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2023 International Conference on Electronics and Devices, Computational science, ICEDCS 2023

作者： Duan, Banxiang Hong, Wencheng Lin, Zexin Huang, Minxia Guangdong Institute of Science and Technology Computer Engineering Technical College Zhuhai China

ISBN: (纸本)9798350343038

This paper delves into the multifaceted aspects affecting residents' physical well-being. Leveraging a combination of advanced techniques, including the XGBoost algorithm, grid search optimization, and Latent Dirichlet Allocation (LDA), alongside proficient software tools such as Matlab, Excel, and PyCharm, we meticulously scrutinized and statistically evaluated various facets of residents' lifestyle habits. These encompassed dietary compositions, food preferences, and mealtime regularities. Our investigation culminated in the development of an XGBoost regression model tailored to predict residents' physical health factors. Subsequently, the model underwent refinement utilizing the SHAP model interpreter, grid search optimization, and LDA to pinpoint the optimal parameters for XGBoost. Armed with the optimized parameters, we harnessed the model's predictive capabilities to assess the propensity of residents to have previously encountered hypertension and diabetes. Furthermore, we visualized the findings, facilitating a more comprehensive comprehension of the outcomes. © 2023 IEEE.

关键词： MATLAB

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Research on the Construction and Application of Smart Classroom Teaching Service Platform Based on Big Data

Research on the Construction and Application of Smart Classr...

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2022 International Conference on computer engineering and Artificial Intelligence, ICCEAI 2022

作者： Xie, Jianhua Zhao, Xi He, Yongjun Chen, Zhe Computer Engineering Technical College Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

In order to solve the problems of the independence of online autonomous learning and live teaching, and the lack of data analysis on the website of excellent courses, this paper studies and designs an intelligent teaching service platform based on Django + Vue framework. The platform adopts B2B2C online education mode, and designs four functional systems based on PC and mobile terminals, including student terminal, lecturer terminal, enterprise terminal and platform terminal. The platform uses Python as the data processing language, through the user's behavior, using collaborative filtering recommendation algorithm modeling, to provide personalized recommendation for users. By using data visualization technology and data mining technology, the learning progress and knowledge mastery of students are visualized. The project can provide high-quality free online course resources for student users. Improve the learning effect of students, while bringing traffic to the one-to-one teaching business line of enterprises, help to increase the conversion rate of paying users, and is of great significance to the realization of the whole online education ecological closed-loop. © 2022 IEEE.

关键词： Students

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A Visualization Data Analysis of Second-hand Houses in Zhuhai Based on Python and ECharts and Bootstrap

A Visualization Data Analysis of Second-hand Houses in Zhuha...

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2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022

作者： Fan, Qiao Lu Duan, Xiang Ban Gao, Jie Guangdong Polytechnic of Science and Technology Computer Engineering and Technical College Guangzhou510640 China

ISBN: (纸本)9781450399548

In the era of huge data, because of the information explosion, it becomes extremely difficult for users to completely understand the source data information of second-hand houses. It is particularly necessary to use large data analysis technology and data visualization technology to meet the needs of buyers and renters. In view of the complicated information system of second-hand housing, buyers and renters need to obtain objective and comprehensive second-hand housing data in a short time, a visual analysis system for second-hand housing data is developed. Selenium+XPath crawler is used to climb the second-hand house data of Zhuhai in Shell second-hand housing network. Then, the data is stored into corresponding CSV files. The CSV files are converted into JSON files through the conversion website. Finally, the visual interface is built by Bootstrap and is displayed with the visual analysis chart of ECharts. Users can directly click the menu on the web-page, a detailed understanding of the second-hand housing source intuitive and appropriate visualization chart data, so as to better make the right decision to buy or rent. The system has a relatively powerful function, to meet the real needs of buyers and renters. © 2022 Owner/Author.

关键词： Python

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Efficient Compression Blind Artifact Removal Using Prompt-Enhanced Residual Networks

Efficient Compression Blind Artifact Removal Using Prompt-En...

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2025 2nd International Conference on Algorithms, Software engineering and Network Security (ASENS)

作者： Zhongming Yang Jianhua Hu Renzhi Wang Computer Engineering Technical College Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Image artifact removal is a crucial method for enhancing the quality of reconstructed images. By training models to accommodate varying Quantization Parameter (QP) qualities, this technique effectively reduces costs and facilitates practical applications, attracting significant scholarly interest. Current blind Compressed Image Restoration (CIR) studies typically rely on quality factor prediction networks to aid in restoring compressed images. However, these predicted quality factors lack spatial information, limiting the network's adaptability to image content. Recent research on prompt-based image restoration has demonstrated the strong generalization capability of prompts in addressing various types and degrees of degradation. For instance, the PromptIR model can handle multiple functions such as dehazing and deraining, providing inspiration for this study. Consequently, we developed a prompt-based blind image artifact removal model, Prompt-Enhanced Residual Networks (PERN), to improve the restoration of compressed images. To further enhance image quality, we incorporated a Residual Network branch. Experimental results show that our model achieves a 27.63% performance improvement compared to traditional JPEG images.

关键词： Q-factor Image quality Training Adaptation models Visualization Image coding Transform coding Image restoration Residual neural networks Software engineering

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Dynamic Programming to Solve the Optimal Production Model

Dynamic Programming to Solve the Optimal Production Model

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2023 International Conference on Applied Physics and Computing, ICAPC 2023

作者： Chen, Xiao Zhang, Pengbei Zhen, Zeming Liang, Wenqian Deng, Baijie Computer Engineering Technical College Guangdong Institute of Science and Technology Zhuhai China School of International Cooperation Guangdong Institute of Science and Technology Zhuhai China

This article mainly investigates the demand for small batch materials. It establishes a multi-objective dynamic programming model aimed at maximizing service level, minimizing inventory, and shortages by listing corre... 详细信息

ISBN: (纸本)9798350359244

关键词： Dynamic programming

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Teaching Reform and Practice of Task-Driven Approach Based on 'Modularization + Digitization' in Information technology Fundamentals Course

Teaching Reform and Practice of Task-Driven Approach Based o...

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作者： Guanghui Feng Xudong Fan Yuqing Gong Banxiang Duan Jianhua Jiang Meng Xia Computer Engineering Technical College Guangdong Polytechnic of Science and Technology School of Computer Science Zhuhai College of Science and Technology

With the continuous advancement of educational digitization, exploring blended learning and online teaching through the use of the Internet has become an inevitable trend in the reform of practice-oriented course instruction. Addressing issues such as the uneven computer application foundation of first-year students, limited class hours despite extensive course content, rapid development of information technology, and the limitations of traditional teaching methods and evaluation approaches. Following the educational principles of "student-centeredness, competency-based, and outcomeoriented," a questionnaire survey was conducted to understand students' learning foundations, assessments, and suggestions. Based on the fundamental, practical, and applied nature of the "Information technology Fundamentals" course, an exploration was made into task-driven teaching reform using a "modularized and digitized" approach. The paper introduces the implementation of teaching reform from the perspectives of instructional goals, execution process, and assessment, highlighting the effects of the reform through theoretical assessments, online project practices, and survey questionnaires. Finally, the experience gained and the existing shortcomings of the reform are summarized.

关键词： modularization digitization task-driven SPOC MOOC

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Research on Rule Engine Optimization Algorithm in Internet of Things Teaching Platform

Research on Rule Engine Optimization Algorithm in Internet o...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： Li, JianZhong Wan, Qiang Zhang, ZhiQiang School of Entrepreneurship Guangdong Innovative Technical College Guangdong DongGuan523000 China School of Computer Science and Engineering South China University of Technology Guangdong GuangZhou510000 China School of Intelligent Manufacture Shunde Polytechnic FoShan528300 China

ISBN: (纸本)9781510672444

The rule engine is an important part of the industry-education integrated Internet of Things teaching platform, and it is the basis for realizing the dynamic configuration of business rules in the practical teaching function. Combined with the data characteristics of the Internet of Things application scenario, this paper proposes a rule engine optimization algorithm based on Rete, and designs a pre-sorting algorithm based on rule frequency, which pre-sorts the order of nodes according to the frequency of use of rule patterns, with priority Match frequently used patterns, increases the sharing rate of nodes, and reduce the memory usage of the inference network. Through experimental simulation, the improved algorithm is verified, and the experimental results prove the effectiveness of the algorithm. © 2024 SPIE.

关键词： Internet of things

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Mathematical Model of Campus Water Supply Network Leakage Problem Based on BP Neural Network

Mathematical Model of Campus Water Supply Network Leakage Pr...

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2023 IEEE International Conference on Control, Electronics and computer technology, ICCECT 2023

作者： Deng, Baijie Duan, Banxiang School of International Cooperation Guangdong Institute of Science and Technology 519090 China Guangdong Institute of Science and Technology Zhuhai Computer Engineering Technical College Guangdong519090 China

ISBN: (纸本)9798350399028

This paper explores the problem of leakage in the campus water supply network. Using methods such as time series analysis, BP neural network, and semi-Markov processes, and software such as Matlab and Excel, the water consumption characteristics of a functional area in the campus are analyzed and statistically analyzed. The data relationship between water meter levels is established, and the leakage rate trend chart and the mathematical model of the water supply network leakage are constructed to analyze the leakage situation of the water supply network. The BP neural network deep learning method is used to predict the location of water supply network leakage, and a mathematical model of water supply network leakage is constructed to determine the location and intensity of leakage based on the model. The semi-Markov process is used to simulate the leakage situation of water pipes, and the optimal maintenance decision-making plan for the network leakage is determined by setting the objective function. © 2023 IEEE.

关键词： Decision making

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Global dissipativity for stochastic quaternion-valued neural networks with proportional delay 23

Global dissipativity for stochastic quaternion-valued neural...

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Proceedings of the 2023 7th International Conference on Electronic Information technology and computer engineering

作者： Shengjie Xu Nan Ding School of Tourism GuangDong Polytechnic of Science and Technology China Computer Engineering Technical College GuangDong Polytechnic of Science and Technology China

ISBN: (纸本)9798400708305

In the paper, the global dissipativity of stochastic quaternion-valued neural networks (SQVNNs) with proportional delay are investigated. Firstly, a novel lemma is established according to Ito's formula and stochastic analysis techniques. Secondly, by employing Lyapunov functional, inequality techniques and stochastic analysis theory, new sufficient contions to ensure the global dissipativity for SQVNNs with proportional delay are derived. Finally, two numerical examples are provided to illustrate the feasibility, effectiveness of our methods.

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Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

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computers, Materials & Continua 2022年第11期73卷 4393-4406页

作者： Vasumathi Devi Majety N.Sharmili Chinmaya Ranjan Pattanaik ELaxmi Lydia Subhi R.M.Zeebaree Sarmad Nozad Mahmood Ali S.Abosinnee Ahmed Alkhayyat Department of Computer Science Engineering Vignan’s Nirula Institute of Technology and Science for WomenGuntur522005India Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnam530048India Department of Computer Science and Engineering Ajay Binay Institute of Technology CuttackOdisha750314India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Computer Technical Engineering Department College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq College of Technical Engineering The Islamic UniversityNajafIraq

Histopathology is the investigation of tissues to identify the symptom of *** histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining *** investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s *** the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data *** this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)*** proposed EHCDLHIC technique initially performs Weiner filtering based noise removal *** the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are *** the classification process,the bidirectional gated recurrent unit(BGRU)model can be *** the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the *** validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is *** experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

关键词： Histopathological image classification machine learning deep learning handcrafted features bacterial foraging optimization

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