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

共 1857 条记录，以下是251-260 订阅

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Research on application of Meta-learning in Traffic Light Recognition 3

Research on Application of Meta-learning in Traffic Light Re...

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3rd international conference on Digital Signal and computer Communications, DSCC 2023

作者： Su, Xiaole Wang, Guiping Yang, Ruonan He, Siyu Hu, Jinzhuo School of Electrics and Control Engineering Chang’an University Shaanxi Xi’an China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

Traffic light recognition is essential to intelligent vehicle information perception, and its accuracy directly affects traffic safety. Based on sorting out and analyzing the existing related research on traditional machine learning and deep learning in traffic light recognition, the basic principles of two standard algorithms in meta-learning are introduced in detail. Compared with traditional machine learning, using the MAML algorithm and Reptile algorithms in meta-learning to recognize traffic lights by selecting the WPI dataset and programming in PYTHON. The simulation results show that the recognition accuracy based on the meta-learning algorithm is higher than that based on the traditional machine learning algorithm;In the meta-learning algorithm, the Reptile algorithm outperforms the MAML algorithm. © 2023 SPIE.

关键词： learning algorithms

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Smart System for Dengue Fever Diagnosis: A machine learning Approach 3

Smart System for Dengue Fever Diagnosis: A Machine Learning ...

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3rd international conference on Emerging Smart Technologies and applications, eSmarTA 2023

作者： Al-Hagree, Salah Alalayah, Khaled M. Al-Majmar, Nashwan Ahmed Mohsen, Ayedh Abdulaziz Aqlan, Amal Alhel-Iani, Mostafa Mohammed, Merown Albazel, Mohammad Al-Qasem, Fahd Mohammed Aljafari, Motea Musleh, Abdulbaset Alnedam, Ibrahim Computer Science Department Faculty of Sciences Ibb University Yemen Computer Science Aljazeera University Yemen Computer Science Ibb University Yemen Computer Science Najran University Saudi Arabia Department of Computer Sciences King Khalid University Community College Mohayel Aseer Saudi Arabia Information Technology Department University of Science and Technology Yemen

ISBN: (纸本)9798350305333

Dengue fever is a serious illness that can lead to death in areas where epidemics spread and in third world countries. Early diagnosis is crucial in preventing the severity of the disease and avoiding fatalities. To address this issue, a smart Android application has been developed that uses machine learning algorithms such as the decision tree to diagnose dengue patients. The decision tree algorithm was found to be the most accurate, with an accuracy rate of 93.7%, while other algorithms like close neighborhood had an accuracy rate of 74.29%, naïve bays had an accuracy rate of 93.07%, and SVM had an accuracy rate of 68.32%. The system's response is based on specific data that is entered and processed through the decision tree algorithm. Overall, the development of this smart system can greatly improve early diagnosis of dengue fever and potentially save lives. © 2023 IEEE.

关键词： Decision trees

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Home Automation Using Wi-Fi: ESP32-Based System for Remote Control and Environmental Monitoring 3

Home Automation Using Wi-Fi: ESP32-Based System for Remote C...

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

作者： Abdul Lateef Haroon, P.S. Shafiulla, Mohammed Naveed, Shaik Mohammed Ahmed, Suhail Nawaz, Shaik Mohammed Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Artificial Intelligence And Machine Learning Ballari India

ISBN: (纸本)9798350318609

This project introduces a comprehensive home automation system leveraging Wi-Fi connectivity and ESP32 micro controllers, aimed at enhancing modern living through seamless automation and user-centric design. The background emphasizes the fusion of IoT technology with home automation, highlighting Wi-Fi's significance in enabling remote monitoring and control of household devices. Methodologically, the project integrates relay modules and DHT11 sensors, orchestrated through a user-friendly smartphone application. Results demonstrate successful implementation and testing, showcasing functionality, reliability, and user-friendliness. Real-time environmental monitoring and remote appliance control fulfill the objectives of enhancing home comfort, convenience, and energy efficiency. The conclusion underscores the project's contribution to advancing smart home technologies, offering users a scalable and customizable solution. Future research opportunities include enhancing system capabilities and addressing security and privacy concerns. © 2024 IEEE.

关键词： Microcontrollers

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application of text mining technology in power data prediction 3

Application of text mining technology in power data predicti...

引用

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

作者： Guo, Jian Guo, Wu College of Architecture and Civil Engineering Qiqihar University Qiqihar China College of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9783800758760

This paper analyses the main research methods of power text mining technology in detail, and discusses the research hotspots of power text named entity recognition and named entity relationship extraction based on machine learning and deep learning. On this basis, the limited Boltzmann machine is further used to predict the power data. The test results show that the analysis of power text features in this paper is accurate, can effectively analyse the key text information, and the prediction results meet the requirements. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Character recognition

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Staying Ahead of the Curve: Early Prediction of Academic Probation among First-Year CS Students 3

Staying Ahead of the Curve: Early Prediction of Academic Pro...

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3rd international conference on Applied Artificial Intelligence (ICAPAI) - Human-Centric AI for the Digital Society

作者： Neda, Barbara Martinez Wang, Max Singh, Amanjeet Gago-Masague, Sergio Wong-Ma, Jennifer Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9798350328929

application processes in higher education, particularly in fields like computer Science (CS), have become more competitive in recent years. Additionally, at the University of California, Irvine (UCI), 57% of students who were admitted and enrolled in the CS program between Fall 2011 and Fall 2015 failed to maintain satisfactory academic standing and entered academic probation. Of these probation students, 41% entered probation during their first year. The objective of this study is to explore the feasibility of using machine learning tools to identify the academic probation status of first-year CS students at an early stage, with the ultimate aim of providing timely support to those who are at the greatest risk of entering academic probation. We trained various classifiers on a dataset containing demographic and academic performance variables for CS students at UCI, and performed feature analysis to identify the most influential variables. Since it is our priority to identify students who enter academic probation, our focus was on maximizing recall. The best-performing classifiers were Linear Support Vector machines (recall of 0.838) and Logistic Regression (recall of 0.812). Our feature analysis revealed that ethnicity, student region, and having taken the Advanced Placement CS exam had a greater impact on the classification task than GPA and summer course enrollment. Future work could focus on expanding the dataset to include academic data available after enrollment to improve our current results. Our findings could also be applied to assess their impact in strategies for student retention in CS. Overall, this study demonstrates the potential for machine learning tools to assist in identifying at-risk students in CS, and highlights the importance of considering a range of demographic and academic variables in this process.

关键词： academic performance prediction computer science education educational data mining academic probation academic probation detection machine learning first-year probation

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Proceedings - 2022 3rd international conference on computer Science and Management Technology, ICCSMT 2022

Proceedings - 2022 3rd International Conference on Computer ...

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3rd international conference on computer Science and Management Technology, ICCSMT 2022

ISBN: (纸本)9798350320404

The proceedings contain 111 papers. The topics discussed include: design of food safety management information system for western restaurant;research on enterprises’ response to emergencies based on machine learning;design and application of crop variety recommendation and production decision system in agricultural production;research on network structure analysis of English learning in social media;research on the prediction of e-commerce platform user churn based on random forest model;gazetteer enhanced named entity recognition for musical user-generated content;an application study of stochastic service systems based on operations search;risk early warning and response of PPP projects in characteristic towns based on fuzzy group decision- making and matter-element model;design and realization of an online examination system based on maven framework;design and implementation of dynamic interactive e-book system;optimization method for cloud service composition of machine tools under multiple constraints;application of environmental monitoring and control system in agricultural production;and design and implementation of warehouse management system based on Vuetify 3.0 framework.

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Intelligent Assembly Optimization Algorithm for Aircraft Panels Based on machine learning 3

Intelligent Assembly Optimization Algorithm for Aircraft Pan...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zhang, Wei Zhang, Yueqin Xuan, Yan Xu, Zilong Nanjing Vocational College of Information Technology Jiangsu Nanjing China

ISBN: (纸本)9798350317022

With the rapid development of computer technology, more and more people are starting to use computers in their daily lives and work. During aircraft flight, traditional aviation products require a lot of manpower and material resources to meet design requirements due to factors such as complex aircraft panel shapes, high precision requirements, and relatively dense structures. In order to save time and improve efficiency, this article utilizes research based on machine learning algorithms to optimize the layout of aircraft internal structures, including simplifying models, modifying geometric parameters, etc., in order to obtain more accurate analysis results. The test results show that the average accuracy of the system is around 0.95 plus or minus 0.02. By using the performance testing indicators and methods mentioned above, the performance of intelligent assembly optimization algorithms can be comprehensively evaluated for better application in practical problem solving. © 2023 IEEE.

关键词： Aircraft Panels Intelligent Assembly machine learning Optimization Algorithms

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Identifying Human Movement Patterns: Multivariate Gait Analysis Through machine learning 3rd

Identifying Human Movement Patterns: Multivariate Gait Analy...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Kumar, Raunak Mittal, Usha Chawla, Priyanka School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India National Institute of Technology Warangal India

ISBN: (纸本)9789819719426

The ability to identify walking conditions correctly is essential for both diagnosing and treating gait abnormalities. This study utilized machine learning algorithms to analyze multivariate gait data obtained from 10 healthy participants walking under three distinct settings, including normal treadmill walking while wearing an ankle brace on the right. In order to increase the precision and efficiency of the ML models, the authors adopted a pipeline technique to handle the data. This research unveils the preeminence of random forest, achieving an impressive (92%) accuracy, surpassing logistic regression, neural network, naive Bayes, and perceptron. It exemplifies the formidable potential of machine learning algorithms for gait classification. The application of the results may be restricted to the particular dataset and walking conditions employed in the study, and the proposed study’s limitations include the use of a constrained number of algorithms and hyperparameter tuning settings. The proposed study has implications for the design of diagnostic tools and assistive devices for people with gait abnormalities and emphasizes the value of paying close attention to hyperparameters and other important model parameters to achieve the highest level of accuracy and performance in machine learning models. Future studies might build on this strategy by utilizing more datasets, additional algorithms, and sophisticated optimization methods to boost the precision of gait categorization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Exercise equipment

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GraphEEG: Uniting CNN and GCN for Superior Personal Identification via EEG Analysis 3

GraphEEG: Uniting CNN and GCN for Superior Personal Identifi...

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3rd international conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Sun, Yifeng Hu, Zhuhua School of Information and Communication Engineering Hainan University Haikou China

ISBN: (纸本)9798350316674

Electroencephalography (EEG), are increasingly utilized for personal identification. This paper introduce a novel solution that fuse convolutional neural networks and Graph Convolutional Network, dubbed GraphEEG. GraphEEG utilize the advantage of CNN in automatic feature extraction and GCN for exploring the potential connectivity among EEG electrodes, achieves excellent performance in personal identification tasks. We assess the performance of the proposed GraphEEG on real-world datasets, including brain-computer interface tasks and compare it to other models. Extensive experiments on real-world datasets demonstrate that the proposed model outperform all baselines, based on popular evaluation metrics, the identification rates is up to ~99% via utilizing intra-session data in the event related potential task. © 2023 IEEE.

关键词： Brain-computer Interface Graph Neural Network machine learning Personal Identification

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Social Media Content Recommendation System using Knn Algorithm 3

Social Media Content Recommendation System using Knn Algorit...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Hariharan, R. Janit, R. Sophia Hindustan Institute of Technology and Science Department of Computer Application Chennai India Hindustan Institute Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350399264

Online streaming platforms for YouTuber short films are now widely used. Sites like YouTube Shorts provide an excellent platform for content creators to share their knowledge, ideas, and interesting information with their audience. There are no limits to creativity in the world of short videos. Billions of people create, edit, watch and share short videos on YouTube Shorts. Whether you want to share your voice with the world or create fun videos for your friends, all you need is your phone and your ideas. machine learning algorithms are used to analyze large amounts of user behavior data such as: B. Search, watch videos, like, comment, subscribe. By analyzing this data, recommendation systems can predict user preferences and recommend videos that users are likely to like. Companies are using social media to grow their business, and data mining and analytics are very important today. This article describes trend analysis for short videos on YouTube. Creators are encouraged to create compelling video content. These user characteristics such as views, comments, likes and dislikes are used for analytics. Analytics can use this algorithm as a supervised algorithm to classify other machine learning models and filter based on content. © 2023 IEEE.

关键词： Python

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