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Research on student achievement prediction based on machine learning 4

Research on student achievement prediction based on machine ...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Liang, Hong Yang, Shuwen Tao, Junyu Faculty of Information Engineering Guangxi Polytechnic of Construction Nanning530007 China School of Science Hubei University of Technology Wuhan430068 China School of Mathematical Sciences Suzhou University of Science and Technology Suzhou215000 China

ISBN: (纸本)9798400708831

Aiming at the problem of how high school teachers can effectively improve examinees' test-taking education ability and put forward relevant test-taking strategies, a mathematical achievement prediction model based on support vector machine and artificial neural network was proposed. A single support vector machine is prone to overfitting on a small sample data set, while artificial neural network can effectively learn the inherent law. therefore, combining the predicted values of the two and modifying them with residual neural network can effectively improve the prediction accuracy. In addition, in order to explore the influence of individual differences and overall differences on the predicted scores in the previous model exams, two prediction methods were designed. the experimental results show that the proposed model is superior to other methods and the overall difference is much greater than the individual difference. © 2023 ACM.

关键词： Support vector machines

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Analysis of Wordle Model Based on machine learning 4

Analysis of Wordle Model Based on Machine Learning

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Wu, Xinyuan Fan, Jiabo Huaqiao University College of Computer Science and Technology Xiamen China

ISBN: (纸本)9798350342710

this study addresses the WORDLE model problem by exploring how to construct appropriate short- and medium-term forecasting models. Firstly, by selecting data with recent stable trends as training and test data, the data are normalised and a combination of genetic algorithms, grid search and manual tuning is used to select suitable LSTM model parameters for forecasting. During the validation process, the R-square of the test set was approximately 67%. Based on the model predictions, the number of reports on 1 March 2023 is expected to be between 18,408 and 28,124. Secondly, four characteristic attributes were chosen as judgement indicators: number of letter repetitions, proximity of repeated letters, spelling rules and familiarity. It was not determined whether there were other features with high explanatory power that were not selected for this model, such as whether the player completed the task independently. Spearman correlation analysis was then conducted and found no correlation between these factors. In addition, principal component analysis, canonical correlation analysis and regression analysis were used as ideas in this paper. After extracting the selected word features, partial least squares regression was used to predict the percentage of occurrences of a particular word (1, 2, 3, 4, 5, 6, X) on a future day. After tenfold cross-validation, the model generalised very well. Finally, this model was used to predict the corresponding percentage of the word "eerie"on March 1, 2023, with a high degree of confidence in the predicted results. © 2023 IEEE.

关键词： Principal component analysis

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An Interpretable machine learning Model with Deep learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease 26th

An Interpretable Machine Learning Model with Deep Learning-B...

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26th international conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Kang, Wenjie Li, Bo Papma, Janne M. Jiskoot, Lize C. De Deyn, Peter Paul Biessels, Geert Jan Claassen, Jurgen A. H. R. Middelkoop, Huub A. M. van der Flier, Wiesje M. Ramakers, Inez H. G. B. Klein, Stefan Bron, Esther E. Erasmus MC Dept Radiol & Nucl Med Rotterdam Netherlands Erasmus MC Dept Neurol Rotterdam Netherlands Univ Med Ctr Groningen Dept Neurol Groningen Netherlands Univ Med Ctr Groningen Alzheimer Ctr Groningen Netherlands Univ Med Ctr Utrecht UMC Utrecht Brain Ctr Dept Neurol Utrecht Netherlands Radboud Univ Nijmegen Med Ctr Nijmegen Netherlands Leiden Univ Med Ctr Dept Neurol & Neuropsychol Leiden Netherlands Leiden Univ Inst Psychol Hlth Med & Neuropsychol Unit Leiden Netherlands Univ Amsterdam Med Ctr VUmc Amsterdam Netherlands Maastricht Univ Med Ctr Alzheimer Ctr Limburg Sch Mental Hlth & Neurosci MHeNS Maastricht Netherlands

ISBN: (纸本)9783031474002;9783031474019

machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. the proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. the proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.

关键词： Alzheimer's disease MRI Convolutional neural network Explainable boosting machine Interpretable AI

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An Advanced machine learning Based Wear Optimization of Nano Particles Reinforced with High Strength Metal Matrix Composite 4

An Advanced Machine Learning Based Wear Optimization of Nano...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Chinthamu, Narender Coumaressin, T. Lal, Bechoo Kakaravada, Ismail Khodade, Hrishikesh Haribhau Sundaramurthy, B. C to Candidate Enterprise Architect Wesco BostonMA United States Sri Manakula Vinayagar Engineering College Department of Mechanical Engineering Puducherry605107 India Kl Deemed University Department of Cse Andhra Pradesh Vaddeswaram India Vignan's Institute of Information Technology Department of Mechanical Engineering Visakhapatnam530049 India Rayat Shikshan Sanstha's S. M. Joshi College Department of Botany Hadapsar India Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation Deemed University Department of Computer Science and Engineering Tamilnadu Salem India

ISBN: (纸本)9798350300888

the use of Aluminium alloys in various industries has increased the demand for strong Aluminium alloys. To meet this demand, researchers are exploring new compositions for hybrid Aluminium metal matrix mixtures. In this study, an attempt was made to create a hybrid Aluminium alloy using stir-casting method and reinforcement with nanoparticles of boron nitride (BN) and nano zirconium dioxide (ZrO2) in 6070 Aluminium alloy. the Taguchi technique was used to enhance the stir-casting procedure limits that as agitation speed, agitation time, and temperature. the optimization was performed by varying the reinforcement content (0-12%) and the above-mentioned process parameters. the microhardness and wear resistance of the stir-cast materials were determined using a Vickers hardness tester and a wear tester. the results showed that the optimization of process parameters and reinforcement content had a significant impact on the microhardness and wear resistance of the Aluminium alloy. By analyzing the results, the optimum conditions for the stir-casting process were determined, leading to the production of Aluminium alloys with improved microhardness and reduced wear rate. In conclusion, this study demonstrated that the addition of BN and ZrO2 nanoparticles to 6070 Aluminium alloy using stir-casting method can enhance the microhardness and wear resistance of the alloy. the Taguchi method showed to be an effective tool in optimizing the stir-casting procedure limits, leading to the production of high-performance hybrid Aluminium alloys. machine learning technique is used to forecast the responses and the developed neural model predicts response at higher accuracy. © 2023 IEEE.

关键词： Aluminum alloys

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Research on the Optimization Model of Standard System Architecture Based on the FAST Method for Complex Business Systems 4

Research on the Optimization Model of Standard System Archit...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Wang, Yanfeng Zhu, Xianghua Li, Gang Feng, Zixia Yu, Haiyu Meng, Kai China National Institution of Standardization No.4 Zhichun Road Haidian District Beijing China State Grid Corporation of China No.86 West Chang 'an Avenue Xicheng District Beijing China China Electric Power Research Institute Co. Ltd No.33 NanBinghe Road XiCheng District Beijing China State Grid Smart Grid Research Institute Co. Ltd North District of Future Science and Technology City Changping District Beijing China

ISBN: (纸本)9798400709449

this paper analyzes the conceptual connotations of standard system and system, pointing out that standard system is characterized by system, thus a standard system architecture can be constructed by system theory. Hierarchical properties are typical features of system. In this paper, the FAST method is used to construct the standard architecture of complex business system layer by layer, and it is pointed out that the structural coordination of standard system and business system, the degree of support of standard system to the function of business system, and the coupling degree between standard sub-systems in the same level should be considered. Finally, this paper uses the FAST based on the complex business system standard body structure analysis method, taking the engineering construction standard system of State Grid Corporation of China (SGCC) as an example to carry on the case analysis. through case analysis, it can be seen that the optimized standard architecture is more balanced and covers more comprehensive business scope. © 2023 ACM.

关键词： computer architecture

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Research on Business Capability Evaluation Algorithm Based on Remote Sensing Satellite Ground Receiving System 4

Research on Business Capability Evaluation Algorithm Based o...

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4th international conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Gao, Zhenhua Li, Liguang Chen, Yanbin Jin, Cheng Wang, Huai Han, Zhuo Space Star Technology CO. LTD. Marketing Department Beijing China 93160th Troop of the Chinese People's Liberation Army Beijing China School of Cyberspace and Technology Beijing Institute of Technology China Space Star Technology CO. LTD. Ground Application System Department Beijing China School of Cyberspace and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331528850

With the rapid development of the aerospace industry, the number of remote sensing satellites launched is increasing, and the ground receiving system of remote sensing satellites is facing an urgent need for fast and stable reception of data transmitted by newly launched satellites. In this context, a method for evaluating the business capability of remote sensing satellite ground receiving systems is proposed, and an AHP based business capability evaluation index system is established. Based on the characteristics of the business, the single machine state evaluation model and system business capability evaluation model of the system were elaborated, and the influence weights of each level of factors were determined. the results of the case analysis show that this evaluation method can quickly determine the capability status of the remote sensing satellite ground receiving system for a certain remote sensing satellite data receiving business, provide guidance for system minimization and improvement, and provide feasible suggestions for the utilization and optimization of system resources. © 2024 IEEE.

关键词： Aerospace industry

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Simulation analysis of rice disease identification method based on improved ShuffleNetV2 4

Simulation analysis of rice disease identification method ba...

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Xiong, Huihuang Hu, Yongzhong Sichuan Chengdu611731 China

ISBN: (纸本)9781510671232

In the current deep learning rice disease recognition method, there are problems such as more complex models, poor adaptability to be deployed on edge devices and mobile terminals with limited computational resources, and poor real-time and accurate recognition of crop diseases, so we propose a lightweight rice disease recognition method that improves ShuffleNetV2. Based on the improved Shuffle Block unit called AugShuffle Block, the ECA (efficient channel attention) module is added, and average pooling is used instead of maximum pooling to obtain more feature information, at the same time, the number of Shuffle Block units used in each Stage module in the network structure is reduced to reduce the number of parameters in the model and the amount of computation. Experiments are conducted on the Kaggle rice disease dataset. the results show that the number of parameters of the model is 0.944 × 106 , the computation amount is 105.47 × 106 (FLOPs), and the average accuracy of disease recognition reaches 96.02%, provides a reference for the deployment of the rice disease recognition method for applications on resource-constrained devices such as mobile terminals. © 2023 SPIE.

关键词： Deep learning

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Identification of Diagnostic Biomarkers for Cardiovascular Diseases and Diabetes Mellitus through Raman Spectroscopy and machine learning Algorithms 16

Identification of Diagnostic Biomarkers for Cardiovascular D...

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16th international conference on Developments in eSystems Engineering, DeSE 2023

作者： Wilson, Megan Obe, Dhiya Al-Jumeily Abbas, Ismail Khan, Iftikhar Birkett, Jason Tang, Leung Assi, Sulaf Liverpool John Moores University Pharmacy and Biomolecular Sciences Liverpool United Kingdom Liverpool John Moores University Computer Science and Mathematics Liverpool United Kingdom Lebanese University Faculty of Science Beirut Lebanon Chemical Analysis Group Agilent Technology United Kingdom

ISBN: (纸本)9798350381344

the use of handheld Raman spectroscopy has increased in popularity based on its ability to provide portable and rapid analysis of endogenous compounds and diagnostic biomarkers in alternative biological matrices, such as fingernails. the application of fingernails as a diagnostic matrix allows for non-invasive, non-intrusive sampling, which can be carried out in the comfort of the patient's home. this study aimed to identify diagnostic biomarkers in fingernails related to cardiovascular diseases (CVDs) and diabetes mellitus (DM) using Raman spectroscopy and machine learning algorithms (MLAs). the findings showed that Raman spectroscopy successfully identified the presence of disease specific biomarkers in CVD and diabetic fingernails. Furthermore, when used in combination with MLAs, Raman spectroscopy was able to differentiate between healthy, CVD and diabetic fingernails. Further investigation will look at applying additional MLAs for determining the prognosis of disease. © 2023 IEEE.

关键词： machine learning

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application of human-computer interaction technology in muscle strength assessment for youth soccer players

Application of human-computer interaction technology in musc...

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2023 international conference on computer application and Information Security, ICCAIS 2023

作者： Zhu, Dong Zhou, Xin Lv, Yunwei Peng, Jin Hunan University of Medicine Hunan HuaiHua418000 China Sangao FC of High School Affiliated to Renmin University of China Beijing100080 China

ISBN: (纸本)9781510675032

the present study aimed to assess the dependability of MicroFET3 hand-held dynamometry (HHD) in young football players, thus establishing a relevant foundation for the utilization of HHD as a standardized tool for injury prevention and training monitoring in youth football players. A total of 37 young football players from Beijing Yi-Cheng BTV Football Club were selected as subjects. One muscle strength test for hip flexor, extensor, adductor, abductor, and knee flexor and extensor of subjects was conducted by Tester A and Tester B using HHD. Tester A repeated the test a day later. the intra-class correlation coefficient (ICC), the standard error of measurement (SEM) and smallest detectable difference (SDD) were computed. the results indicated that for the same tester, the test-retest reliability was excellent (ICC=0.84-0.96), with SEM ranging from 1.45-5.95 and SDD ranging from 4.01-16.49 (kg). For different testers, the inter-tester reliability was also excellent (ICC=0.85-0.97), with SEM ranging from 1.12-5.54 and SDD ranging from 3.09-15.34 (kg). this paper shows MicroFET3 HHD demonstrated excellent test-retest reliability and inter-test reliability in lower limb muscle strength tests of young football players. © 2024 SPIE.

关键词： machine learning

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Optimizing Solar-Energy Harvesting Communication Systems: An energy allocation scheme for maximizing data transmission 4

Optimizing Solar-Energy Harvesting Communication Systems: An...

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4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Li, Shuai Yao, Zhonghao Chen, Shengbo Schoool of Software Henan University Henan Kaifeng China School of Computer Science & Technology Beijing Institute of Technology Beijing China School of Computer and Information Engineering Henan University Henan Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Kaifeng China

ISBN: (纸本)9781510681767

the solar energy harvesting communication system is the basic support layer that is associated with the Internet of things (IoT). However, the inherent stochasticity of solar energy brings about extensive research on energy allocation in the system. In this paper, we propose an energy allocation algorithm for maximizing the data transfer (MDT) of a solar energy harvesting communication system while jointly considering the practical constraints of battery capacity and link capacity restrictions. We can solve it through the convex optimization problem, but the solution is extremely complicated. this complexity is the result of the coupling of energy allocation problems caused by restricted battery capacity and link capacity. To simplify the solution process, we propose a decoupled energy allocation (DEA) algorithm. Simulation experiments are conducted through real energy harvesting data sets, and the results show that DEA outperforms the other two heuristic schemes. © 2024 SPIE.

关键词： Convex optimization

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