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9th International Conference on Big data and information Analytics, BigDIA 2023

作者： Li, Zhanzhe Li, Kai Liu, Lihua Wang, Mao Liu, Qingbao National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

The maintenance big data management of aviation equipment has always been a major issue in equipment maintenance reform. Especially, the analysis of maintenance errors based on aviation equipment maintenance database is particularly important. This paper proposes an association analysis method based on Apriori algorithm for date mining of a certain aviation equipment maintenance monthly report. Firstly, the BI-LSTM-CRF neural network is used for feature entity recognition. Then, the Apriori algorithm is used for association rule analysis to mine potential laws between multidimensional data, and radar charts are drawn to customize comprehensive evaluation indicators for overall visual analysis of different aviation equipment. Finally, a case demonstration is presented at the end. The proposed method aims to mine the hidden value information in unstructured text materials for key data such as equipment fault location, fault type, and impact degree. © 2023 IEEE.

关键词： data mining

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Ontology-based Methods for Healthcare System

Ontology-based Methods for Healthcare System

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Abhilash, C.B. Mahesh, Kavi Computer Science and Engineering Indian Institute of Information Technology Dharwad Dharwad India Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad India

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Analytical in the healthcare arena involves data in a variety of formats, and organising them into understandable patterns is a difficult process. data from numerous sources can be merged for interoperable access by using standard nomenclature. In this research, we use ontology approaches to model data by creating a domain ontology. The competency questions for domain ontology are designed to emphasise the significance of using ontology. A framework for evaluating domain experts is also discussed. © 2022 IEEE.

关键词： Ontology

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Discovering Persistent Subgraph Patterns over Streaming Graphs 28th

Discovering Persistent Subgraph Patterns over Streaming Gra...

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28th International Conference on database systems for Advanced Applications, DASFAA 2023

作者： Huang, Chu Zhang, Qianzhen Guo, Deke Zhao, Xiang Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many real graph applications. Prior subgraph discovery problem over streaming graphs mostly focuses on characteristics like frequency and burstiness. Persistence, as a new characteristic, is getting increasing attention. Persistent subgraph discovery highlights behaviors where a subgraph appears recurrently in many time windows, which is vital for many real-world applications (e.g., anomaly detection). While persistent subgraph discovery enjoys many interesting real-life applications, there is no off-the-shelf solution to compute the persistent pattern efficiently. In this work, we are the first to study the persistent subgraph pattern discovering problem over the streaming graph. We devise an auxiliary data structure called TFD to detect the persistent subgraph patterns in real-time with limited memory usage. TFD maps each subgraph into the corresponding bucket based on hash functions to compute the persistence of each pattern. Then we introduce optimizations to separate persistent and non-persistent patterns, further improving the effectiveness and throughput in space-scarce scenarios. Extensive experiments confirm the superiority of our proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Parallelism or Fairness? How to Be Friendly for SSDs in Cloud Environments

Parallelism or Fairness? How to Be Friendly for SSDs in Clou...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Zhou, Yang Wang, Fang Shi, Zhan Feng, Dan Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350358711

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%. © 2024 IEEE.

关键词： Cloud platforms

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2025年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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Backtracing Social Events of Interest via Logical Correlation Using GDELT 9

Backtracing Social Events of Interest via Logical Correlatio...

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9th International Conference on Big data and information Analytics, BigDIA 2023

作者： Zhao, Dongxu Zhang, Xin Pan, Yan Shi, Honglin Qian, Liwei Yang, Guang National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Defense Technology College of Systems Engineering Changsha China

ISBN: (纸本)9798350330076

In response to the inability of mainstream methods in existing social event backtracing studies to address the logical correlation problem, the insufficient and inaccurate utilization of event characteristics for implementing logical association backtracing events, we propose a backtracing method for social events of interest. The basic idea is to obtain a set of related events given a small amount of labeled data on interested events, and to construct the causal chain of event occurrence through their merging and fusion. Specifically, we design a node feature construction strategy to select topic and title information from various elements of events to construct node features, thereby addressing the problem of insufficient and inaccurate node feature composition. We also design an adjacency relationship construction strategy, which uses the co-occurrence of participants in event records as the basis for constructing adjacency relationships, and completes the final construction of adjacency relationships by considering time elements comprehensively, thereby resolving the problem of inaccurate construction of inter-event correlation relationships. We construct a multi-layer graph convolutional network to learn the associated features and achieve node classification through a normalization function, thereby solving the problem of logical association inference between events. Experimental results demonstrate that LC-BTE is effective in backtracing analysis of events related to social events of interest. Additionally, this paper explores the performance changes of the method under semi-supervised mode and conducts experimental analysis. Finally, this paper provides the causal chain of events based on the backtracing results. © 2023 IEEE.

关键词： backtracing GDELT logical correlation social events

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An efficient memory data organization strategy for application-characteristic graph processing

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Frontiers of Computer science 2022年第1期16卷 191-193页

作者： Peng FANG Fang WANG Zhan SHI Dan FENG Qianxu YI Xianghao XU Yongxuan ZHANG Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage SystemEngineering Research Center of data storage systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor... 详细信息

关键词： application processing pattern

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Augmenting the Perceptual Experience of Being Faced Using Gaze Modeling during Online Video Viewing

Augmenting the Perceptual Experience of Being Faced Using Ga...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuto Nakajima Masakazu Hirokawa Modar Hassan Kenji Suzuki Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan Data Science Laboratories NEC Corporation Japan Institute of Systems and Information Engineering University of Tsukuba Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Live Performances have been conducted online in recent years due to the influence of the COVID-19 pandemic. This generated a novel problem of losing eye contact with the performers, which deteriorates the viewing experience in the online setting. This study proposes a feedback system based on gaze modeling to augment the perceptual experience of being faced by the performer during an online live performance viewing. This system aims to improve the experience of viewing online live performances by recreating a perceptual experience of interaction between the performer and the audience. The proposed system consists of a gaze recognition unit that judges whether the performer is looking at the camera based on gaze modeling in the video, and a stimulus presentation unit that presents the judgement result to the audience through vibration stimulus. This paper describes the gaze recognition model, experiments to determine the parameters to be used in the model, and the evaluation of live performance viewing experiments using the proposed system.

关键词： Vibrations COVID-19 Analytical models Visualization Sensitivity Pandemics Cameras Cybernetics

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Deep Learning for Automated Classification of Kidney Lesions in Chronic Kidney Disease Patients 2

Deep Learning for Automated Classification of Kidney Lesions...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Lakshmi, B. Seetha Anand, Vijay Omotunde, Habeeb Birundadevi, M. Mahalakshmi, S. Brahmam, Madala Guru Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Namakkal India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Department of Information Systems Riyadh Saudi Arabia Sri Sairam Siddha Medical College and Research Centre Department of Maruthuvam Chennai India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Deep Learning was utilized to autonomously classify renal abnormalities, which improved the diagnostic accuracy for individuals with chronic kidney disease (CKD), according to this study. During training on a meticulously selected dataset, the proposed model outperformed the most recent approaches. By utilizing medical imaging data - specifically quantitative and qualitative data - to differentiate between discrete kidney abnormalities, the model demonstrated exceptional levels of accuracy, precision, and recall. Furthermore, apart from effectively resolving ethical considerations pertaining to data privacy, this innovative methodology has the potential to fundamentally transform the existing landscape of chronic kidney disease (CKD) diagnosis and treatment. The results demonstrate that the model effectively manages intricate renal disorders, signifying a noteworthy progression in the field of medical image processing. This research paves the way for additional fruitful lines of inquiry, which ought to enhance nephrology diagnostics and patient care. © 2024 IEEE.

关键词： Automated Classification Chronic Kidney Disease Deep Learning Diagnostic Accuracy Kidney Lesions Medical Image Analysis

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