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Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

地球空间信息科学学报（英文版） 2024年第3期27卷 602-623页

作者： Roberto Tomás Qiming Zeng Juan M.Lopez-Sanchez Chaoying Zhao Zhenhong Li Xiaojie Liu María I.Navarro-Hernández Liuru Hu Jiayin Luo Esteban Díaz William T.Szeibert José Luis Pastor Adrián Riquelme Chen Yu Miguel Cano Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencePeking UniversityBeijingChina Institute for Computer Research(IUII) University of AlicanteAlicanteSpain College of Geological Engineering and Geomatics Chang'an UniversityXi'anChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain School of Civil Engineering Lanzhou University of TechnologyLanzhouChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Land Satellite Remote Sensing Application Center(LASAC) Ministry of Natural Resources of P.R.ChinaBeijingChina The First Topographic Surveying Brigade of Ministry of Natural Resources of the People's Republic of China Xi'anChina Sixense Departamento SatéliteL'Hospitalet de LlobregatBarcelonaSpain

Landslides are destructive geohazards to people and infrastructure,resulting in hundreds of deaths and billions of dollars of damage every ***,mapping the rate of deforma-tion of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated *** this paper,the main outcomes relevant to the joint European Space Agency(ESA)and the Chinese Ministry of science and Technology(MOST)Dragon-5 initiative cooperation project ID 59,339"Earth observation for seismic hazard assessment and landslide early warning system"are *** primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks,detect potential landslides in wide regions,and demonstrate EO-based landslide early warning system over selected *** work only focuses on the landslide hazard content of the project,and thus,in order to achieve these objectives,the following tasks were developed up to now:a)a procedure for phase unwrap-ping errors and tropospheric delay correction;b)an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements;c)the application of polarimetric SAR interferometry(PolInSAR)to increase the number and quality of monitoring points in landslide-prone areas;d)the semiautomatic mapping and preliminary classification of active displacement areas on wide regions;e)the modeling and identification of landslides in order to identify triggering factors or predict future displacements;and f)the application of an InSAR-based landslide early warning system on a selected *** achieved results,which mainly focus on specific sensitive regions,provide essential assets for planning present and future scientific activities devoted to identifying,mapping,characterizing,monitoring and predicting landslides,as well as for the implementation of early warning systems.

关键词： InSAR SAR offset tracking PolInSAR landslide mapping modeling early warning monitoring

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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Cryptocurrency Transaction Anomaly Detection Based on Chebyshev Graph Neural Network 1st

Cryptocurrency Transaction Anomaly Detection Based on Cheby...

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1st Conference on Blockchain and Web3.0 Technology Innovation and application, BWTAC 2024

作者： Li, Chao Zhu, Xiangkai Li, Jike Xie, Nengfu Zeng, Qingtian School of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Key Laboratory of Agricultural Blockchain Application Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819794119

Recent research has focused on effectively combining blockchain with graph neural networks, utilizing the efficient computing methods of graph neural networks to promote the application and development of blockchain technology in more fields. However, traditional GNN methods only focus on the neighborhood information around the account, ignoring global topology and high-order neighborhood information. Additionally, the importance of different fraudulent accounts with the same neighborhood cannot be distinguished during the aggregation process. To address these above challenges, we propose a research model for anomaly detection in cryptocurrency transactions based on Chebyshev graph neural networks. Specifically, we use the Chebyshev kernel function to obtain high-order neighborhood information and differentiate the importance of accounts with the same neighborhood through the Laplace operator. We demonstrate through lemma that unreasonable coefficients lead to Chebyshev networks being inferior to other methods and penalize the learned outlier coefficients to mitigate the impact on higher-order neighborhood approximations. Subsequently, we aggregate multi-layer neighbor information through attention mechanisms to achieve comprehensive information integration. The effectiveness of the model has been verified through multiple experiments on the Elliptical dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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MIDC: Medical image dataset cleaning framework based on deep learning

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Heliyon 2024年第19期10卷 e38910页

作者： Yi, Sanli Chen, Ziyan School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 China Key Laboratory of Computer Technology Application of Yunnan Province Kunming 650500 China

Deep learning technology is widely used in the field of medical imaging. Among them, Convolutional Neural Networks (CNNs) are the most widely used, and the quality of the dataset is crucial for the training of CNN diagnostic models, as mislabeled data can easily affect the accuracy of the diagnostic models. However, due to medical specialization, it is difficult for non-professional physicians to judge mislabeled medical image data. In this paper, we proposed a new framework named medical image dataset cleaning (MIDC), whose main contribution is to improve the quality of public datasets by automatically cleaning up mislabeled data. The main innovations of MIDC are: firstly, the framework innovatively utilizes multiple public datasets of the same disease, relying on different CNNs to automatically recognize images and remove mislabeled data to complete the data cleaning process. This process does not rely on annotations from professional physicians and does not require additional datasets with more reliable labels;Secondly, a novel grading rule is designed to divide the datasets into high-accuracy datasets and low-accuracy datasets, based on which the data cleaning process can be performed;Thirdly, a novel data cleaning module based on CNN is designed to identify and clean low-accuracy datasets by using high-accuracy datasets. In the experiments, the validity of the proposed framework was verified by using four kinds of datasets diabetic retinal, viral pneumonia, breast tumor, and skin cancer, with results showing an increase in the average diagnostic accuracy from 71.18 % to 85.13 %, 82.50 %to 93.79 %, 85.59 %to 93.45 %, and 84.55 %to 94.21 %, respectively. The proposed data cleaning framework MIDC could better help physicians diagnose diseases based on the dataset with mislabeled data. © 2024

关键词： Classification accuracy Data cleaning Deep learning Mislabeled data Public medical datasets

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A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

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computers, Materials & Continua 2021年第1期66卷 931-945页

作者： Cuixiang Wang Xing Shao Yifan Meng Jun Gao School of Information Engineering Yancheng Institute of TechnologyYancheng224000China School of Computer Nanjing University of Posts and TelecommunicationNanjing210003China Modern Agricultural Resources Intelligent Management and Application Laboratory Huzhou Normal UniversityHuzhou313000China School of Science and Technology Troy UniversityTroy36082USA

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying *** improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this *** pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision *** addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the ***,PNBA reduces the number of interactions between reader and tags,and improves the tags identification *** analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification ***,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

关键词： Radio frequency identification(RFID) tag anti-collision algorithm physical layer network coding binary search tree algorithm

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Methodologies for Ensuring Robust Cybersecurity in Health Care Agencies for the Purpose of Securing Medical Internet of Things Devices

Methodologies for Ensuring Robust Cybersecurity in Health Ca...

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Disruptive Technologies (ICDT), International Conference on

作者： S.B. Prakalya Rajesh David Swapna Neerumalla J. Jegan S Sajithra B. Nancharaiah Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Application Architect KUKA U.S. Holdings Company LLC Austin TX USA Department of Computer Science and Engineering Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering School of Technology The Apollo University Chittoor Andhra Pradesh India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Usha Rama College of Engineering and Technology Telaprolu Andra Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Improvements in patient tracking, diagnosis, and treatment have resulted from the widespread use of MIoT devices in healthcare organizations. Patient safety and sensitive health data are at danger due to the increased number of linked devices, which has brought about major cybersecurity issues such as illegal access, data breaches, and service disruptions. This study delves into different approaches to guarantee strong cybersecurity in healthcare organizations, with a particular emphasis on safeguarding MIoT devices. Anomaly detection systems, secure communication protocols, device authentication, and end-to-end encryption are some of the key tactics that are investigated. In addition, the research looks into how machine learning and AI may be used for predictive analytics and real-time threat detection in MIoT networks, and how blockchain technology can guarantee data integrity and safe communication in these networks. Following legal standards like HIP AA and creating multi-layered security measures to reduce risks are also emphasized in the research. The goal of this study is to present a thorough framework for protecting MIoT devices in healthcare settings, guaranteeing patient safety and data confidentiality by integrating established cybersecurity practices with new technology. Additionally, issues like managing a diverse ecosystem of MIoT devices and device resource limits are discussed, along with suggestions for adaptable security solutions that are lightweight. The results provide healthcare organizations with practical advice on how to strengthen the security of their systems and prevent cyberattacks on their MIoT infrastructure.

关键词： Protocols Standards organizations Medical services Organizations Threat assessment Real-time systems Safety Encryption Internet of Things computer security

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An Analysis of Cyber security Risks and Authentication Systems 10

An Analysis of Cyber security Risks and Authentication Syste...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sampath, P. Shukla, Vijay Kumar, Santosh Gehlot, Anita Samatha, V. Kumar, T. Sampath School of Computing Easa College of Engineering and Technology Coimbatore641105 India Institute of Technology Department of Cse Greater Noida India Lucknow Public College of Professional Studies Department of Computer Science Vinamra Khand Gomti Nagar Uttar Pradesh Lucknow226010 India Uttaranchal Institute of Technology Uttaranchal Univeristy India Dkw Govt College for Women Computer Application Nellore524003 India Sr University Dept of CSandAI Telangana India

ISBN: (纸本)9789380544472

This paper presents an analysis of cyber security risks and authentication systems. It provides an overview of the most commonly used authentication systems, their pros and cons, and the potential security risks associated with each one. It then discusses the importance of implementing an effective authentication system, the challenges involved in doing so, and the best practices to ensure a secure system. Finally, the paper concludes with recommendations for organizations to consider when implementing authentication systems. The paper provides an in-depth look at cyber security risks and authentication systems in order to help organizations make informed decisions on how to protect their data. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Authentication

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When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline 39

When Hypergraph Meets Heterophily: New Benchmark Datasets an...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Ming Gu, Yongchun Wang, Yi Fang, Yujie Bai, Lu Zhuang, Xiaosheng Lio, Pietro Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China Zhejiang Institute of Optoelectronics China School of Computer Science and Technology Zhejiang Normal University China School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University China Department of Mathematics City University of Hong Kong Hong Kong Department of Computer Science and Technology Cambridge University United Kingdom

ISBN: (纸本)157735897X

Hypergraph neural networks (HNNs) have shown promise in handling tasks characterized by high-order correlations, achieving notable success across various applications. However, there has been limited focus on heterophilic hypergraph learning (HHL), in contrast to the increasing attention given to graph neural networks designed for graphs exhibiting heterophily. This paper aims to pave the way for HHL by addressing key gaps from multiple perspectives: measurement, dataset diversity, and baseline model development. First, we introduce metrics to quantify heterophily in hypergraphs, providing a numerical basis for assessing the homophily/heterophily ratio. Second, we develop diverse benchmark datasets across various real-world scenarios, facilitating comprehensive evaluations of existing HNNs and advancing research in HHL. Additionally, as a novel baseline model, we propose HyperUFG, a framelet-based HNN integrating both low-pass and high-pass filters. Extensive experiments conducted on synthetic and benchmark datasets highlight the challenges current HNNs face with heterophilic hypergraphs, while showcasing that HyperUFG performs competitively and often outperforms many existing models in such scenarios. Overall, our study underscores the urgent need for further exploration and development in this emerging field, with the potential to inspire and guide future research in HHL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Advances in Big Data and Data Mining: Techniques and applications in Data Fusion for Enhanced Insights and Decision-Making

Advances in Big Data and Data Mining: Techniques and Applica...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Meenakshi Maindola Ramy Riad Al-Fatlawy Rakesh Kumar Dr. Nandini Shirish Boob S P Sreeja N Sirisha Department of Computer Science & Eng. Graphic Era Deemed to be University Dehradun Department of Computers Techniques Eng. College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Eng. & Applications GLA University Mathura Saveetha School of Eng. Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Master of Computer Application New Horizon College of Engineering Bangalore Department of Computer Science and Eng. MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Big data's explosive growth has transformed several businesses by making it possible to glean insightful information from big, complex databases. This study examines new developments in big data and data mining methods, concentrating on data fusion as a means of improving decision-making. Data fusion, the act of combining many data sources to get more accurate and thorough insights, is becoming essential in industries including cybersecurity, healthcare, finance, and marketing. The study looks at how cutting-edge data mining methods, such as anomaly detection, classification, and clustering, might increase the efficiency of data fusion. Important issues are also covered, including managing heterogeneous data, guaranteeing data quality, and scalability. This paper shows how data fusion approaches may enhance decision-making processes by offering more comprehensive perspectives and useful insights via an analysis of real-world applications. The paper's conclusion discusses potential future developments, emphasising how machine learning and artificial intelligence may be used to improve data mining and fusion procedures, potentially leading to even higher levels of precision and effectiveness in decision-making across a range of industries.

关键词： Industries Soft sensors Decision making Data integration Medical services Machine learning Big Data Real-time systems Data mining Anomaly detection

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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