtlantaGA 30060USA the College of Computer Science and Technology Jilin UniversityChangchun 130012China the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA

The novel coronavirus,COVID-19,has caused a crisis that affects all segments of the *** the knowledge and understanding of COVID-19 evolve,an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the *** studies indicate that a city Digital Twin(DT)is beneficial for tackling this health crisis,because it can construct a virtual replica to simulate factors,such as climate conditions,response policies,and people's trajectories,to help plan efficient and inclusive ***,a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions,limiting its advantages when facing urgent crises,such as the COVID-19 *** Learning(FL),in which all clients can learn a shared model while retaining all training data locally,emerges as a promising solution for accumulating the insights from multiple data sources ***,the enhanced privacy protection settings removing the privacy barriers lie in this *** this work,we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status *** particular,an FL central server manages the local updates of multiple collaborators(city DTs),providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection *** approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a"global view"of city crisis ***,it also helps improve each city's DT by consolidating other DT's data without violating privacy *** this paper,we use the COVID-19 pandemic as the use case of the proposed *** experimental results on a real dataset with various response plans val

关键词： COVID-19 Digital Twin(DT) Federated Learning(FL) deep learning

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Fuzzy Neural Network Sliding-Mode Control with Actor-Critic for a Class of Robot Systems 10

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic ...

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10th International Conference on Orange Technology, ICOT 2022

作者： Hung, Lon-Chen Wei, Chun-Cheng Chen, Hung-Jen Yang, Chih-Fu Cheng, Wei-Min Lunghwa University of Science and Technology Department of Electronic Engineering Taiwan School of Software and Big Data Changzhou College of Information Technology China Lunghwa University of Science and Technology Department of Computer Information and Network Engineering China

ISBN: (纸本)9781665454193

In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties. © 2022 IEEE.

关键词： Sliding mode control

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Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems

Optimizing Railway Traffic Management Using Fuzzy Logic-Base...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Nalli Vinaya Kumari G S Pradeep Ghantasala Rajesh Sharma R Ananthanagu U Pellakuri Vidyullatha Dileep Kumar Pandiya Department of Computer Science and Engineering Malla Reddy Engineering College for Women JNTUH Hyderabad Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Principl Software Engineer Zoominfo Technologies Boston USA

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks.

关键词： Schedules Uncertainty Scalability Decision making Web pages Rail transportation Real-time systems software Delays Safety

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

Probabilistic Graph Modeling based Safety Classifier Algorit...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Karkouri, Najib El Tigani, Smail Saadane, Rachid Chehri, Abdelah Pierre, Samuel Neya, Noureddine Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Accident prevention

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Abstractive Text Summarization in Arabic-like Script using Multi-Encoder Architecture and Semantic Extraction Techniques

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IEEE Access 2025年 13卷 104977-104991页

作者： Fatima, Wajiha Rizvi, Syed Saqib Raza Ghazal, Taher M. Kharma, Qasem M. Ahmad, Munir Abbas, Sagheer Furqan, Muhammad Adnan, Khan Muhammad Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Networks and Cybersecurity Amman Jordan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Software Engineering Department Amman Jordan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Korea University University College Seoul 02841 South Korea Prince Mohammad Bin Fahd University Computer Science Department Alkhobar Saudi Arabia Capital University of Science & Technology Department of Software Engineering Islamabad Pakistan Gachon University Faculty of Artificial Intelligence and Software Department of Software Seongnam-si 13120 South Korea

In the field of Natural Language Processing (NLP), the task of text summarization plays a vital role in understanding textual content and producing concise summaries. Text summarization approaches can be categorized as either extractive or abstractive, with the latter largely unexplored in the Arabic script languages. Previous research on abstractive summarization for the Urdu language has progressed in creating logical and succinct summaries but has failed to preserve the underlying semantics, often resulting in inconsistent and ambiguous outputs. This study focuses on designing and implementing an abstractive text summarization model for the Urdu language by achieving the semantic meaning of the original text. The proposed model improves readability and comprehension by generating concise and meaningful overviews of original content while introducing new words and expressions. The study implements a multi-layer transformer encoder as part of the mBART (Multilingual BART) model. The encoder consists of multiple stacked transformer layers. It utilizes self-attention mechanisms to process and understand the input sequences effectively, achieving a BERTScore of 90%, a BLEU score of 43%, and ROUGE scores of 80.3% for ROUGE-1, 74.3% for ROUGE-2, and 80.3% for ROUGE-L. These results demonstrate the efficacy of the model in producing coherent and high-quality abstractive summaries for the Urdu language. © 2013 IEEE.

关键词： Abstractive Text Summarization (ATS) Natural Language Processing (NLP) Semantic Meaning Text Generation Text Summarization (TS)

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Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

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computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of Information Technology Faculty of Computer Sciences and Information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial Intelligence Cairo UniversityGizaEgypt Department of Business Administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： Unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

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Machine Learning-Based Path Loss Estimation Model for a 2.4 GHz ZigBee Network 3rd

Machine Learning-Based Path Loss Estimation Model for a 2.4 ...

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3rd International Conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Ragam, Prashanth Karthik, Guntha Jagadesh, B.N. Jyothi, Sankati School of Computer Science and Engineering VIT-AP University Inavolu Beside AP Secretariat Andharpradesh Amaravathi India Department of ECE Stanely College of Engineering and Technology for Women Telangana Hyderabad India Software Development Cell VIT-AP University Inavolu Beside AP Secretariat Andharpradesh Amaravathi India

ISBN: (纸本)9789819966899

Wireless sensor networks (WSNs) and Internet of Things (IoT) have received remarkable attention from the past few years in various applications. Modeling of path loss (PL) for the deployment of a developed WSN system is a crucial task owing to the time-consuming and elegant operation. However, radiofrequency (RF) engineers adopted either deterministic or stochastic empirical models to estimate the PL. In general, empirical models utilize predefined influenced parameters including path loss (dB), path loss exponent (ϒ), and other significant parameters. Although, empirical models differ significantly from original measurement due to consideration of different terrains. In this study, an endeavor has been made to develop a machine learning-based model to estimate the path loss for a standard ZigBee communication network operating on a 2.4 GHz carrier frequency deployed in an urban area. An experimental setup was designed and tested in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to collect the influence parameters such as received signal strength indicator (RSSI), frequency, distance, and transmitter antenna gain. Besides that, environmental parameters such as temperature and humidity are also included. In this context, a three-layer, feed-forward backpropagation multilayer perception neural network (BPNN) machine learning and Log-Distance empirical models were employed to estimate the PL. The obtained results reveal that the BPNN model noticeably enhanced the coefficient of determination (R2) and reduced root mean square error (RMSE) compared with the empirical model. The R2 and RMSE metrics were obtained as 0.97220 and 0.03630 in the NLOS scenario as well as 0.99820 and 0.00773 in the LOS environment, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Analysis the Research Hotspots and Key Technical of Intelligent Manufacturing 22

Analysis the Research Hotspots and Key Technical of Intellig...

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4th International Conference on Management science and Industrial engineering, MSIE 2022

作者： Cai, Yu Wu, Jiahao Zaheer, Muhammad Department of Computer Software Engineering Institute of Guangzhou Guangzhou51098 China College of Computer Information and Engineering Nanchang Institute of Technology Nanchang330044 China Southern University of Science and Technology Shenzhen518000 China

ISBN: (纸本)9781450395816

Intelligent manufacturing is one of the keys to open up the future of life, and multidisciplinary interactions have shown its strong vitality. This study conducts a scientometric review of the global research published since 1987 on intelligent manufacturing, through citation, keyword, clustering and keywords burst analysis. A total of 3994 research documents are taken as data sources, which are retrieved from the Web of science Core Collection database from 1987 to 2021. Using the CiteSpace analysis tool, this paper discussed the development process of the relevant years of intelligent manufacturing, analyzed the research hotspots and their technology, presented the emerging models and techniques, and identified the latest research evolution and trends. The results will provide reference for scientific scholars in future research in the field of intelligent manufacturing. © 2022 ACM.

关键词： Industrial research

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Matrix-Based Ant Colony System for Traveling Salesman Problem

Matrix-Based Ant Colony System for Traveling Salesman Proble...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xu Li, Jian-Yu Chen, Chun-Hua Zhan, Zhi-Hui Kwong, Sam Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665410205

Ant colony system algorithm (ACS), as an important evolutionary computation (EC) algorithm, has demonstrated significant advantages in solving complex optimization problems. However, traditional EC algorithms and traditional ACS algorithm often face the challenge of slow computational speed when dealing with large-scale problems. In recent years, matrix-based EC approaches have been proposed to accelerate the computational speed, which has obtained promising results in dealing with large-scale problems. However, most existing matrix-based EC algorithms are designed for continuous optimization problems, while the matrix-based approach integrated with ACS has not attracted enough attention, which will be efficient for solving large-scale discrete optimization problems. Therefore, in this paper, we propose a matrix-based ACS (MACS) algorithm and apply it to solve the traveling salesman problem (TSP). MACS is an innovative improvement over the traditional ACS algorithm, utilizing matrix operations to parallelly let ants select city and update pheromone. Experimental results show that the MACS algorithm has significantly better efficiency in accelerating computational speed while maintaining the remarkable problem-solving ability in solving large-scale TSP. © 2024 IEEE.

关键词： Ant colony optimization

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