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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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1st international conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023

1st International Conference on Optimization and Data Scienc...

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1st international conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023

ISBN: (纸本)9783031814549

the proceedings contain 35 papers. the special focus in this conference is on Optimization and Data Science in Industrial Engineering. the topics include: A Framework for Simulating the Optimal Allocation of Shared E-Scooters;Performance Enhancement of Two-Way DF Relayed Cooperative NOMA Vehicular Network with Outdated CSI and Imperfect SIC;integration of Artificial Intelligence for the Analysis and Monitoring of Projects Within Companies;multi-criteria Inventory Classification with machine learning Algorithms in the Manufacturing Industry;analyzing Results of Business Process Automation with machine learning Methods;predictive Analysis of Surface Defects in Engineering Structures Using machine learning Technologies;enhancing Autonomous Industrial Navigation: Deep Reinforcement learning for Obstacle Avoidance in Challenging Environments;Fast and Accurate Right-Hand Detection Based on YOLOv8 from the Egocentric Vision Dataset;Fine-Tuning of 3D Hand Pose Estimation on HOI4D Dataset by Convolutional Neural Networks;prediction of Ethereum Prices Based on Blockchain Information in an Industrial Finance System Using machine learning Techniques;email Classification of Text Data Using machine learning and Natural Language Processing Technique;the Fake News Detection Model Explanation and Infrastructure Aspects;An Advanced Approach to COVID-19 Detection Using Deep learning and X-ray Imaging;exploring Factors that Affect the User Intention to Take Covid Vaccine Dose;unsupervised Incremental-Decremental Attribute learning Healthcare application Based Feature Selection;on Solving the Physicians Scheduling Problem at an Emergency Department: A Case Study from Canada;Temporal Emotional and thematic Progression (TETP): A Novel Analysis of Mental Health Discussions on Social Platforms.

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1st international conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023

1st International Conference on Optimization and Data Scienc...

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1st international conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023

ISBN: (纸本)9783031814570

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Harvest Helper: AI-Driven Crop Selection Model

Harvest Helper: AI-Driven Crop Selection Model

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Himanshu S Bardhiya Mutyala Pavitra Ashutosh Bagde Vansh A Bardhiya Artificial Intelligence & Data Science Datta Meghe Institute of Higher Education and Research (DMIHER) Sawangi India Computer Science & Design Datta Meghe Institute of Higher Education and Research (DMIHER) Sawangi India Datta Meghe Institute of Higher Education and Research (DMIHER) Sawangi India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

To maximize agricultural output, this study presents Harvest Helper, an AI-driven crop selection algorithm that makes use of data on market trends, soil health, and environmental variables. Traditional crop selection techniques frequently fall short of keeping up with the complexity of contemporary agriculture as the world's food need grows. the Harvest Helper model uses cutting-edge machine learning algorithms to give farmers customized crop suggestions based on real-time data, increasing yield and resource efficiency. In an extensive analysis including 600 farms in various farming zones, the model showed a 20% average output gain and a 30% decrease in water and fertilizer use. Furthermore, an 85% satisfaction rating with the model's use and performance was reported by farmers in their comments. this study demonstrates how AI has the power to revolutionize farming methods and provide a long-term answer to future food production problems. Recommendations for future study to further enhance and increase the application of the model are presented, along with limitations pertaining data quality and technology accessibility. Accurately predicting crop yields is crucial to resource management and sustainable agriculture. this study examines the effectiveness of three machine learning classifiers on a dataset comprising soil and environmental parameters: Random Forest, Decision Tree, and Logistic Regression. Based on our findings, the Random Forest model performs noticeably better than the other classifiers, suggesting that it might be a reliable tool for crop prediction.

关键词： Analytical models machine learning algorithms Crops Soil Market research Agriculture Data models Resource management Random forests Farming

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4th international conference on Advanced Research in Technologies, Information, Innovation and Sustainability 2024, ARTIIS 2024

4th International Conference on Advanced Research in Technol...

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4th international conference on Advanced Research in Technologies, Information, Innovation and Sustainability 2024, ARTIIS 2024

ISBN: (纸本)9783031832062

the proceedings contain 91 papers. the special focus in this conference is on Advanced Research in Technologies, Information, Innovation and Sustainability. the topics include: A Mathematical Model for a Realistic Course Scheduling at Universities;Linking Clinical Information to ICF: Inter- and Intra-Rater Agreement When Using a Coding Platform;optimization of Bucking Rules in the Forestry Industry Considering Taper Functions;ready for Digitization: Decision-Making Model for Low Code Development Platform Selection;"Pre-diagnosis and Referral of Patients to a Specialist Medical Team through Facial Biomarker Detection";vertical Aeroponics as a Better Alternative for Sustainable Environment Control, a Simulation Approach;how to Reduce the Waiting List for Maxillofacial Surgery of third Molars;a New Efficient Genetic Algorithm for Solving the Kidney Exchange Problem;network Emulator Based on Openstack for the Implementation of a Virtual Data Network Laboratory in Higher Education Institutions;unveiling the Supply Chain Digital Transformation of Small and Medium Enterprises: A Managerial Insight on Success Factors and Challenges;GHG Web Portal Dashboard: A Scalable and Flexible Digital Platform for Efficient Environmental Monitoring;the Sensors and their Communication Among the Production Activity of Agroindustry Ecosystems;representation Dimension of Quantitative Reasoning and Cooperative learning;application of the Linear Model on the Optimal Fleet Calculation for Internal Transportation of a Peruvian University;transforming Graduate Data for Strategic Decision Making: A Business Intelligence Approach in Higher Education;Strategies Based on learning Styles for the Academic Improvement of the UISRAEL Students;gamified application for the Reinforcement of Mathematical Skills in Elementary Education;hydrogen as an Energy Vector: Analysis of the Global Development on Decision-Making Support Systems;safePlace: A Mobile application Prototype for Supporting People with Anxiet

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4th international conference on Advanced Research in Technologies, Information, Innovation and Sustainability 2024, ARTIIS 2024

4th International Conference on Advanced Research in Technol...

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4th international conference on Advanced Research in Technologies, Information, Innovation and Sustainability 2024, ARTIIS 2024

ISBN: (纸本)9783031840777

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Research on Relationship Between Aging Population Distribution and Real Estate Market Dynamics based on Neural Networks 25

Research on Relationship Between Aging Population Distributi...

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proceedings of the 2025 4th international conference on Big Data, Information and computer Network

作者： Yiqiu Tang Shenghan Zhao School of Professional Studies Columbia University New York New York State USA The Department of Economics Cornell University New York New York State USA

ISBN: (纸本)9798400712425

this study aims to analyze the relationship between demographic patterns and real estate market movements. In particular, the study aims to examine the relation with the help of a neural network-based model. Considering that the model in question is using a deep learning approach with, one long short-term memory network enhanced with several optimization techniques, adaptive learning rate methods and data augmentation techniques. Collectively, these strategies provide sufficient means to understand the potential impacts for the dynamics of the housing market, those that run between the population age structure, migration demographics and economic indicators. the model's timeliness is validated through its ability to match market trends tightly, while providing the potential to minimize the impact of an aging population on housing prices from structural angles. the empirical analysis showed that the proposed LSTM model provides more reliable predictions compared to conventional statistical methods. In addition, the model demonstrates excellent generalization for application robustness in different market conditions.

关键词： Deep learning

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4th international conference on Advanced Research in Technologies, Information, Innovation and Sustainability 2024, ARTIIS 2024

4th International Conference on Advanced Research in Technol...

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4th international conference on Advanced Research in Technologies, Information, Innovation and Sustainability 2024, ARTIIS 2024

ISBN: (纸本)9783031832093

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Research on the application of Deep learning in Electrocardiogram Monitoring and Early Warning Systems 25

Research on the Application of Deep Learning in Electrocardi...

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proceedings of the 4th international conference on computer, Artificial Intelligence and Control Engineering

作者： Yan Yan Yuhua Hao Jie Wang Jiwen Li Gubiao Fu Department of Health Services and Management Dalian Neusoft University of Information Dalian Liaoning China Engineering Department Yi'an Medical Technology (Haining) Co. Ltd Jiaxing Zhejiang China Department of Intelligent Medicine and Information Engineering Dalian Neusoft University of Information Dalian Liaoning China

ISBN: (纸本)9798400712647

Electrocardiography (ECG) is a well-known technology that is very important in diagnosing heart diseases. However, it takes a long time and doctors often make mistakes when they try to understand all the information from an ECG. this means that there is a need to find a better and quicker way to diagnose heart diseases. In this study, we have built an ECG monitoring and early warning system using the ESP32 hardware platform with deep learning algorithms. the front-end module is developed based on the WeChat applet platform, and the back-end uses the Flask framework. this system can acquire, process and detect ECG signals based on the LSTM model. the aim is to improve the monitoring capability and early warning efficiency of ECG through automation and intelligent means.

关键词： Deep learning

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BCI based EEG Signals classification using machine learning Approach

BCI based EEG Signals classification using Machine Learning ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Yogendra Narayan Daivik Gautam Dinesh Lakhwani Rajyavardhan Kakkar ECE Department Chandigarh University Gharuan Punjab India Department of Aerospace Engineering Chandigarh University Gharuan Mohali Punjab

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

this research investigates the effectiveness of classifiers in non-invasive electroencephalogram (EEG) Classification based brain computer interface (BCI) systems. Such systems are crucial for improving the quality of life for people with neuromuscular disorders. the study uses EEG data from 25 healthy individuals over three experimental sessions to evaluate the effectiveness of k-nearest neighbor (KNN) and Multi-layer Perceptron (MLP) classifiers, as well as support vector machine (SVM) and random forest (RF) classifiers. the data were processed using signal augmentation techniques, such as a 4th band-pass Butterworth filter. then, the data underwent evaluation through the application of 12-time domain feature extraction techniques. the results show that SVM performs better than RF with a 98.75% classification accuracy. It proves its superior ability to distinguish between left-and right-hand movement patterns. Similarly, MLP outperforms KNN with an accuracy of 95%, highlighting its potential for creating reliable online BCI applications. these findings pave the way for future developments in Brain computer Interfacing (BCI) technology, which aims to provide dependable and effective user interfaces for people with motor disabilities.

关键词： Support vector machines Band-pass filters Reactive power Accuracy Reviews Nearest neighbor methods Motors Electroencephalography Brain-computer interfaces Classification tree analysis

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Using Deep learning to Study Key Electrode Points in AIEEG 24

Using Deep Learning to Study Key Electrode Points in AIEEG

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proceedings of the 2024 4th international conference on Big Data, Artificial Intelligence and Risk Management

作者： Guoxia Zou Guangdong Open University (Guangdong Polytechnic Institute) Guangzhou Guangdong China

ISBN: (纸本)9798400709753

Over 1 billion people worldwide are living with the inconvenience caused by disabilities. With the rapid development of artificial intelligence technology, human-machine interface technology can lead to a qualitative breakthrough in helping people with mobility difficulties. Although the related research aims to understand the users’ action needs through an electroencephalogram (EEG), too many electrode points are currently used, limiting this technology's application. Indeed, finding the key electrode points will ease the transformation of EEG research into a real-world application. Studying the key electrode points in AIEEG (Action Idea Electroencephalogram) is significant in achieving this goal. Hence, this study used the LSR (least squares regression) algorithm to calculate the feature coefficients of 16-channel data and conducted grouping experiments based on the characteristics of the feature coefficients. then, used the Conv1d-GRU model to train and verify the collected data of the electrode points corresponding to each group of characteristic coefficients. the verification results demonstrate that four electrode points are required in the AIEEG to collect data and identify five action ideas. Relying on these four points provides a testing accuracy of 99%.

关键词： Action Idea Electroencephalogram

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