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检索条件"任意字段=5th International Conference on Machine Learning and Computer Application, ICMLCA 2024"

共 2900 条记录，以下是21-30 订阅

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Robotic Depalletizing via Reinforcement learning of a Pushing Policy 5th

Robotic Depalletizing via Reinforcement Learning of a Pushin...

引用

5th 5th Olympus - international conference on Supply Chain

作者： Dimou, Argyris Kiatos, Marios Malassiotis, Sotiris Informat Technol Inst ITI Ctr Res & Technol Hellas CERTH Thessaloniki 57001 Greece Univ Thessaloniki Dept Phys Thessaloniki 54124 Greece Progress Robot Thessaloniki 54645 Greece

ISBN: (纸本)9783031693434;9783031693441

Depalletizing, the process of unloading objects from pallets, is a critical task in warehouse automation. Robotic systems face challenges due to the diverse shapes and sizes of objects, often leading to failed grasps in cluttered environments. Pushing actions offer a promising solution to facilitate object singulation. In this work, we employ reinforcement learning (RL) for obtaining optimal pushing policies in depalletizing. We formulate the singulation task as goal reaching, where the target object is moved towards a goal area free from obstacles. Our approach, Goal-FCN (Goal-Driven Fully Convolutional Network), combines feature extraction with a fully convolutional neural network to predict effective pushing actions based on RGB-D images and scene segmentation. Experiments demonstrate the efficacy of our RL-based approach in achieving high success rates in challenging depalletization scenarios.

关键词： Robotics machine learning Reinforcement learning

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Research on Analyzing Influencing Factors and Predicting Methods of Data Resource Prices Based on Interpretable machine learning 24

Research on Analyzing Influencing Factors and Predicting Met...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Zhao, Xueshan School of Economics and Management Guangzhou Institute of Science and Technology Guangdong Guangzhou China

ISBN: (纸本)9798400709999

Aiming at the current problem of the lack of differentiated and reasonable pricing proposals for data resources in different industries, we propose a data resource price prediction method based on interpretable machine learning by analyzing the influencing factors. Based on the dataset transaction information of China Guoxin Youyi data website, we propose a data resource price prediction and feature analysis model based on XGBoost and SHAP. the best prediction model is obtained by analyzing and preprocessing the raw data;then the processed data are used as inputs to XGBoost and iteratively trained;then the SHAP results are used to analyze the influencing factors on the datasets of different industries, and the combination of the predicted features is optimized using the last recursive deletion method. the results show that the XGBoost-based data resource value prediction model outperforms five major comparison models after tuning the hyperparameters;the introduction of SHAP model enhances the interpretability of the model to identify and characterize the key factors affecting the price of data resources in different industries;the best combination of predictive effect features in each industry is obtained through the last-rank recursive feature deletion;and the best prediction model is developed for the data trading It provides decision-making reference for data trading websites to efficiently filter high-quality and high-value data and increase revenue. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Construction of Financial Statement Analysis and Prediction Model Based on Deep learning Algorithm 24

Construction of Financial Statement Analysis and Prediction ...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Liu, Jingjing Li, Yanwei Yang, Degang Weifang Engineering Vocational College Shandong Qingzhou China Shandong Lei Ao New Energy Co. Ltd. Shandong Weifang China

ISBN: (纸本)9798400709999

In order to understand the construction of financial statement analysis and prediction models based on deep learning algorithms, a research on the construction of financial statement analysis and prediction models based on deep learning algorithms is proposed. this paper first proposes a model for the intelligence of the financial information of enterprises based on deep learning. the next generation method transforms non-time indicators into real-time indicators and divides and evaluates financial data using neural networks with 4 hidden layers. Real financial data was used to evaluate the data and analysis, and the experimental results showed that the intelligent method focused on abnormal financial data based on deep learning is feasible. best and correct. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Analysis and Research on Generative Artificial Intelligence in the Field of international Library and Information Science 24

Analysis and Research on Generative Artificial Intelligence ...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Yan, Erkai Gao, Mengxiao Tang, Mei Library Northwestern Polytechnical University Shaanxi Xi'an China

ISBN: (纸本)9798400709999

Generative artificial intelligence is an artificial intelligence technology based on deep learning whose core lies in leveraging computer algorithms and training data to generate new, practically valuable content, encompassing text, images, audio, videos, etc. this technology is poised to exert profound impacts on the transformation and development of libraries. Drawing on generative artificial intelligence research publications in the field of international library and information science included in the Scopus database as the data source, this paper employs CiteSpace software and SciVal tools to conduct a visual analysis of literature outputs, core authors, journal sources, and keywords. the results show that generative artificial intelligence research in the international library and information science field is applied primarily in areas such as reference services, information literacy education, and smart libraries. Recommendations are made to promote the application and development of generative artificial intelligence technology in libraries by strengthening technological research and application, boosting data analysis and data sharing, emphasizing information security and privacy protection, promoting cross-boundary integration and ecological development, etc. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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the Role of AI Interventions in Reducing Abusive Supervision: Leveraging machine learning for Enhanced Employee Performance in the Digital Economy 24

The Role of AI Interventions in Reducing Abusive Supervision...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Lu, Ling Xie, Ling Yuan, Dan Peng, Ling Lin, Haibo Guangdong University of Science and Technology Guangdong Dongguan China Faculty of Business City University of Macau China

ISBN: (纸本)9798400709999

this study explores the role of AI interventions within the digital economy in reducing abusive supervision and enhancing employee performance. Based on empirical research using a sample of 855 employees across various industries, the study examines how AI technology not only mitigates abusive supervision but also drives performance improvement. through multiple regression analysis and Bootstrap mediation tests, the results confirm that AI interventions significantly reduce abusive supervision and enhance employee performance. Additionally, the study introduces Perceived Organizational Support (POS) as a moderating variable, revealing that POS has a significant moderating effect on the relationship between AI interventions and performance. these findings offer valuable theoretical support and practical insights for managers navigating the digital economy, highlighting the benefits of leveraging AI technology to strengthen organizational support, reduce negative supervisory practices, and optimize employee performance. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Regression analysis

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A Byzantine Client Detection Method for Federated learning Based on Encrypted Repeated Median Linear Regression 24

A Byzantine Client Detection Method for Federated Learning B...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Guo, Jinlong Yang, Ming Li, Huijuan Wu, Xiaoming He, Yunpeng Wang, Xin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Shandong Jinan China Shandong Evayinfo Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

Federated learning is an emerging distributed machine learning technology that allows participants to jointly train machine learning models locally without the need for large-scale data transmission and sharing, thereby greatly improving data security. However, some Byzantine clients may intentionally tamper with local model parameter updates with the intention of manipulating the global model's update direction, which can lead to a decline in the overall performance of federated learning. Besides, existing Byzantine client detection research is conducted in plain text, which will lead to the leakage of private information. To solve the above issues, we propose a Byzantine client detection method for federated learning based on encrypted repeated median linear regression. Specifically, it combines the repeated median linear regression algorithm with homomorphic encryption, which can detect and eliminate clients with Byzantine behavior in the ciphertext state, ensuring the security and reliability of the parameters in the entire federated learning process. Additionally, we also designed an adaptive gradient clipping method based on historical iteration to clip the gradient to a reasonable range, which can not only reduce communication and computing overhead, but also improve the convergence speed and stability of the model. Experiments show that the proposed method can maintain high accuracy under the influence of malicious behavior of Byzantine clients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Study on the application of transfer learning in small sample image classification of military equipment 5

Study on the application of transfer learning in small sampl...

引用

5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Lu, Leiji Yu, Hongxia Xiao, Hongju Bao, Lei Department of Information Engineering Army Academy of Artillery and Air Defense Hefei China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

To address the issue of insufficient military equipment sample data, which cannot meet the training requirements of deep neural networks and tends to cause overfitting, this paper introduces transfer learning technology to solve the small-sample classification problem for military equipment. By constructing a multi-type sample training set and fine-tuning the convolutional layers of pre-trained models, specific target classifiers are trained. Practice has proven that the application of transfer learning in small-sample classification tasks saves model training time, resolves issues related to model overfitting and strong dependence on data labels, and effectively improves the accuracy of image classification based on deep learning for small samples of military equipment. © 2025 SPIE.

关键词： Deep neural networks

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Research on the application of Intelligent Writing Teaching System Based on computer Vision and Natural Language Processing in Higher Vocational English 24

Research on the Application of Intelligent Writing Teaching ...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Wen, Liuchun Guangdong Polytechnic of Water Resources and Electric Engineering Guangdong Guangzhou China

ISBN: (纸本)9798400709999

In the context of vocational English teaching, writing skills are crucial for students' professional communication ***, traditional teaching methods often face challenges such as delayed feedback and insufficient *** the development of artificial intelligence technologies, the integration of computer vision (CV) and natural language processing (NLP) into writing education provides a novel solution to these *** study explores the application of a CV- and NLP-based intelligent writing assistance system in vocational English writing *** system enhances grammar correction, semantic understanding, and automated feedback, significantly improving students' writing performance while reducing teachers' *** experimental evaluation demonstrated that the system effectively improved writing accuracy, logical clarity, and overall learning *** findings indicate that AI-driven educational tools have great potential in transforming traditional language teaching practices. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Apprentices

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Research on Question Answering System of Intelligent Robot Based on Deep learning 5

Research on Question Answering System of Intelligent Robot B...

引用

5th IEEE international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Dexing, Bian Wenjing, Wang Honghao, Bai Tianjin Richsoft Electric Power Information Technology Co. Ltd Digital Applications Business Unit Tianjin China

ISBN: (纸本)9798331533694

the preset function modules of intelligent customer service robot question-answering system are usually executed on the basis of the imported question-answering objectives, and the question-answering has deviation, which leads to the stable recall rate. therefore, the design and research of intelligent customer service robot question-answering system based on deep learning are proposed. According to the current measurement, we access the central processing unit, set up DM368 memory control and voice circuit, and realize the design of system hardware;Combined with deep learning technology, the question-and-answer deviation is controlled, and the question-and-answer function module of customer service robot is designed, and the application connection with dynamic switching database is established to realize the design of system software. the system test shows that: For randomly selected test groups, in the context of 6 time periods, the deep learning intelligent customer service robot QA system designed this time has a faster task processing speed and a higher recall rate, which shows that the system itself has strong pertinence and stability, and the QA processing efficiency is obviously improved, which has practical application value and innovative significance. © 2025 IEEE.

关键词： Sales

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Vehicle classification method based on deep learning 5

Vehicle classification method based on deep learning

引用

5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Gu, Yehua Nanjing Normal University Nanjing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this paper, I propose a vehicle classification method based on deep learning, which uses attention-enhanced convolutional neural network (CNN) to realize high-precision vehicle recognition. the model automatically focuses on key vehicle features, significantly improves classification accuracy, and reduces computing costs through structural and training strategy optimization, making it suitable for mobile devices and edge computing. the experimental results show that the model performs well in the identification of various types of vehicles, especially in a specific category of 100% accuracy, showing the application potential in intelligent transportation and vehicle management services. © 2025 SPIE.

关键词： Convolutional neural networks

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