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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 3244 条记录，以下是31-40 订阅

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A New data Compression Method for Face recognition 3

A New Data Compression Method for Face Recognition

引用

3rd international conference on Cloud Computing, Big data Application and Software Engineering, CBASE 2024

作者： Wang, Xuansheng Zheng, Hongying Shenzhen Institute of Information Technology Guangdong Province Shenzhen China

ISBN: (纸本)9798331506582

Previous studies on feature extraction techniques frequently depended on converting data straight from high-dimensional regions into low-dimensional subspaces. However, these approaches often struggled to preserve the intricacies and nuances of the original data that are crucial for accurate analysis. In this groundbreaking study, we introduce a new data compression technique for feature extraction. This method meticulously compresses data samples into a more compact form while diligently preserving their pivotal characteristics, ensuring that no vital information is lost in the process. By achieving this delicate balance between compression and fidelity, our approach enables seamless integration with established dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), significantly enhancing their performance in face recognition tasks. Experimental evaluations underscore the efficiency and efficacy of our data compression method, demonstrating substantial gains in both computational efficiency compared to traditional methodologies, thereby paving the way for more practical and powerful applications in the field of pattern recognition and machine learning. © 2024 IEEE.

关键词： Face recognition

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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...

引用

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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Adaptive machine learning Model for Complex data Processing 3

Adaptive Machine Learning Model for Complex Data Processing

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Rui Wei, Xiuzhuo Yang, Yuzhu Wan, Ruoyan Zhao, Huinan Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

With the rapid development of information technology today, data is emerging at an unprecedented speed and scale, with diverse forms of existence and extremely wide sources. Especially in the medical field, the complexity of data is particularly prominent, including multi-source, incremental, high-dimensional, and significant noise interference. To address these challenges, this article delves into adaptive machine learning (ML) models for complex data processing and focuses on their application in the field of medical diagnosis. ML technology, with its excellent data processing and pattern recognition capabilities, has opened up new paths for the precision and intelligence of medical diagnosis. This paper proposes a medical diagnosis model based on multimodal data fusion and ML to address the complexity of medical data. This model integrates multiple types of data from different sources and utilizes advanced fusion techniques to achieve comprehensive information extraction and deep fusion. The experimental results show that the medical diagnosis model proposed in this paper exhibits excellent performance in various disease diagnosis tasks, significantly improving the accuracy and efficiency of diagnosis. © 2024 IEEE.

关键词： data fusion

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Improving the accuracy of pine wilt disease detection through the integration of multispectral imaging and machine learning 3

Improving the accuracy of pine wilt disease detection throug...

引用

3rd international conference on Image Processing and Media Computing (ICIPMC)

作者： Chu, Shiwei Bao, Wenxia Anhui Univ Sch Elect Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350386660;9798350386677

In this study, the accuracy of pine wood nematode detection was significantly improved by combining a UAV-mounted multispectral imaging system with machine learning algorithms, particularly the deep learning models SSD and YOLO v5. The study employed image preprocessing techniques, such as orthorectification and atmospheric correction, to ensure the accuracy and consistency of the data. On this basis, subtle differences in the health status of pine trees were revealed by analysing the spectral features extracted from the multispectral images. Traditional algorithms such as random forest and support vector machine, as well as deep learning models were used for classification and recognition, in which SSD and YOLO v5 demonstrated high accuracy in processing complex image data, reaching 0.92 and 0.93, respectively. This achievement not only improves the detection efficiency of pine wilt disease, but also provides a new technological pathway for forestry health management, which has an early diagnosis and treatment of the disease. important significance for early disease diagnosis and treatment.

关键词： Multispectral imaging machine learning pine wilt disease

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Design and Implementation of Marketing Classification System Based on Artificial Intelligence 3

Design and Implementation of Marketing Classification System...

引用

3rd IEEE international conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Tian, Shuo Shanghai University Shanghai China

ISBN: (纸本)9798350380989

This paper mainly introduces some machine learning methods used in the field of data mining. The method of data mining is discussed by taking market segmentation algorithm as an example. This paper presents an improved support vector machine method. By judging the similarity between each sampling point and its closest class, the classifier is obtained by training it with support vector machine. A method based on stepwise approximation method is proposed to solve the problem of too large size of KNN training samples. The experimental results show that non-SVM is superior to SVM in classification accuracy and classification speed. In addition, this project will also adopt artificial intelligence technology to build a data analysis system for high-end wine, analyze, compare and match massive information data of high-end wine through data mining, and discover the comparison information and market potential of main ingredients in high-end wine. It is compared with the chemical composition of high-grade red wine, and the relevant marketing treatment of high-grade red wine is obtained. It can help high-grade red wine manufacturers to analyze the composition content and quality of high-grade red wine. © 2024 IEEE.

关键词： artificial intelligence data analysis data mining high-end red wine machine learning marketing classification algorithm

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AI-Enhanced data mining for Fraud Detection in Financial Transactions 3

AI-Enhanced Data Mining for Fraud Detection in Financial Tra...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Gupta, Rajan Goyal, Raghav Malik, Kanta Sahu, Indu School of IT Vivekananda Institute of Professional New Delhi India Gemini Solutions Private Limited. Application Development Haryana Gurugram India

ISBN: (纸本)9798350396157

Financial frauds are on the rise globally, causing significant financial losses. This issue has far-reaching consequences, impacting the investment industry, government, and corporate sectors alike. Manual verifications and inspections are inaccurate, expensive, and time consuming for detecting such fraudulent operations. Artificial Intelligence (AI) and machine learning (ML) techniques should be used intelligently to identify fraudulent transactions by evaluating enormous amounts of financial data. The paper outlines a process of detecting financial fraud with the application of AI and machine learning. Finally, the paper discusses how organizations can leverage AI techniques for the detection of financial frauds. © 2024 IEEE.

关键词： machine learning

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The 3rd international Workshop on machine learning on Graphs (MLoG) 23

The 3rd International Workshop on Machine Learning on Graphs...

引用

16th international conference on Web Search and data mining

作者： Derr, Tyler Ma, Yao Rozemberczki, Benedek Shah, Neil Pan, Shirui Vanderbilt Univ Nashville TN 37235 USA New Jersey Inst Technol Newark NJ USA Isomorph Labs London England Snap Inc Santa Monica CA USA Griffith Univ Nathan Qld Australia

ISBN: (纸本)9781450394079

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd international Workshop on machine learning on Graphs (MLoG)(1), held in conjunction with the 16th ACM conference on Web Search and data mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

关键词： machine learning on Graphs

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Heart Disease Prediction using machine learning Algorithms: A data Science Approach 3

Heart Disease Prediction using Machine Learning Algorithms: ...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Murugananthan, V. Rehan, Mahmoud Yehia Emam Selim Srinivasan, R. Kavitha, M. School of Computing Technology Park Malaysia Kuala Lumpur57000 Malaysia Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

The widespread use of data mining is a direct result of the practice's first success in more public arenas like marketing, e-commerce, and retail. Discoveries in healthcare are among them. data is abundant in healthcare systems. In spite of this, there is a lack of efficient analysis tools for uncovering latent connections in data. In particular, this study aims to describe the Nave Bayes and decision tree classifiers used in our research to predict cardiac events. It has been shown, through experimentation on the same dataset, that Decision Tree is more effective than Bayesian classification when it comes to predictive data mining. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey of Advancements in DBSCAN Clustering Algorithms for Big data 3

A Survey of Advancements in DBSCAN Clustering Algorithms for...

引用

3rd international conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Kulkarni, Omkaresh Burhanpurwala, Adnan Kondhwa-BK Pune India VIIT Department of Artificial Intelligence and Data Science Kondhwa-BK Pune India

ISBN: (纸本)9798350309409

Cluster analysis is an unsupervised machine learning job of grouping objects based on some similarity measure. Among clustering algorithms, DBSCAN (Density Based Spatial Clustering of Application with Noise) contributes to unsupervised machine learning by enabling the clustering of datasets with varying densities, shapes, and sizes. DBSCAN does not require the predefinition of the number of clusters and is able to recognize noiseless arbitrary clusters by using two parameters, minPts and eps. This paper reviews the different DBSCAN algorithms for big data clustering and provides a detailed comparison among the algorithms. © 2024 IEEE.

关键词： data mining

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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 ...

引用

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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