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2024 International Conference on Artificial Intelligence of Things and Computing, AITC 2024

作者： Xu, Xuesong Zhang, Xia Xiao, Gang Li, Chen Wang, Xuebin Cheng, Zhenbo Lu, Jiansha College of Mechanical Engineering Zhejiang University of Technology Zhejiang Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Zhejiang Hangzhou China College of Energy Environment and Safety Engineering China Jiliang University Zhejiang Hangzhou China Zhejiang Academy of Special Equipment Science Zhejiang Hangzhou China

ISBN: (纸本)9798400709869

Using the Patsnap patent database as a data source, this study analyzes the research and development trends of steel belt elevator technology from the perspectives of time distribution, geographic distribution, technological direction, and research themes. The analysis aims to clarify China's position in the global elevator technology landscape and provide a basis for addressing technological gaps. The study focuses on three key areas: steel belt materials, steel belt structures, and steel belt damage detection. The results indicate that: 1) The number of global patents related to steel belt elevators has been steadily increasing;2) The major patent-holding countries are China, the United States, Switzerland, Finland, Japan, and Germany. China leads in the number of patents, but its innovation impact is relatively low, while the United States and Switzerland are at the forefront of technological innovation;3) The main target regions for these patents are China, the United States, and Iran, with several elevator companies having already established patent portfolios in China and Iran;4) Current research primarily covers steel belt materials, structures, and damage detection, with the critical challenge being the need for more efficient, accurate, and convenient methods for detecting steel belt damage. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Patents and inventions

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Secure E-Health Record Sharing using Blockchain: A Comparative Analysis Study 6

Secure E-Health Record Sharing using Blockchain: A Comparati...

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Kondepogu, Moses Dayanand Andrew, J. Karunya Institue of Technology and Sciences Computer Science and Engineering Coimbatore India Manipal Institue of Technology Manipal Academy of Higher Education Computer Science and Engineering Manipal India

ISBN: (纸本)9781665410359

Blockchain is a digital ledger where the data entries are recorded in a decentralized fashion that cannot be altered. This is likely to prosper in a variety of industries, including healthcare. According to a survey, 70% of healthcare professionals believe that blockchain has the largest influence on clinical report management, regulating documents, and creating a framework for sharing electronic health records (EHR) in the healthcare industry. Even though blockchain technology is having real potential for improving health information systems, the rise of this technology has also led to innovative proposals and applications. This review mainly focuses on EHR data exchange between the patient and the hospital entities like doctors, record verifiers, insurance companies, pharmaceuticals, etc. The proposed research study has also discussed about the techniques used in these models to counter the problems present in the traditionally used server-client model like a data breach, Unauthorized access, centralized data management, etc., and improvisation of the blockchain-based models. Further, a comparative analysis has been provided on various blockchain-based systems for hosting a secure EHR sharing. © 2022 IEEE.

关键词： Smart contract

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Review of deep learning-based weed identification in crop fields

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International Journal of Agricultural and Biological engineering 2023年第4期16卷 1-10页

作者： Wenze Hu Samuel Oliver Wane Junke Zhu Dongsheng Li Qing Zhang Xiaoting Bie Yubin Lan School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000ShandongChina Department of Agricultural Engineering Harper Adams UniversityNewport TF108NBShropshireUK School of Computer Science and Technology Shandong University of TechnologyZibo 255000ShandongChina Academy of Ecological Unmanned Farm Zibo 255000ShandongChina

Automatic weed identification and detection are crucial for precision weeding *** recent years,deep learning(DL)has gained widespread attention for its potential in crop weed *** paper provides a review of the current research status and development trends of weed identification in crop fields based on *** an analysis of relevant literature from both within and outside of China,the author summarizes the development history,research progress,and identification and detection methods of DL-based weed identification *** is placed on data sources and DL models applied to different technical ***,the paper discusses the challenges of time-consuming and laborious dataset preparation,poor generality,unbalanced data categories,and low accuracy of field identification in DL for weed *** solutions are proposed to provide a reference for future research directions in weed identification.

关键词： deep learning weed detection weed classification cation image segmentation Convolutional Neural Network image processing

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Bayesian hypergame approach to equilibrium stability and robustness in moving target defense 63

Bayesian hypergame approach to equilibrium stability and rob...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Hanzheng Cheng, Zhaoyang Chen, Guanpu Johansson, Karl Henrik Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China University of Chinese Academy of Science Academy of Mathematics and System Science Chinese Academy of Science Beijing China Kth Royal Institute of Technology and the Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players' underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results. © 2024 IEEE.

关键词： Hierarchical systems

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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An Optimized Wireless Video Transmission in a Smart Agricultural Solutions 40

An Optimized Wireless Video Transmission in a Smart Agricult...

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40th National Radio science Conference, NRSC 2023

作者： Naeem, Mohamed Aboul-Dahab, Mohamed Technology and Maritime Transport Computer Networks and Data Centre-Cairo Arab Academy for Science Cairo11799 Egypt Technology and Maritime Transport Department of Electronics and Communications Engineering. Arab Academy for Science Cairo11799 Egypt

ISBN: (纸本)9798350311440

Smart agriculture requires field sensing, especially video and photo transmissions, to detect severe issues in this field. Although the most suitable communication infrastructure is wireless transmission, the agriculture field suffers from severe diffraction and scattering of the wirelessly propagated waves. This paper proposes an optimization technique for improving wireless transmission in the agricultural environment by utilizing the most appropriate wireless impairment mitigation methods. The proposed technique adopts a joint wireless transmission optimization approach for both the client and the network sides. The amendment in the wireless transmission from the client-side perspective utilized the concurrent load balance over the heterogeneous wireless connections. While the refinement of the heterogeneous wireless network achieved better coverage by minimizing the number of propagating nodes by optimizing the propagating nodes' placement within the agricultural plot. The simulation, experiment, and results proved the effectiveness of the proposed solution for achieving better wireless video transmission performance. © 2023 IEEE.

关键词： Heterogeneous networks

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A Topic-Guided Self-Attention Network for Daily Mental Wellbeing Prediction Using Mobile Devices

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IEEE Transactions on Affective Computing 2024年第2期16卷 783-798页

作者： Xu, Zeju Liu, Guanzheng Zhao, Guozhen Zhang, Zhiguo Li, Chenzhong Wang, Changhong Shenzhen Campus of Sun Yat-Sen University School of Biomedical Engineering Guangdong Shenzhen518000 China Institute of Psychology CAS Key Laboratory of Behavioral Science Beijing100101 China University of Chinese Academy of Sciences Department of Psychology Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen China Biomedical Engineering School of Medicine Shenzhen518172 China

Prediction of daily mental wellbeing holds profound implications for individual healthcare and societal stability. Previous studies have shown the potential of using individual's multimodal behavioral data collected through mobile devices to predict his/her daily mental wellbeing metrics, such as stress, mood, and anxiety. However, effectively capturing long-range dependencies in behavioral time series data while accurately representing the statistical distribution patterns of various behaviors over a certain period is a significant challenge. In this paper, we propose a daily mental wellbeing prediction model based on a Topic-Guided Self-Attention Network (TGSAN). This model utilizes self-attention mechanism to capture long-range dependencies from the behavioral data collected by mobile devices. We utilize a multi-granularity time encoding method to inject time information of different granularities (i.e., day and hour, or week and day) into the behavioral data, thereby enhancing the sensibility of the self-attention network to capture every individual's habitual cyclicality rhythm. Then, we introduce a neural topic model to analyze the statistical distribution characteristics of various behaviors in the monitoring period as behavioral distribution patterns for different individuals, and further propose a topic attention network to enhance the model's classification performance by guiding the weights of long-range dependencies features from the self-attention network with the derived topic information. Compared to state-of-the-art methods, the proposed TGSAN achieved superior performance on datasets that measure different mental health indicators (stress, mood, and anxiety), with F1 scores outperforming by 4.5% and 2.3% on the Crosscheck and StudentLife datasets, respectively, and accuracy outperforming by 3.3% on the GLOBEM dataset. Our study demonstrates the effectiveness and interpretability of combining self-attention mechanisms with neural topic model, for a b

关键词： Prediction models

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Integrating Digital Twins with AI for Real-Time Intrusion Detection in Smart Infrastructure Networks

Integrating Digital Twins with AI for Real-Time Intrusion De...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sasikala, M. Mahaboob John, Y.M. Jothi, B. Nandhini, S. Senthil Kumar, S. Sona College of Technology Department of Information Technology Salem India Mahendra College of Engineering Ece Department Salem India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Karpagam Academy of Higher Education Computer Science and Engineering Coimbatore India R.M.K. College of Engineering and Technology Department of Mechanical Engineering Thiruvallur India

ISBN: (纸本)9798350360660

The integration of Digital Twins with Artificial Intelligence (AI) represents a revolutionary approach to enhancing real-time intrusion detection in smart infrastructure networks. Digital Twins, as virtual replicas of physical assets, enable continuous monitoring and predictive analysis, while AI provides advanced analytical capabilities for identifying and mitigating potential security threats. This paper presents a comprehensive framework that leverages Digital Twins in conjunction with AI algorithms to detect and respond to intrusions in smart infrastructure networks. By simulating network behavior and employing machine learning models to analyze data patterns, the proposed system ensures robust security measures. One of the proposed techniques involves the use of a hybrid anomaly detection algorithm combining Autoencoders and Recurrent Neural Networks (RNN). The Autoencoder is utilized to learn a compact representation of the normal network behavior by reducing dimensionality and reconstructing inputs, while the RNN captures temporal dependencies and sequences within the network data. This hybrid model enhances the detection of both known and unknown intrusion patterns by focusing on deviations from the learned normal behavior. The integration of these AI components with Digital Twins enables a more nuanced and accurate understanding of network anomalies, thereby improving the system's ability to detect and respond to intrusions promptly. Experimental results demonstrate the efficacy of this approach in real-time threat detection, showcasing significant improvements in response times and accuracy. This integration not only enhances the resilience of smart infrastructure but also paves the way for future advancements in secure and intelligent network management. © 2024 IEEE.

关键词： Network intrusion

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Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 127-139页

作者： Tao, Xian Yan, Shaohua Gong, Xinyi Adak, Chandranath Institute of Automation Chinese Academy of Sciences Beijing100190 China Binzhou Institute of Technology Binzhou256606 China CAS Engineering Laboratory for Intelligent Industrial Vision Beijing100190 China School of Artificial Intelligence University of ChineseAcademy of Sciences Beijing100049 China Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

In industrial quality assessment, monitoring whether the textured product contains defects is a critical step. Compared to a large number of defect-free images that are easy to obtain, anomaly samples are limited and vary randomly in size and type. It is challenging to develop an automatic and accurate texture defect localization system that only uses normal images for training. In this article, a multiresolution feature learning network is proposed to detect various texture defects in an unsupervised manner. A robust pretrained model is first employed to extract the perceptual features from the input image, then the perceptual features of various layers are fed to the corresponding multiscale autoencoder framework. This hierarchical alignment strategy aids in receiving multilevel information for locating anomalies of various sizes. Moreover, a residual attention module is embedded in the architecture to further improve the detection performance. Our proposed method has achieved state-of-the-art performance on the texture dataset of MVTecAD. We also extended the experiment to the real industrial texture datasets, and its detection result is better than the major existing advanced techniques. © 2020 IEEE.

关键词： Textures

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SNCA:Semi-Supervised Node Classification for Evolving Large Attributed Graphs

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Big Data Mining and Analytics 2024年第3期7卷 794-808页

作者： Faima Abbasi Muhammad Muzammal Qiang Qu Farhan Riaz Jawad Ashraf Luxembourg Institute of Science and Technology and University of Luxembourg Esch-Sur-Alzette L-4362Luxembourg Department of Computer and Information Sciences Northumbria UniversityNewcastle NE18STUK Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Computer Science University of LincolnLincoln LN67TSUK Faculty of Computing Engineering and MediaDe Montfort UniversityLeicesterLE19BHUK

Attributed graphs have an additional sign vector for each ***,edge signs represent like or dislike relationship between the node *** has applications in domains,such as recommender systems,personalised search,***,limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge *** inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the *** study of attributed networks is challenging due to noise,sparsity,and class imbalance *** this work,we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed *** propose Semi-supervised Node Classification in Attributed graphs(SNCA).SNCA is robust to underlying network noise,and has in-built class imbalance handling *** perform an extensive experimental study on real-world datasets to showcase the efficiency,scalability,robustness,and pertinence of the *** performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.

关键词： attributed networks node classification recommender systems

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