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检索条件"任意字段=2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022"

共 4132 条记录，以下是51-60 订阅

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Unsupervised Vehicle Re-Identification with Clustering and Appearance Style Exploration 24

Unsupervised Vehicle Re-Identification with Clustering and A...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Taleb, Hamdan Zhu, Xiaobo Wang, Chao College of Electronic and Information Engineering Tongji University Shanghai China School of Informatics and Engineering Suzhou University Jiangsu Suzhou China

ISBN: (纸本)9798400718120

Vehicle re-identification (Re-ID) has emerged as a critical challenge in the fields of computer vision and intelligent transportation systems, driven by the need to accurately identify vehicles across multiple camera views in diverse urban environments. Traditional methods relying on appearance-based features such as color and resemblance often fall short due to the variability in vehicle appearances caused by factors like illumination changes, camera angles, and occlusions. To address these limitations, this paper proposes a novel approach that leverages unsupervised learning, integrating agglomerative clustering techniques and style transfer via the StarGAN2 model. By treating each camera as a unique style domain, the proposed method generates new training pictures with diverse styles, reducing camera-induced variability and enhancing cross-camera vehicle identification. The repeated learning framework further refines vehicle identities by clustering similar pictures, thereby improving the system’s ability to distinguish between vehicles in real-world scenarios. This approach not only minimizes the need for manual annotation but also enhances the robustness and adaptability of vehicle Re-ID systems, making it a valuable contribution to urban surveillance and intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Unsupervised learning

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Recurrent Convolutional Fact Graph Neural Networks for Temporal KnowledgeGraph Reasoning 24

Recurrent Convolutional Fact Graph Neural Networks for Tempo...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Feng, Siling Ye, Zimin Liu, Qian Huang, Mengxing Information and Communication Engineering Hainan University Hainan Haikou China

ISBN: (纸本)9798400718120

Temporal Knowledge Graph (TKG) reasoning involves predicting future facts by modeling the complex event relationships between historical entities in the real world. TKGs can be viewed as a sequence of knowledge graph snapshots ordered by timestamps. However, reasoning over TKGs remains challenging due to the difficulty of capturing the intricate, multi-level dependencies between entities and relationships in the sequence. Existing TKG reasoning methods often overlook the impact of local fine-grained features on sequence dependencies, which are crucial for accurate prediction. In this work, we propose the Recurrent Convolutional Fact Graph Neural Network (RCF-GNN), designed to enhance the modeling of sequential dependencies in TKGs by capturing local fine-grained features between adjacent subgraphs. The model leverages a ConvGRU network in the encoder to capture both long-term and local fine-grained dependencies through convolutional sliding operations. Additionally, a one-dimensional convolution-based multi-relation aggregator is introduced to improve the extraction of structural dependencies and deep interactive features among concurrent facts. Experimental results on the Wiki, Yago, and GDELT datasets demonstrate the model’s superiority in entity and relation prediction tasks, validating the effectiveness of our model. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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A study on the effect of ideal education on ideal cognitive brain areas of college students based on FMRI information technology and data analysis

A study on the effect of ideal education on ideal cognitive ...

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2022 international conference on algorithms, data mining, and information technology, admit 2022

作者： Jun, Xia Long, Yang School of Information and Communication Engineering Uestc Chengdu China School of Marxism Uestc Chengdu China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

Ideal education, the key component of civic moral education, usually describes in qualitative terms and lacks of quantitative results for empirical evidence. Thus, the research of ideal education is difficult to reflect on the situation accurately and even more hard to guide reality practice. To address this problem, we first combine brain plasticity theory, functional brain localization theory, functional magnetic resonance imaging theory, and FMRI information technology. Subsequently, we propose an magnetic resonance experiments, which focuses the ideal cognitive brain area of college students under various conditions. Through data analysis based on FMRI information technology and data analysis methods, it is shown that ideal education and its methods significantly affect college students' ideal cognitive brain regions. © 2022 IEEE.

关键词： data handling

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28th international conference on information and Software Technologies, ICIST 2022

28th International Conference on Information and Software Te...

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28th international conference on information and Software Technologies, ICIST 2022

ISBN: (纸本)9783031163012

The proceedings contain 26 papers. The special focus in this conference is on information and Software Technologies. The topics include: Enhancing End-to-End Communication Security in IoT Devices Through Application Layer Protocol;rationale, Design and Validity of Immersive Virtual Reality Exercises in Cognitive Rehabilitation;ioT Applications Powered by Piezoelectric Vibration Energy Harvesting Device;holistic Approach for Representation of Interaction Scenarios in Semantically Integrated Conceptual Modelling;a Model-Driven Framework for Design and Analysis of Vehicle Suspension Systems;financial Process mining Characteristics;intelligent Method for Forming the Consumer Basket;Intelligent Invoice Documents Processing Employing RPA Technologies;Topic Modeling for Tracking COVID-19 Communication on Twitter;survey of Cloud Traffic Anomaly Detection algorithms;efficiency of End-to-End Speech Recognition for Languages with Scarce Resources;improvement of Speech Recognition Accuracy Using Post-processing of Recognized Text;technology-Enriched Challenge-Based Learning for Responsible Education;open Course Integration into Formal Education: Case on databases Course;the Ways of Recognition of Open Online Courses;a Case Study on Gaming Implementation for Social Inclusion and Civic Participation;Designing MOOC Based on the Framework for Teacher Professional Development in STEAM;real-Time Anomaly Detection for Distributed Systems Logs Using Apache Kafka and ***;decomposition of Fuzzy Homogeneous Classes of Objects;deep Learning in Audio Classification;research of Cryptocurrencies Function of Instant Payments in the Tourism Sector: Risks, Options, and Solutions;random Forest Classifier for Correcting Point Cloud Segmentation Based on Metrics of Recursive 2-Means Splits;automated System and Machine Learning Application in Economic Activity Monitoring and Nowcasting;Artificial Intelligence Solutions Towards to BIM6D: Sustainability and Energy Efficiency.

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Integrating Attention Mechanisms with Bidirectional Long Short-term Memory Recurrent Neural Networks for Improved Speech Recognition 24

Integrating Attention Mechanisms with Bidirectional Long Sho...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Kimani, Mercy Wairimu Nderu, Lawrence Ndirangu, Dalton Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya United States International University Nairobi Kenya

ISBN: (纸本)9798400718120

Speech-to-text technology is indispensable for converting spoken words into text, facilitating easy storage and retrieval. This process involves several critical stages, progressing from a basic model through signal pre-processing, feature extraction, feature selection, and modeling. There has been extensive literature on improving speech recognition, however, challenges persist, particularly in addressing word error rates, accuracy, latency, and computational efficiency in continuous input streams in a noisy environment. This research systematically evaluates the effectiveness of recurrent neural networks (RNNs), long short-term memory neural networks (LSTMs), gated recurrent units (GRUs), bi-directional long short-term memory (Bi-LSTM), and bi-directional long short-term memory (Bi-LSTM) with attention mechanism networks. It further proposes integrating attention mechanisms with a Bidirectional Long Short Memory Recurrent neural network to enable the model to focus on the crucial parts of the input sequence thus increasing computational efficiency. Experimental results, based on the Libri speech and TIMIT training dataset, demonstrate promising outcomes. The proposed model exhibits superior performance, achieving a word error rate of 1.6%, and an accuracy of 98.4% on the training dataset. As a future avenue of exploration, the study proposes evaluating the use of transformer models to reduce character error rates further and enhance accuracy in continuous input streams. © 2024 Copyright held by the owner/author(s).

关键词： Long short-term memory

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A Tennis Momentum Analysis Method Based on Gaussian Dynamics and Machine Learning 24

A Tennis Momentum Analysis Method Based on Gaussian Dynamics...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Long, Zheng Li, Na Sun, Mengqing Luo, Jiawen Pan, Ting Yin, Yujie Yang, Xinhua School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou China School of Economics and Management Lanzhou University of Technology Gansu Lanzhou China College of Energy and Power Engineering Beihang University Beijing Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing Beijing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China

ISBN: (纸本)9798400718120

Momentum analysis in tennis matches has predominantly focused on qualitative methods, lacking systematic quantitative approaches. This study proposes a novel method that integrates Gaussian dynamics models with machine learning techniques to quantitatively analyze and predict momentum changes in tennis matches. By selecting and defining key indicators such as serve success rate and scoring rate, we developed an algorithm to identify and predict momentum shifts. The proposed method employs ensemble learning techniques, specifically Stacking, to combine multiple machine learning models, enhancing prediction accuracy and execution efficiency. Experimental validation using data from major tennis tournaments, including the 2023 Wimbledon Championships, demonstrates the effectiveness of the proposed approach. The results show that the Stacking model outperforms individual models in accuracy and robustness, providing scientific decision support for coaches and players. This method has low computational requirements, is simple to implement, and considers a wide range of variables, making it highly efficient. Future research will focus on further optimizing the model and applying it to other sports and match types. © 2024 Copyright held by the owner/author(s).

关键词： Gaussian dynamics model

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Optimization of Edge Delay Sensitive Task Scheduling Based on Genetic Algorithm

Optimization of Edge Delay Sensitive Task Scheduling Based o...

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2022 international conference on algorithms, data mining, and information technology, admit 2022

作者： He, Jiayu School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

Task scheduling is a key issue in the edge computing environment, and the goal is to provide optimal task allocation among resources. Reasonable decision-making for task scheduling on edge devices with limited resources can greatly improve offloading efficiency and reduce application processing latency. Aiming at the task offloading decision-making problem in the edge computing environment, a network model and a task scheduling model are established, the task scheduling method is defined to reduce the task delay, and an optimization algorithm is proposed to solve the task completion in the edge task scheduling under the delay-sensitive task constraints time optimal solution. The algorithm adopts the Genetic Algorithm (AG), which encodes the tasks generated by edge devices in binary mode, optimizes the selection function, and shortens the task delay. The experimental results show that the algorithm proposed in this paper can effectively reduce the task execution time and delay compared with other strategies. © 2022 IEEE.

关键词： Genetic algorithms

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Defect detection method of electronic components based on improved YOLOX

Defect detection method of electronic components based on im...

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2022 international conference on algorithms, data mining, and information technology, admit 2022

作者： Wu, Dongliang Liu, Zhigui School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

It is difficult to detect artificially in view of the small defect size of electronic components in industrial environment, this paper proposed a defect detection method of electronic components based on improved YOLOX algorithm. Firstly, a new Spatial Pyramid Pooling structure is used in the feature extraction part, in which Maxpool is replaced by Softpool to retain more feature information. Secondly, in the feature fusion part, the channel attention is added to enhance effective features and improve the detection ability of small size defects. Finally, the α-Complete Intersection over Union is used to optimize loss function and the cosine annealing algorithm is used to accelerate the convergence of the model to achieve the best detection results. The improved YOLOX method is tested on the self-made electronic component defect dataset, the average detection accuracy is 99.05%, and the detection time of each picture is about 0.18 seconds. It is verified in the PCB defect public dataset with good experimental results. © 2022 IEEE.

关键词： Defects

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Design of a technology Talent Pool information Query System Based on Big data mining technology 24

Design of a Technology Talent Pool Information Query System ...

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international conference on Computer and Multimedia technology (ICCMT)

作者： Chen, Man Hubei Acad Sci & Tech Informat Wuhan 430064 Peoples R China

ISBN: (纸本)9798400718267

In order to improve the comprehensiveness and success rate of information query, a technology talent pool information query system based on big data mining technology is designed. By collecting and storing data, cleaning and preprocessing, provide a user-friendly query interface to help users easily and quickly access information from the technology talent pool, protect users' information security and privacy through the security and privacy module. At the same time, the K-means clustering method is optimized using the firefly algorithm to achieve comprehensive mining of information in the technology talent pool. The system performance test results indicate that the system has a high success rate and recall rate in information query, which can provide support and reference for scientific and technological innovation activities, talent selection, and strategic decision-making of scientific research institutions and enterprises.

关键词： data mining technology talent pool information inquiry K-means clustering Firefly algorithm

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The research status of international tourism big data: Visual analysis based on citespace and VOSviewer

The research status of international tourism big data: Visua...

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2022 international conference on algorithms, data mining, and information technology, admit 2022

作者： Caisheng, Liao Xinze, Zhan School of Management Jinan University Guangzhou China School of Business Guangxi University Nanning China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

The research of tourism big data has attracted extensive attention from the Industry and academics. At present, the research status of tourism big data based on knowledge graph analysis are still relatively rare. [Methods/Process] In this study, SCIE and SSCI from the Core set of Web of Science were selected as data sources to screen out 1130 effective literatures related to tourism big data. CiteSpace and VOSviewer are used to analyze the literature visually. the research results show that:(1) The field of international tourism big data research covers a wide range of areas, with diversified research topics and multidisciplinary crossover *** hotspots of research are closely related to information technology. (2) The lack of close collaboration among authors can, to a certain extent, hinder the production of high-quality research results. In the future, we need to conduct more detailed research on the application scenarios and limitations of various *** the same time, we should strengthen the cooperation between institutions and authors. In addition, need to focus on innovation in data processing and analysis methods © 2022 IEEE.

关键词： Big data

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