检索结果-内蒙古大学图书馆

machine learning algorithm based on optical sensors for simulating sports tourism environment

Optical and Quantum Electronics 2024年第4期56卷 680-680页

作者： Tang, Huaming Quan, Na Guilin Tourism University Guilin 541006 China

Aiming at sports tourism environment simulation, this paper aims to explore the application of machine learning algorithm based on light sensor in this field. We propose a new method for sports tourism simulation, which is based on optical sensor data acquisition and processing. The aim of this research is to develop an accurate and efficient algorithm to simulate the optical properties of sports event venues in order to provide a more realistic sports tourism experience. The research method consists of two main steps: optical sensor data collection and machine learning algorithm modeling and training. First, a network of optical sensors was set up to collect light intensity and color information at different locations in the stadium. This data is then used to train a machine learning algorithm to predict and simulate the sports tourism environment under different optical scenarios. Through experiments and evaluations, the proposed algorithm performs well in terms of accuracy and efficiency, and can simulate the lighting characteristics of sports venues with high accuracy. This provides a strong technical support for sports tourism simulation, which can provide users with a more realistic experience. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Environmental simulation Light sensor machine learning algorithm Sports tourism

来源：评论

学校读者我要写书评

暂无评论

Prediction Accuracy Improvement for Cardiovascular Diseases Using machine learning algorithm 10

Prediction Accuracy Improvement for Cardiovascular Diseases ...

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Debnarth, Sanjoy Kumar Malik, Swati Kaur, Gaganpreet Bagchi, Susama Soomro, Abdul Majid Naeem, Awad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India National College of Business Administration & Economics Department of Computer Science Multan Pakistan

ISBN: (纸本)9798350382471

Worldwide Cardiovascular diseases (CVDs) are one of the most common causes of death. Multiple ML algorithms are employed in this work to address the CVD problem. The technique used in this study employs several machine-learning algorithms to predict CVD. To predict cardiac sickness, data mining approaches such as Decision Trees (DT), Naive Bayes (NB), Support Vector machine (SVM), Logistic Regression (LR), Random Forest (RF), Ada Boosting, Gradient Boosting, and Bagging algorithms have been utilized. Using Base learners and Ensemble classifiers, this study tried to predict the presence of heart disease. Bagging and Gradient Boosting Classifiers had the highest accuracy, with 81.3% and 81.3%, respectively. © 2023 IEEE.

关键词： Bagging Boosting Cardiovascular diseases Classifiers machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Fake News Detection Using machine learning algorithm 2

Fake News Detection Using Machine Learning Algorithm

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Narkhede, Apurva Patharkar, Devyani Chavan, Nekita Agrawal, Rahul Dhule, Chetan Iot Cyber Security G H Raisoni College of Engineering Department of Data Science Nagpur India

ISBN: (纸本)9798350369960

Fake news is a big concern since it spreads widely over social media and other media channels, creating significant social and national damage with devastating consequences. In order to address this issue, substantial research how to be recognized on fake news detection has been done. The purpose of this study is to analyze the existing research on fake news detection and select the best conventional machine learning frameworks to develop an algorithm for supervised machine learning. The algorithm will be able to classify false reports as true or fraudulent using textual analysis methods such as NLP. The proposed process involves data preprocessing and vectorization, where the NLP library will be used to perform tokenization and feature extraction of text data, utilizing tools such as Count Vectorizer and Tiff Vectorizer. Further, feature selection methods will be employed to evaluate and determine the most fitting features to achieve the highest precision based on confusion matrix results. The outcome of the decision tree classifier algorithm gives the accuracy of 90%. © 2023 IEEE.

关键词： data preprocessing Fake news feature extraction machine learning algorithm vectorization

来源：评论

学校读者我要写书评

暂无评论

AN INNOVATION DEVELOPMENT OF BANDWIDTH MANAGEMENT IN 4G WIRELESS LOCAL AREA NETWORK (4G-WLAN) USING SMART machine learning algorithm

AN INNOVATION DEVELOPMENT OF BANDWIDTH MANAGEMENT IN 4G WIRE...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Gopinathan, B. Mukherjee, Papiya Naveen Kumar, N. Nandhini, R. Shanmugapriya, S. Lalitha, S.D. Department of Computer Science and Engineering Adhiyamaan College of Engineering Tamilnadu Hosur India Department of CSE Education Koneru Lakshmaiah Foundation Green Fields Andhra Pradesh Guntur India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of ECE Veltech Multitech Dr Rangarajan Dr Shakuntala Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Virudhunagar India Department of CSE R.M.K. Engineering College RSM Nagar Tamilnadu Kavaraipettai India

ISBN: (纸本)9798350305708

Currently, the vast use of existing fourth generation networks is further increasing its users. A major problem with this process is the allocation of equal bandwidth to everyone on the network. This varies depending on the secondary users who are aging. But the time difference between them allows both of them to be switched to get the required bandwidth utilization. An advanced machine learning algorithm is proposed in this paper. It is designed to reduce the hassle for the primary user by calculating the login times of the primary user and the secondary user. Also the switching schedule data for the secondary user is given to improve and fix them so that they do not have immediate connection problems. And the most important thing in this system is time management for primary and secondary users. Two are even better designed to manage this effectively. © 2023 IEEE.

关键词： bandwidth utilization machine learning algorithm primary users secondary users

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning algorithm in Financial Industry 3

Application of Machine Learning Algorithm in Financial Indus...

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Chen, Jing Xi'An Mingde Institute of Technology Shaanxi Province Xi'an China

ISBN: (纸本)9798350317022

In the financial sector, data are collected from many sources, such as central banks, stock exchanges and other organizations. Then use various algorithms to process these data and predict the future results of the asset. These data can be used to predict the price trend of short-term and long-term securities or commodities. This is achieved by applying machine learning algorithms to analyze history and current market trends. The quantitative analysis method for financial data is a method that uses computers to analyze financial data, so as to predict the fluctuation direction of the securities market to obtain excess returns. machine learning has become an important means in quantitative analysis methods, and has shown better performance than traditional quantitative analysis methods. The quantitative analysis method based on machine learning is used to analyze and invest financial data, which has the advantages of objectivity, accuracy and other advantages that traditional investment methods do not have, so it is used by investors and financial institutions. © 2023 IEEE.

关键词： financial industry machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning algorithm for the estimation of time-dependent strength of basic oxygen furnace slag-treated soil

引用

DEVELOPMENTS IN THE BUILT ENVIRONMENT 2024年 17卷

作者： Kang, Gyeong-o Seo, Jaehyun Chang, Seongkyu Gwangju Univ Dept Civil Engn 277 Hyodeck Ro Gwangju 61743 South Korea Gwangju Univ Dept Comp Engn 277 Hyodeck Ro Gwangju 61743 South Korea

The main purpose of this study is to predict the time-dependent strength of BOF slag-treated dredged soil using four machine learning (ML) algorithms (random forests, multi-layer perceptron, support vector regression, knearest neighbors). These models were trained using a dataset developed from the published literature. The slag type, slag content, water content, and curing time were used as input values. Here, the curing time was divided into three stages according to the magnitude of strength development. Among the algorithms, the multi-layer perceptron (MLP) was selected as the optimal model, and its predicted strength was compared with that of BOF slag-treated soil calculated by the previous empirical equation. In addition, MLP accurately predicted the strength of BOF slag-treated soil compared with that of the empirical equation. Consequentially, ML algorithms had higher applicability for estimating of the time-dependent strength of BOF slag-treated soils.

关键词： BOF slag Dredged clay Time -dependent strength Prediction machine learning algorithm Empirical equation

来源：评论

学校读者我要写书评

暂无评论

Combustion of Raw Biogas/Hot Air in a Porous Radiant Burner and Performance Prediction Using a machine learning algorithm

引用

JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS 2024年第6期16卷 064502-064502页

作者： Dash, Kishan Acharya, Saroj Kumar Samantaray, Sikata SOA Deemed Univ Dept Mech Engn ITER Bhubaneswar 751030 India

Combustion of raw biogas/hot air was performed in a porous radiant burner associated with a solar heater, and performance was predicted by a linear regression model using a machine learning algorithm. The test was conducted for the combustion of three different compositions of raw biogas mixtures having CO2 percentages of 25%, 30%, and 35% at the thermal load of 200-400 kW/m(2). The hot air was supplied at an average temperature of 50 degrees C from the solar heater air supply system for proper combustion in lean mixture conditions. The porous radiant burner associated with a solar heater has offered radiation efficiency of 15.34-47.93%, NOX of 1-3.1 ppm, and CO of 25-87 ppm for three different compositions of raw biogas mixtures at the thermal load of 200-400 kW/m(2) and equivalence ratio of 0.70-0.91. The increased radiation efficiency has indicated that the porous radiant burner can be an alternative for low-calorie fuel like raw biogas. Data analysis and processing have been performed using the machine learning algorithm, and the linear regression model has been developed using the python programming language. The error between predicted and experimentally calculated radiation efficiency is 1.67%.

关键词： porous radiant burner solar heater raw biogas combustion machine learning algorithm linear regression model

来源：评论

学校读者我要写书评

暂无评论

MEMS fault-tolerant machine learning algorithm assisted attitude estimation for fixed-wing UAVs

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Pasha, Amjad Ali Sankaralingam, L. Rahman, Mustafa Mutiur Alam, Mohammad Irfan Juhany, Khalid Ahmad King Abdulaziz Univ Aerosp Engn Dept Jeddah 21589 Saudi Arabia Hindustan Inst Technol & Sci Sch Aeronaut Sci Chennai 603103 India Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada Konkuk Univ Konkuk Aerosp Design Airworthiness Res Inst KADA Seoul South Korea

The utilization of Micro-electromechanical Systems (MEMS) sensors is widespread for directly detecting attitude angles, such as Accelerometer, Gyro, and Magnetometer readings. However, these MEMS sensors are prone to flaws, leading to inaccurate estimates of attitude angles and, consequently, causing UAVs to lose control. Given that UAVs are operated remotely over long distances, ensuring accurate attitude estimates becomes crucial. This study aims to address this issue by employing machine learning algorithms (MLA). These algorithms were trained and evaluated to overcome the problem by predicting missing data from a malfunctioning MEMS sensor using the available data from other MEMS sensors. To calculate the attitude angles, the study utilizes the Extended Kalman Filter (EKF) technique. Furthermore, a novel fault-tolerant machine learning-aided estimation algorithm has been proposed specifically for estimating the attitude angles (phi, theta, psi) of fixed-wing UAVs. The significance of this research becomes even more prominent when considering the occurrence of MEMS sensor failure. In such cases, the machine learning algorithm plays a crucial role as it has been pre-trained specifically to handle these scenarios. The algorithm is equipped with the ability to effectively address and mitigate the challenges posed by MEMS sensor failures. By leveraging its pre-existing knowledge and learned patterns, the algorithm can accu-rately predict missing data caused by malfunctioning MEMS sensors. This capability proves invaluable in ensuring the reliable estimation of attitude angles, even in the face of sensor failures. Thus, the integration of machine learning into the estimation process enhances the resilience and robustness of the system, making it well-equipped to handle the uncertainties introduced by MEMS sensor failures.

关键词： Fault-tolerant machine learning algorithm Attitude angles estimation Kalman filter

来源：评论

学校读者我要写书评

暂无评论

A machine learning algorithm-enhanced mathematical simulation based on Carrera unified formulation to estimate fundamental frequency of the FG origami-enabled metamaterial composite system surrounded by auxetic concrete foundation

引用

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年

作者： Wei, Qiujie Li, Jialing Cheng, Xi Rehman, Ateekh Ur Chengdu Polytech Chengdu Peoples R China Chongqing Youth Vocat & Tech Coll Sch Artificial Intellegence Chongqing Peoples R China Zhuzhijian Technol Chongqing Co Ltd Chongqing Peoples R China King Saud Univ Coll Engn Dept Ind Engn Riyadh Saudi Arabia

This study investigates the nonlinear dynamic behavior and natural frequencies of skew plates integrated with FG (Functionally Graded) origami-enabled metamaterials and supported by auxetic concrete foundations. The analysis is performed using the Carrera unified formulation (CUF), which provides a robust and accurate method for assessing the flexural response of complex plate systems. The formulation accounts for geometric nonlinearity, material gradation, and skew angles, capturing both the nonlinear natural frequencies and dynamic deflections. The inclusion of auxetic concrete foundations enhances the structural performance due to their negative Poisson's ratio, which offers improved energy absorption and deformation characteristics. To further validate the accuracy and reliability of the proposed method, the results are verified through comparison with existing literature and are supplemented by a novel fuzzy decision tree algorithm as a machine learning tool. The fuzzy algorithm aids in automating the verification process and identifying patterns in the dynamic response, ensuring computational efficiency and robustness. Results demonstrate the significant influence of skew angles, and metamaterial properties on the dynamic behavior of skew plates. The study highlights the advantages of FG origami-enabled metamaterials in enhancing structural stiffness and reducing dynamic deflections, making them suitable for advanced engineering applications. This research bridges the gap between computational mechanics and machine learning, providing a comprehensive approach to analyzing and verifying the nonlinear dynamic performance of skew plates with auxetic foundation support.

关键词： Carrera unified formulation skew plate FG origami-enabled metamaterials auxetic concrete foundation nonlinear natural frequency and dynamic deflection machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Performance optimization of cement calciner based on CFD simulation and machine learning algorithm

引用

ENERGY 2024年 302卷

作者： Cui, Ying Ye, Lin Yao, Zhongran Gu, Xiaoyong Wang, Xinwang Wuxi Inst Technol Sch Automot & Transportat Wuxi 214000 Jiangsu Peoples R China Southeast Univ Sch Energy & Environm Key Lab energy Convers & Proc Measurement & Contro Minist Educ Nanjing 210096 Jiangsu Peoples R China Monash Univ Dept Chem Engn ARC Res Hub Computat Particle Technol Clayton Vic 3800 Australia Wuxi Inst Technol Sch Control Wuxi 214000 Jiangsu Peoples R China Gaolang West Rd 1600 Wuxi 214000 Jiangsu Peoples R China

In order to improve the combustion efficiency and decomposition rate of the cement calciner and reduce pollutant emission, a performance optimization method based on Computational Fluid Dynamics (CFD) numerical simulation integrated with machine learning is proposed. The Multiphase Particle-in-cell (MP-PIC) method and the chemical reaction models are employed to simulate the coal combustion and CaCO3 decomposition process, whose calculation results are combined with the industrial practical data of the cement plant, so as to establish a more comprehensive training database. On this basis, a novel Topology Particle Swarm Optimization algorithm integrating with Convolutional Neural Network and Long Short-Term Memory (RITPSO-CNN-LSTM) algorithm model is established to predict combustion efficiency, decomposition rate, and NOx emission, respectively. Results show that compared with two other relative basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.045 %, 0.038 %, and 0.021 %, respectively. The addition of CFD simulation data makes the prediction model more applicable with higher stability and accuracy. Based on the prediction results, Grey Wolf Optimizer (GWO) algorithm is employed to optimize operating parameters, and finally the average optimization amount of combustion efficiency, decomposition rate, and NOx emission are 2.17 %, 2.24 %, and 6.15 ppm, respectively, which meet the optimization requirements.

关键词： Cement calciner Combustion characteristics Calcium carbonate decomposition MP-PIC simulation machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：