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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是11-20 订阅

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ADMIT 2024 - conference Proceedings: 2024 3rd international conference on Algorithms, data mining, and Information Technology

ADMIT 2024 - Conference Proceedings: 2024 3rd International ...

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3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

ISBN: (纸本)9798400718120

The proceedings contain 70 papers. The topics discussed include: research on the optimization of driving strategy of intelligent networked vehicles based on big data technology;a tennis momentum analysis method based on Gaussian dynamics and machine learning;sentiment and user churn analysis for online platform business using yelp studies;research on commercial vehicle replacement prediction model based on improved attention mechanism;cross-sectional industrialization factors’ contribution to heat wave risk classification;cross-domain person search data augmentation based on style transfer;recurrent convolutional fact graph neural networks for temporal knowledge graph reasoning;and research on network quality evaluation system based on the entropy weight method.

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Research on the optimization of driving strategy of intelligent networked vehicles based on big data technology 24

Research on the optimization of driving strategy of intellig...

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3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Zhiwen, Xiong Ziling, Feng Guangxi Vocational Normal College Guangxi Nanning China

ISBN: (纸本)9798400718120

Through the exploration of the principle of behavioral decision-making method, we propose an intelligent decision-making framework and design a personalized driving strategy generation model. Combined with big data driving behavior analysis and algorithm optimization, driving behavior pattern recognition and intelligent path planning are realized. Experimental results show that the proposed model has significant effects in improving road condition adaptability, driving safety and user experience. This research provides new ideas and methods for the development of intelligent networked vehicle technology, and lays a foundation for the construction of future intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Driving strategy

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Design of a Real-Time Crime Monitoring System using machine learning 3

Design of a Real-Time Crime Monitoring System using Machine ...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Krishnaveni, A. Manikandan, C. Department ofComputer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system’s ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches. © 2025 IEEE.

关键词： Crime prediction exact location facial data machine learning public surveillance recognition model

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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

3rd International Conference on Language Processing and Know...

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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

ISBN: (纸本)9783031850660

The proceedings contain 29 papers. The special focus in this conference is on Language Processing and Knowledge Management. The topics include: machine learning Serving the School Orientation Process;improving Arabic Fake News Detection Across Context-Aware Attention Deep Model Based on Natural Language Processing;towards a Maude-Based Approach for Formal Modeling Deep Neural Networks;Anti-pattern Based IoRT-Aware Business Process Structure Verification Approach;opinion Analysis Based on a Sentiment Lexical Ontology and Deep learning Models: Tunisian Dialect Case;disfluent-to-Fluent Tunisian Dialect Speech Translation with Fine-Tuning Pre-trained Language Models;BERT-Based Model for Sarcasm Detection in Arabic Texts;typology of Event data Imperfections;towards Sentiment Analysis for Libyan Dialect;text Categorization Can Enhance Domain-Agnostic Stopword Extraction;normalized Orthography for Tunisian Arabic;deep learning Approach for Early Prediction of Depression on Social Network;adapting Large Language Models to Biomedical Domain: A Survey of Techniques and Approaches;tunisian Arabic Understanding: Resources Analysis and Evaluation;from data to Decisions: An Ontology-Driven Method for Opinion mining;LLMs for Cyberbullying Detection in Political Social Media;Assessing BERT Models for Arabic Named Entity recognition in a Multi-dialectal Context;tunisian Normalized Pronunciation;traffScOnto: Ontology for Traffic Management in the Context of Smart City Domain;SERTUS dataset Collection from Spontaneous Environments;an Agricultural Sentiment dataset for Pest Control and Crop Diseases;deep learning Approach to Identify and Classify Arabic Verbal Multi-word Expressions;a Design pattern-Based Approach for Analyzing MapReduce Applications;analyzing the Impact of Big data in Mental Health;The Impact of AI on Knowledge Management;a Rule-Based System for Translating Libyan Dialect Dual Forms to Modern Standard Arabic.

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An Autoencoder Framework for Few-Shot Human Activity recognition with Sensor data 3rd

An Autoencoder Framework for Few-Shot Human Activity Recogni...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Han, Zhengxuan He, Mingshu School of International School Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819624676

As the training sample data in deep learning is not always easy to obtain, and the labeling task of sample data is very labor-intensive, weakly supervised learning method as a branch of machine learning has increasingly garnered interest. The scale of training datasets for different actions in sensor-based human activity recognition (HAR) often varies widely. For some action categories with little sample data, it is difficult for conventional learning methods to learn their features, which will lead to poor model performance in practical applications. In weakly supervised learning methods, the model can still achieve excellent performance without the need for a good deal of various label data in the training stage. Therefore, this study will use weakly supervised learning methods to improve low model performance caused by unbalanced samples. Based on autoencoder, which is a weakly supervised learning method, this research proposed a framework for sensor-based HAR to ameliorate the negative effects of the unbalanced distribution of training samples on the model. Results from experiments on open datasets demonstrated that the proposed framework enhances the original model's low accuracy caused by sample imbalance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Deep learning-Based Automatic Identification System for Marine Plankton 24

Deep Learning-Based Automatic Identification System for Mari...

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3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Xiang, Su Shandong University of Science and Technology Shandong Qingdao China

ISBN: (纸本)9798400718120

Due to their diverse species and complex morphology, the automatic identification of marine plankton has always been a challenging task. In response to this problem, this study proposes an innovative deep learning-based intelligent identification system. The system integrates advanced computer vision and artificial intelligence technologies, possessing the capability of automatic feature extraction and pattern recognition. We extensively collected marine sampling data and combined it with public datasets to construct a large-scale database containing 200 species of plankton and 500,000 images. Through the training and optimization of convolutional neural network models, the system demonstrates outstanding classification performance of 94.2% on the test set. Not only does the system outperform traditional methods in accuracy, but its inference speed has also been optimized through model compression and parallel computing, capable of processing 620 images per second. By deploying containerization and automatic scaling strategies, the system ensures high availability. This research provides strong technical support for marine ecological conservation, fisheries management, and other related fields. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

3rd World Conference on Intelligent and 3D Technologies, WCI...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

ISBN: (纸本)9789819791279

The proceedings contain 45 papers. The special focus in this conference is on Intelligent and 3D Technologies. The topics include: Multi-scale Point Cloud Shape Completion Network Based on Deep learning;research on Application and Effect Evaluation of Product Innovation Management Based on Deep learning;study on Deep learning-Based Personalised Product Recommendation Model for Autonomous Question-and-Answer Robot;application of Image Watermarking Technology Based on Deep learning in Copyright Protection;research on the Combination of Building Structural Health Monitoring and Deep learning Image Processing;electrical Equipment Prediction in a Variable Electromagnetic Field Using Deep learning;state Monitoring and Fault Prediction of Wind Farm Transmission and Transformation Equipment Based on Deep learning;application of Deep learning Algorithms in the Innovation Ecosystem of Electric Power;optimization Strategy for Inventory Management Based on machine learning;intelligent Design and Evaluation of Aging Adaptable Public Spaces Based on Deep learning;performance Optimization and Acceleration of machine learning Algorithms in Task Allocation of Mine Maintenance Robots;Application of Deep learning to Improve the Performance of Automotive Electronic Control Unit (ECU);deep learning-Based Scene Classification for Remote Sensing Images;Improved Swarm Intelligence Optimization Algorithm Based on SL-Relu Activation Function Improvement Strategy and Its Application in Price Forecasting;Automatic Segmentation of Traumatic Penumbra in Rat Brain Based on Improved UNet++;A Brain Tumor Classification Method Based on ResNeXt-SESA Network;A LSTM Algorithm for Coastal City Cultural Scene Value Sustainable Development Forecast Improvement;pattern recognition in Archive Analysis Using data mining;building Crack Detection Method Based on Convolutional Neural Network.

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

3rd World Conference on Intelligent and 3D Technologies, WCI...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

ISBN: (纸本)9789819791231

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Oracle Bone Inscriptions recognition Based on Transfer learning and Improved ResNet50 3rd

Oracle Bone Inscriptions Recognition Based on Transfer Learn...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Li, Zengming He, Xuansen Lu, Haihui Mo, Jianming Wen, Guanjin Huang, Ziyun Zhang, Wenjian Guangzhou College of Commerce Guangzhou511363 China College of Information Science and Engineering Hunan University Changsha410082 China

ISBN: (纸本)9789819624676

Oracle Bone Inscriptions (OBI) are the earliest Chinese characters found so far, they are the treasures of Chinese traditional culture and have high research value. However, the machine recognition of OBI is a difficult problem. To address this challenge, this paper presents a new method of OBI recognition based on transfer learning and improved ResNet50 network. Firstly, the deep convolutional neural network structure of ResNet50 is used to obtain rich feature representations by learning on large general dataset, and then it is transferred to the recognition of OBI, so as to effectively extract the key features of OBI. On this basis, the parameters of ResNet50 model are fine-tuned on a small data set of a specific OBI topology to improve the recognition rate of OBI. The experiment utilized rubbed images of the original OBI as the dataset. The findings demonstrated that the proposed method achieved superior recognition accuracy, reaching 95.641% compared to alternative methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Enhanced data mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

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3rd international conference on Algorithms, data mining, and Information Technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： data aggregation

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