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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 3121 条记录，以下是61-70 订阅

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Research on Automatic Workpiece Sorting System Based on machine Vision 3

Research on Automatic Workpiece Sorting System Based on Mach...

引用

2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Ji, Linfeng Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (纸本)9798350341546

This paper expounds the automatic recognition method of parts based on computer vision. The feature database of the processed parts is constructed by using machine learning method. Image preprocessing, threshold segmentation, edge and contour extraction are used to analyze the features of the image of the inspected part to judge whether the inspected part is qualified or not. Aiming at the attitude error generated when the workpiece is placed, a method for positioning the center of gravity and rotation angle of the workpiece based on boundary detection and contour tracking is proposed. Use the similarity measure to determine the characteristics of the object to be processed. Simulation results show that the method proposed in this paper is effective. © 2023 IEEE.

关键词： image processing machine vision pattern recognition workpiece

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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...

引用

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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Big data Analytics and mining Methods, Applications and Trends 3

Big Data Analytics and Mining Methods, Applications and Tren...

引用

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

作者： Li, Jian Wang, Siyi Wang, Jinxiao Zeng, Dexian Space Engineering University BeiJing China

ISBN: (纸本)9798331506582

In the era of big data information, along with the continuous development and application of 5G, online teaching, machine learning and other high-tech, the amount of information and data in many fields, such as education, scientific research, medical care, communication, e-commerce and so on, is growing geometrically. How to analyse and research these massive data more intelligently and efficiently, and mine the data information with practical application value, is the current key issue in the field of big data technology. Therefore, we need to further study and explore more efficient and intelligent data analysis and mining methods and techniques in order to better use big data technology to support the development of various fields. © 2024 IEEE.

关键词： data Analytics

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Based on the Application of machine learning Algorithms in Business data Analysis 3

Based on the Application of Machine Learning Algorithms in B...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Zhang, Yiwen The University of Auckland Auckland Cbd Auckland1010 New Zealand

ISBN: (纸本)9798350319125

With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machine learning algorithm has been widely used in the field of business data analysis. This paper will introduce the application of machine learning algorithm in business data analysis, including classification, clustering, prediction and association rule mining. © 2023 IEEE.

关键词： artificial intelligence Business data analysis commercial market machine learning algorithms

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Construction and Validation of Big data Analytics in Air Traffic Control Flow Prediction Models 24

Construction and Validation of Big Data Analytics in Air Tra...

引用

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

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Air traffic control flow prediction is one of the key issues in aviation transportation management, crucial for optimizing resource allocation, enhancing control efficiency, and ensuring flight operation safety. This paper proposes a solution for air traffic control flow prediction based on big data analysis and machine learning techniques. Through extensive feature engineering and model construction using large-scale historical data, multiple prediction models including Support Vector Regression, Random Forest, Gradient Boosting Decision Tree, and an ensemble model were trained. Comprehensive performance evaluations using cross-validation and multiple metrics were conducted. Experimental results demonstrate that the proposed ensemble model effectively combines the strengths of multiple base models, significantly outperforming individual models in terms of prediction accuracy, stability, and generalization ability. This provides an efficient and reliable technical path for air traffic control flow prediction, with broad application prospects. © 2024 Copyright held by the owner/author(s).

关键词： Resource allocation

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Deep learning-Based Automatic Identification System for Marine Plankton 24

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

引用

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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data mining Technology in the international Student Evaluation Using Artificial Neural Network with Latent Dirichlet Allocation 3

Data Mining Technology in the International Student Evaluati...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Dong, Hao Yan, Tingxing Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350318609

Recently, Educational data mining (EDM) has increased remarkable consideration. The significant aim of educational institutions is to offer efficient education for students to improve academic performance. When data mining approaches are used in the context of education, they can discover hidden knowledge and patterns that can aid in decision-making processes focused at improving an educational system. Forecasting the student's achievements is a significant problem for higher education administrators. To address this challenge, this research proposes the machine learning (ML) method of Artificial Neural Network (ANN) with the Latent Dirichlet Allocation (LDA) approach for the prediction of the performance of the international student's academics. The presented approach considered the last semester for the international student's performance as well as their personal features were examined. The pre-processing techniques like cleaning, integration, transformation, feature scaling as well as reduction were utilized for the prevention of anomalies as well as high dimensionality reduction in the collected data and to acquire the most correlated features. The experimental results shows that the proposed ANN-LDA approach achieves the accuracy of 95.7% when compared to the previous works like K-Nearest Neighbor (KNN) and Support Vector machine (SVM). © 2024 IEEE.

关键词： data mining

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Kannada Continuous Speech recognition Using Deep learning 3rd

Kannada Continuous Speech Recognition Using Deep Learning

引用

3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Birla Inst Technol Ranchi Mesra India Natl Inst Technol Durgapur India

ISBN: (纸本)9783031640667;9783031640674

Automatic Speech recognition (ASR) is a prevalent approach for attaining human-machine interaction by enabling machines to transcribe speech data. We propose a Continuous Speech recognition model in the Kannada language using deep learning techniques such as Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (Bi-GRU). The model was trained and validated using 100 and 20 h of data, respectively. The experiment has generated encouraging results with a Character Error Rate (CER) of 15.62% and a Word Error Rate of 34.47% (WER).

关键词： Kannada Automatic Speech recognition Continuous Speech recognition Deep learning

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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's machine learning Kit machine learning(MLKit) for Optical Character recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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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...

引用

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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