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4th International Conference on Machine Learning, Image Processing, Network Security and data Sciences (MIND)

作者： Wang, Hui Wang, Yanan Xiong, Shanmei Nanchang Inst Technol Nanchang 330018 Peoples R China

ISBN: (纸本)9783031243516;9783031243523

Machine learning uses learning theory and modeling computations to learn and predict big data analytics, and these types of computations enable predictions or database decisions by modeling input data samples. It has been successful in several application areas such as computer vision, natural language processing, and biometrics. This paper aims to study the data analysis and prediction of the electrochemical properties of carbon nanomaterials based on machine learning. Taking a typical carbon nanomaterial - graphene as an example, in the analysis and prediction algorithm, the preparation method of graphene and the commonly used electrochemical analysis. On the basis of the method, graphene was prepared by hydrothermal method and chemical reduction method, and the electrochemical properties of graphene under different preparation methods were discussed. The test results show that the capacitance and rate characteristics of the H-RGO electrode are better than those of the T-RGO electrode.

关键词： Machine Learning Carbon Nanomaterials Electrochemical Properties data analysis and prediction

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Financial data analysis and prediction Based on Generative Adversarial Fill-in Network Optimisation 24

Financial Data Analysis and Prediction Based on Generative A...

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3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Yang, Gang Zhejiang Inst Econ & Trade Teaching & Res Sect Financial Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798400709777

The analysis and prediction of financial data is crucial for investors and financial institutions, however, there are often a large number of missing values in financial data, which can seriously affect the integrity of the data and the accuracy of the analysis results. Therefore, the study proposes a financial data analysis and prediction method with improved generative adversarial filler network optimisation, which utilises the powerful generative capability of generative adversarial networks to fill in the financial data value that is missed, and combines it with traditional data analysis and prediction models to achieve more accurate and comprehensive analysis and prediction of the financial market. The experimental results show that the average absolute error of the proposed model under study is in the range of 0.1 to 0.11, and the performance of the model decreases less with the increase of the missing rate, and especially performs well at high missing rates. Regardless of the change in the number of attributes, the error value of the proposed model is still very small, which indicates that the model is also highly adaptable to handle data with different numbers of attributes. The proposed method can significantly improve the prediction accuracy and stability when dealing with financial data containing missing values. The study provides new ideas and technical support for the field of financial data analysis and prediction.

关键词： Generative adversarial filler networks Financial data Missing data filling data analysis and prediction

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data analysis and prediction by the Random Forest Algorithms——A Case Study of the Survival prediction of the Titanic on the Kaggle Platform

Data Analysis and Prediction by the Random Forest Algorithms...

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2019第三届教育、文化与社会发展国际学术会议

作者： Han Ma Zefeng Li School of Data Science City University of Macau

In the information age, data analysis and prediction have become more and more *** to extract value from data is a research *** paper elaborates on data analysis and prediction from the aspects of purpose, method, result, conclusion, and demonstrates the thoughts and practices of data analysis through the Titanic project of Kaggle platform.

关键词： data analysis and prediction data Visualization Feature Engineering Random Forests

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The Application of Computer Algorithms in Marketing data analysis and prediction 2

The Application of Computer Algorithms in Marketing Data Ana...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Jiao, Pengfei Guo, Chunxiang Shaanxi Fashion Engineering University School of Economics and Management Shaanxi Xi'an China

ISBN: (纸本)9798350317558

Many enterprises face data quality issues such as missing data, data errors, data inconsistencies, etc. Using data mining algorithms to analyze and predict market data has become an important way for enterprises to improve their competitiveness and market efficiency. This article adopts data mining algorithms, uses large-scale market data for analysis and modeling, and constructs a data set containing multiple characteristic variables by collecting and sorting historical market data. This article chooses data mining algorithms to train and model the data set, and optimizes the performance and accuracy of the model by repeatedly adjusting model parameters and evaluation indicators. Research results show that the algorithm's accuracy test data is between 85% and 94%. By using algorithms for data analysis, companies can more accurately predict market trends, customer behavior and sales performance. This helps companies develop more precise marketing strategies and resource allocation plans, and improve marketing effectiveness and return rates. © 2024 IEEE.

关键词： Computer Algorithm data analysis and prediction data Mining Marketing Strategy

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Predictive analysis of Solar Energy Production Using Neural Networks 8th

Predictive Analysis of Solar Energy Production Using Neural ...

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8th Future of Information and Communication Conference (FICC)

作者： Subburaj, Vinitha Hannah Gallegos, Nickolas Subburaj, Anitha Sarah Sopha, Alexis MacFie, Joshua West Texas A&M Univ Canyon TX 79015 USA GNIRE Lubbock TX 79416 USA

ISBN: (纸本)9783031280726;9783031280733

The energy industry is always looking at smart ways of conserving and managing the growing demand of consumable and renewable energy. One of the main challenges with modern electric grids systems is to develop specialized models to predict various factors, such as optimal power flow, load generation, fault detection, condition-based maintenance on assets, performance characteristics of assets, and anomalies. When it comes to energy prediction, the data that is associated with it is diverse. Accurate analysis and predictions of such data cannot be done manually and hence needs a powerful computing tool that is capable of reading such diverse data, converting them to a form that can be used for analysis and knowledge discovery. In this research effort, data collected from a local utility company over a period of three years from 2019 to 2021 were used to perform the analysis. Machine learning and neural network algorithms were used to build the predictive model. Different levels of data abstraction along with optimization of model parameters were done for better results. A comparative study was done at the end on the various algorithms used to determine the model performance.

关键词： Renewable energy Energy production data analysis and prediction Neural networks

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Predicting Mortality Risk among Elderly Inpatients with Pneumonia: A Machine Learning Approach 24

Predicting Mortality Risk among Elderly Inpatients with Pneu...

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24th International Conference on Enterprise Information Systems (ICEIS)

作者： Silva, Victor Monteiro De Souza Fernandes, Damires Yluska Da Cunha Rego, Alex Sandro Fed Inst Paraiba Joao Pessoa Paraiba Brazil

ISBN: (纸本)9789897585692

Community-acquired Pneumonia (CAP) is a serious respiratory infection that can cause life-threatening risk in people of different ages, especially in elderly inpatients. Regarding this age group, mortality rates by CAP still can reach 30% of all respiratory causes of death. In this work, we propose a machine learning approach to predict mortality risk among elderly inpatients with CAP. The approach uses real world data of elderly people with CAP from a hospital in Brazil, collected from 2018 to 2021. Based on patients data as learning features, our approach is able not only to classify patients at risk of mortality during hospitalization, but also to estimate the probability concerning the prediction. Some classification models have been examined and, among them, the best performance in terms of Area under ROC Curve (AUC) value has been achieved by the Logistic Regression (LR) classifier (AUC=0.81). Accomplished results show that the presented approach outperforms CURB-65 score as baseline in terms of both AUC values and probability of patient death. Besides, our approach is able to output probabilities ranging from 50 to 99% w.r.t. positive classification, i.e., patients that may come to death. A statistical test confirms that the presented approach outperforms the baseline provided by the CURB-65.

关键词： data analysis and prediction CAP Probability of Death ROC Curve AUC

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The Application of Intelligent database System in Material Support 24

The Application of Intelligent Database System in Material S...

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3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Zhai, Chenggong Xu, Hongsi Lv, Huifang Zhou, Liyong Wan, Fei Zhang, Pinghua Army Logist Acad Dept Quartermaster Procurement Chongqing Peoples R China Army Logist Acad Dept Logist Command Chongqing Peoples R China Army Logist Acad Mil Logist Dept Chongqing Peoples R China

ISBN: (纸本)9798400709777

Material support is a crucial part of the daily operations of many organizations and enterprises. With the continuous development of information technology, the application of intelligent database systems in the field of material support has become an important tool for improving efficiency, reducing costs, and providing better decision support. It can be used for material inventory management, real-time monitoring of inventory levels, and automatic triggering of ordering or replenishment processes. In addition, the intelligent database system can also predict future material needs, optimize the supply chain, assist in emergency response and material allocation, and conduct quality control and tracking. These characteristics can improve the efficiency, accuracy, and reliability of material management, ensure timely supply and quality safety of materials, and meet people's basic living needs and emergency rescue needs

关键词： Intelligent database Material support data analysis and prediction Supply chain optimization

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Modeling and Predictive analysis of Small Internal Leakage of Hydraulic Cylinder Based on Neural Network

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ENERGIES 2021年第9期14卷 2456页

作者： Guo, Yuan Xiong, Ge Zeng, Liangcai Li, Qingfeng Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic cylinder. In this paper, a model is proposed to simulate a small internal leakage of hydraulic cylinders, to convert the amount of leakage of hydraulic oil into strain signals through high-precision strain gauges and to train the collected strain signals using various neural networks to form a computational model and obtain prediction results from the model. The neural networks applied in this paper are convolutional neural networks, BP neural networks, T-S neural networks and Elman neural networks. The predicted results of the neural network are compared with the actual leakage amount. The results show that the prediction accuracy of the above four kinds of neural networks are all above 90%, of which the convolutional neural network is the most accurate. This research provides scientific and technical support for measuring and predicting small leaks.

关键词： small internal leakage of hydraulic cylinder neural network simulation experiment data analysis and prediction

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