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Advancing osteoarthritis research:the role of AI in clinical,imaging and omics fields

Bone Research 2025年第2期13卷 307-319页

作者： Jingfeng Ou Jin Zhang Momen Alswadeh Zhenglin Zhu Jijun Tang Hongxun Sang Ke Lu Shenzhen Hospital Southern Medical UniversityShenzhenChina Faculty of Computer Science and Control Engineering Shenzhen University of Advanced TechnologyShenzhenChina Department of Orthopaedic Surgery The First Affiliated Hospital of Chongqing Medical UniversityChongqingChina

Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal *** diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint *** limitations result in delayed diagnoses and inconsistent ***,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and *** advancements in artificial intelligence(AI)offer transformative potential to address these *** review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics *** consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA *** findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.

关键词： subjective clinical assessments analysis omics data degenerative joint disease osteoarthritis imaging techniques clinical assessment biological datasetsmaking diagnostic methodsincluding

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Failure Prediction Mechanism of Disk Devices Based on LSTM 2

Failure Prediction Mechanism of Disk Devices Based on LSTM

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2nd Asia-Pacific Conference on Communications Technology and computer Science, ACCTCS 2022

作者： Xu, Zhenpeng Ma, Jinwei Liu, Yu Jiangsu Automation Research Institute Department of Computer Lianyungang China School of Computer Engineering Weifang University Weifang China

ISBN: (纸本)9781665400343

In order to reduce the impact of the disk device failure on the information system performance fluctuation and business services, a disk device failure prediction mechanism based on LSTM is proposed based on the disk state feature extraction and neural network model. Firstly, based on the SMART information, the performance data and the function attenuation characteristics of the disk, the characteristic index of the disk device is selected and extracted. Then, a disk device failure prediction mechanism is established based on the improved LSTM by using the characteristic index as the training set. Finally, the active failure prediction of disk device was developed. Performance analysis shows the failure prediction mechanism of disk device proposed can obtain online prediction function through the data pre-processing methods such as SMART data item selecting, disk loss feature construction, the data item cleaning, and the sample standardization. The proposal is superior to the traditional neural network model in terms of the error square sum and correction decision coefficient. © 2022 IEEE.

关键词： Feature extraction

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Modeling the Processes of Distribution of Disinformation in the Internet Environment

Modeling the Processes of Distribution of Disinformation in ...

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International Conference on Information Technologies in engineering Education (Inforino)

作者： Alexandr Maximov Anatoliy Shvetcov Department of Automation and Computer Engineering Vologda State University Vologda Russia

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article addresses the issue of the spread of misinformation in the online environment. It proposes the use of agent-based modeling to study and analyze this process. A model of an information agent for the process of spreading misinformation has been developed, represented by a graph of states and transitions of a probabilistic finite automaton. The structure is determined and the relationships between the transition probabilities of this automaton are established. An agent-based simulation model of the processes of dissemination of misinformation in the Internet environment has been developed using the NetLogo 6.3 multi-agent modeling system. The results of simulation experiments and assessment of the adequacy of the software model are presented. Options for using abstract and simulation models in the educational process of the direction “Information Systems and Technologies” are proposed.

关键词： Network topology Navigation Automata Probabilistic logic Mathematical models Software Internet

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Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning 1

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13th International Advanced Computing Conference, IACC 2023

作者： Kumbhar, Sujit Khot, Sanjay Jujare, Varsha Patil, Vishal Husainy, Avesahemad Shedbalkar, Koustubha Department of Technology Shivaji University Maharashtra Kolhapur416004 India Department of Automation and Robotics Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Computer Science and Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Electrical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India

ISBN: (数字)9783031567001

ISBN: (纸本)9783031566998

In the current research work, a single cylinder spark (S.I)ignition engine were used for investigations of premium level gasoline-ethanol-alkane experimentally with different operating conditions e.g. variation spark ignition timing. The Engine Lab and PE3 software were used for engine control and data acquisition system. The data obtained after experimentation were used to predict the engine emissions for different operating conditions. The engine emission characteristics were predicted using three machine learning algorithmsviz linear regression, decision tree and random forest. It was found that emissions characteristics such as carbon monoxide, unburnt hydrocarbon found to be minimum for 24°bTDC experimentally as well as predicted by machine learning algorithms with different operating conditions than other spark timing positions such as 15°, 18°, 21°, 27°, 30° bTDC. All three machine learning algorithms gave better results but the random forest algorithm were more accurate than linear regression and decision trees. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Paraffins

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Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of Science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings International Conference on Software engineering and ACM International Conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (Science and Information Organization). All Rights Res

关键词： Software design

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Lumbar spine localisation method based on feature fusion

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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Ontology-driven Data Management Design in Healthcare Domain: The ADCATER Experience

Ontology-driven Data Management Design in Healthcare Domain:...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Cocco, Leonardo Fantozzi, Paolo Lembo, Domenico Nanni, Umberto Scafoglieri, Federico Maria Department of Computer Control and Management Engineering University of Rome la Sapienza Rome00185 Italy Department of Law Economics Politics and Modern Languages LUMSA University Rome00192 Italy

In this paper, we outline our experience in implementing the data management component of a data-intensive healthcare application within the ADCATER project (Advanced Digital Solutions for Professional Food and Nutrition Catering Service), where an ad hoc ontology, tailored to the project’s domain, plays a crucial role in driving the system design. We will discuss the creation process of this ontology, its underlying building principles, and how it aids the development of a reconciled database useful to integrate and consolidate heterogeneous sources of information, vital for the proper running of solution at hand. Here, the ontology is essential for harmonizing vocabularies and ensuring the establishment of a schema devoid of inconsistencies. Finally, we will explore how Business Intelligence services, operating on the foundation of the Data Warehouse built upon the reconciled database, are seamlessly aligned with the crafted ontology. © 2024 Copyright for this paper by its authors.

关键词： Data warehouses

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Resilient Platooning control of Connected Automated Vehicles in the Presence of Cyber-attacks

Resilient Platooning Control of Connected Automated Vehicles...

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IEEE Conference on Decision and control

作者： Azwirman Gusrialdi Muhammad Iqbal Zhihua Qu Automation and Mechanical Engineering unit Tampere University Finland Department of Electrical Engineering and Automation Aalto University Finland Department of Electrical and Computer Engineering University of Central Florida USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper considers the problem of platooning control of connected automated vehicles under cyber-attacks. Specifically, the attacker aims to prevent the follower vehicle from maintaining a pre-defined safe distance from its immediate predecessor by manipulating the measurement of the on-board radar and inserting bounded injections into the communication links and actuators of the follower vehicles. A novel distributed resilient control is proposed which does not require any assumptions on the number of attacks. It is shown that by appropriately designing the information being exchanged between the vehicles, the resilient control ensures that the follower vehicles converge to the leader vehicle’s velocity and constant distance between the vehicles in presence of any number of attacks. Furthermore, the proposed resilient control also enables attack detection and identification in real-time and distributed manner. A Numerical example demonstrates the effectiveness of the proposed strategy.

关键词： Actuators Radar measurements Radar Real-time systems Cyberattack

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Personalized Privacy Protection Incentive Mechanism for Mobile Crowdsourcing Based on Homomorphic Encryption and Edge Computing

Personalized Privacy Protection Incentive Mechanism for Mobi...

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IEEE International Conference on Web Services (ICWS)

作者： Yingxin Li Weilong Wang Yingjie Wang Tong Xiangrong Peiyong Duan Zhipeng Cai Department of Computer and Control Engineering Yantai University Department of Computer Science and Engineering Southeast University Department of Electrical and Control Qilu University of Technology Department of Computer Science Georgia State University

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the rapid development of crowd sensing computing, Mobile crowdsourcing (MCS) has become an indispensable part of today’s society. While MCS brings convenience to people, it also exposes them to the risk of privacy leakage. In addition, the demand for data is increasing, and the personalized privacy requirements of crowd workers may affect the service quality. In order to address these problems, this paper proposes a personalized privacy protection incentive mechanism (PPPIM) for MCS based on homomorphic encryption and edge computing. Firstly, this paper designs a personalized privacy metric, using social attributes and private attributes of crowd workers to calculate the privacy level required by crowd workers. Then, based on homomorphic encryption and edge computing, a personalized residual federated security learning scheme (PRFSL) is proposed to ensure the security, timeliness, integrity of task data and the privacy of crowd workers’ needs to improve encryption efficiency. Finally, based on the evolutionary game, a personalized privacy incentive mechanism is proposed to improve the overall service utility. Experimental comparisons based on real datasets show that the proposed scheme can not only ensure the security, timeliness, and integrity of task data more effectively. It can also effectively reduce data processing time, improve the probability of crowd workers actively completing tasks and the overall service quality utility.

关键词： Crowdsourcing Measurement Privacy Data privacy Web services Sensors Homomorphic encryption Protection Mobile computing Edge computing

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Hierarchical Multimodal Decoupling-Fusion Framework for offline Multiple Appropriate Facial Reaction Generation

Hierarchical Multimodal Decoupling-Fusion Framework for offl...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Qincheng Liu, Xiaofeng Li, Jie Ni, Rongrong Xue, Pujun Song, Siyang College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350368741

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals' facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions. © 2025 IEEE.

关键词： 2D 3D facial behaviour fusion Facial identity decoupling Multiple Appropriate Facial Reaction Generation

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