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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是141-150 订阅

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Comparison between machine learning Models and Neural Networks on Music Genre Classification 3

Comparison between Machine Learning Models and Neural Networ...

引用

3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Ma, Zizhi Nankai University School of Software Engineering Tianjin China

ISBN: (纸本)9781665459112

In terms of music genre classification, neural networks and machine learning models have their respective advantages. This paper aims to compare the performance and feature extraction capability between neural networks and traditional machine learning algorithms on music genre classification. All the components of 9 main music features, each with seven statistical values, were extracted as essential features, and different dimension reduction methods were applied. This paper compares the performance of training the features by neural networks and machine learning models. Finally, this paper used the output of layers in the neural networks as features and applied traditional machine learning models for training to see if their performance could be optimized. The result showed that the performance was raised by about 20%, compared to the essential features, and raised by about 5%, compared to the reduced features. So, it can be concluded that the feature extraction capability of neural networks is better than traditional machine learning models. Also, using features filtered by neural networks and applying traditional machine learning models for training is a method providing both excellent performance and high efficiency. © 2022 IEEE.

关键词： Feature extraction

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Design and Implement of Soccer Player AI Training System using Unity ML-Agents 3

Design and Implement of Soccer Player AI Training System usi...

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2022 3rd international conference on computer Information and Big Data applications, CIBDA 2022

作者： Li, Hui School of Computer Science Wuhan Donghu University China

ISBN: (纸本)9783800758760

Reinforcement learning is one of the paradigms and methodologies of machine learning, which is used to describe and solve problems in which agents learn strategies to maximize rewards or achieve specific goals in the process of interacting with the environment. However, the application of reinforcement learning to the real world has many limitations. Unity provides a special toolkit for simulating reinforcement learning, which can simulate the learning environment and train Agents in Unity. This article uses Unity to build an AI training environment for football players, and uses the Unity machine learning Agents toolkit for reinforcement learning. Through the design and implementation of such a system, a complete simulation reinforcement learning solution is provided for AI engineers. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Reinforcement learning

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Research on 3D Semantic Map Based on SLAM and YOLOv4 3

Research on 3D Semantic Map Based on SLAM and YOLOv4

引用

3rd international conference on machine learning and computer application, icmlca 2022

作者： Qin, Lijuan Ma, Zeqi School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510664814

To enable mobile robots to perform different tasks intelligently and autonomously in different work scenarios, we usually use SLAM to achieve simultaneous localization and map building of the robot mainly using to complete the functions. Localization and mapping are done only by detecting and matching feature points in the image, which only utilizes the geometric information in the environment and cannot obtain the linguistic information in the environment, resulting in the robot not being able to perceive the surrounding environment well. Location and mapping can only be done by detecting and matching the feature points in the image. Only the geometric information in the environment is used, and the linguistic information in the environment cannot be obtained, which makes the robot unable to perceive the environment well. In this paper, a three-dimensional semantic map is implemented based on the system design. The data uses RGBD image sequence, and the target detection algorithm YOLOv4 is applied to ORB-SLAM2 system to extract the semantic information in the environment. A three-dimensional dense point cloud map constructed by ORB-SALM2 system is segmented using an improved LCCP algorithm. Semantic information in the environment is projected to the three-dimensional point cloud according to the globally consistent camera posture, and a three-dimensional dense point cloud map with semantic information is constructed. © 2023 SPIE.

关键词： Robots

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Identification Method of Dress Pattern Drawing based on machine Vision Algorithm 3

Identification Method of Dress Pattern Drawing based on Mach...

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Lyu, Ke Yan, Haizhang Zhejiang Sci-Tech University School of International Education Zhejiang310000 China Xidian University School of Computer Science and Technology Xi'an71000 China

ISBN: (纸本)9781665459112

This paper uses the machine vision method to identify the skirt module. We have constructed three kinds of machine recognition models of skirt profile processing, structure analysis of style drawing, and size estimation. The author constructs a relatively complete image recognition system for dress pattern drawing. In addition, we conducted an effect evaluation with a certain number of samples at the later stage of the experiment. This study has A good effect in distinguishing an A-type skirt from an H-type skirt, identifying the reasonable degree and length of the skirt, and determining the quantity statistics of each component element in the skirt pattern diagram. © 2022 IEEE.

关键词： Image recognition

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The application of white-box encryption algorithms for distributed devices on the Internet of Things 3

The application of white-box encryption algorithms for distr...

引用

3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Xu, Zheng Tongji University Software Engineering Shanghai China

ISBN: (纸本)9781665459112

With the rapid development of the Internet of Things and the exploration of its application scenarios, embedded devices are deployed in various environments to collect information and data. In such environments, the security of embedded devices cannot be guaranteed and are vulnerable to various attacks, even device capture attacks. When embedded devices are attacked, the attacker can obtain the information transmitted by the channel during the encryption process and the internal operation of the encryption. In this paper, we analyze various existing white-box schemes and show whether they are suitable for application in IoT. We propose an application of WBEAs for distributed devices in IoT scenarios and conduct experiments on several devices in IoT scenarios. © 2022 IEEE.

关键词： Cryptography

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Discerning Art Works through Active machine learning 3

Discerning Art Works through Active Machine Learning

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Yu, Zihao Grinnell College GrinnellIA50112 United States

ISBN: (纸本)9781665459112

Scene classification is a popular and important question in computer vision and has been developed in different areas. Applying computer vision to artworks has become a popular topic in recent years. However, the traditional random sampling to identify the artworks through machine learning requires a large data set and, therefore, a higher cost to get a solid result. This paper compares random sampling and active learning (uncertainty sampling) performance using a data set (8446 paintings) of the 50 most influential painters in Europe from the 13th to the 20th century. and then propose that the active learning strategy can build a stronger model that requires smaller data sets. The active learning model can be further improved through training in larger data sets and applied in the artwork recognition for artificial intelligence.. © 2022 IEEE.

关键词： computer vision

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Auto Parts Defect Detection Based on Few-shot learning 3

Auto Parts Defect Detection Based on Few-shot Learning

引用

3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Xu, Jiancheng Ma, Jialei Changchun University of Science and Technology College of Science and Technology Jilin Changchun China

ISBN: (纸本)9781665459112

In the process of auto parts products from manufacturing to quality inspection, it is inevitable that there will be defects on the surface due to aging of machine tools or human factors. How to better apply computer vision technology to defect detection of auto parts is still an issue discussed by the industry and academia. For the condition that sufficient labeled samples is usually not available in the industrial field, this paper uses an improved few-shot metric learning model to identify auto parts samples. According to the traditional few-shot learning models lack of the considerations of channel level and internal relations of support set, this paper adds ECA module and Transformer module to few-shot learning. Add more attention to the key information of support set and query set samples at channel level while increasing the attention to the intrinsic feature of support set, to improve the classification effect. © 2022 IEEE.

关键词： machine tools

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Research on Abstractive Summarization Technology Based on Deep learning 3

Research on Abstractive Summarization Technology Based on De...

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Chen, Yifei University of Arizona College of Science TucsonAZ85721 United States

ISBN: (纸本)9781665459112

Abstractive text summarization is an important research field in natural language learning. Compared with the extracted text summary, it is more in line with the preparation steps of the manual summary. In traditional machine learning, supervised learning is the main method. Text annotation, feature extraction, classification training, and weight extraction are also very different from those based on deep learning. Starting with the task of abstractive text summarization based on deep learning, this paper summarizes the current research status of abstractive text summarization, in-depth analysis of multiple designs of encoder-decoder for current deep learning methods, and points out the difficulties of current research and the hot direction of future development. © 2022 IEEE.

关键词： Extraction

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Plant Disease Detection Using machine learning Techniques 3

Plant Disease Detection Using Machine Learning Techniques

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Varshney, Divyanshu Babukhanwala, Burhanuddin Khan, Javed Saxena, Deepika Singh, Ashutosh Kumar Nit Kurukshetra Department of Computer Application Haryana India

ISBN: (纸本)9781665494991

Plant diseases may have a major impact on food safety, also a considerable decline in agricultural product output. The great majority of automated systems developed thus far are based on digital pictures, allowing for the rapid deployment of algorithms. The difficulty of autonomous illness identification in plants has been solved using traditional machine learning approaches such as (SVM) support vector machines, Multilayer Perception Neural Networks, and Decision Trees. The focus of this article was on leaf plant disease. A new plant leaf disease detection technique has been developed that is based on a transfer learning methodology such as deep learning, where CNN is employed as a feature extractor and SVM is used for classification. A benchmark dataset called PlantVillage was used to assess the evaluation of the proposed model. The suggested model was examined and compared to current methodologies, and it outperformed previous work, achieving an 88.77 percent training accuracy. © 2022 IEEE.

关键词： Data mining

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application of Video Analysis Based on Mobile Edge Computing 3

Application of Video Analysis Based on Mobile Edge Computing

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3rd IEEE international conference on computer and Communications (ICCC)

作者： Rao, Zhuoyi Guo, Zhigang BUPT Sch Informat & Commun Engn Beijing Key Lab Network Syst Architecture & Conve Beijing Peoples R China

ISBN: (纸本)9781509063529

Mobile Edge Computing (MEC) provides cloud computing capabilities within the access network, which can offer better service combined with applications those need ultra-low latency and large calculation. In this paper, we discuss the detection and recognition models in different scenarios and develop the video analysis application with fusion model to improve the recognition accuracy based on the MEC platform-WiCloud. Test and verify the application's performance in the service environment with low latency and high-bandwidth provided by the WiCloud.

关键词： MEC SDN feature detection machine learning Gaussian Mixture Model

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