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检索条件"任意字段=3rd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024"

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2024 ieee 3rd international conference on electrical engineering, big data and algorithms, eebda 2024

2024 IEEE 3rd International Conference on Electrical Enginee...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

ISBN: (纸本)9798350380989

The proceedings contain 323 papers. The topics discussed include: design of atmospheric environment monitoring system based on Internet of Things;prediction of automatic parking accuracy based on PSO optimized BP neural network;predictive modeling in employee satisfaction: an approach with RA-SSA-SVM-based machine learning optimization;research on multibeam line planning based on affine transformation and Softmax improved K-means clustering analysis;a prediction model for electricity consumption in the Beijing-Tianjin-Tangshan region based on informer;flow field analysis of UAV power unit;surveillance information processing strategy of distribution network under power supply reliability;research on anti-telecom fraud based on artificial intelligence and edge computing;and research on the optimization of supermarket staff scheduling based on intelligent algorithm combined with binary coding.

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Integrating big data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between big data and conventional soil science practices.. © 2025 ieee.

关键词： data quality

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Exploration of Feature engineering Teaching Based on Max-Relevance Min-Redundancy 24

Exploration of Feature Engineering Teaching Based on Max-Rel...

引用

Proceedings of the 2024 3rd international conference on Artificial Intelligence and Education

作者： Zhengguang Chen Xiaohui Ma Shuo Liu Huiyan Feng Shuxin Yin Nan Xu College of Information and Electrical Engineering Heilongjiang Bayi Agricultural University Daqing Heilongjiang China

ISBN: (纸本)9798400712692

To meet the requirements of science and engineering personnel training in the new era, based on the teaching content of the feature engineering part of big data in the undergraduate teaching stage, combined with the process of machine learning practice, this paper discusses the course practice of the Max-Relevance Min-Redundancy algorithm in the task-driven Teaching mode. The study revolves around teaching content on feature engineering in big data during undergraduate education. Students practice algorithms in specific projects and accumulate practical experience in machine learning. They can better understand and apply the maximum correlation minimum redundancy algorithm to select feature variables, improve the performance of machine learning models, be familiar with the process of Feature engineering, and improve students' ability to solve practical problems and employment competitiveness. The results of teaching practice show that the teaching practice has achieved a good teaching effect in improving students' practical ability and understanding and mastering of feature engineering.

关键词： Machine learning

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big data and Machine Learning for Climate Change Prediction: An Integrated Approach to Environmental Monitoring

Big Data and Machine Learning for Climate Change Prediction:...

引用

3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Ramesh, Azmeera Gulmira, Zufarova Shnain, Ammar Hameed Ramya, R. Nagaveni, P. Aanandha Saravanan, K. CMR TECHNICAL CAMPUS Telangana India Andijan Machine-Building University Faculty of Economics 56 Boburshah Prospect Andijan Uzbekistan College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Psna College of Engineering and Technology Department of Information Technology India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

The integration of big data and machine learning (ML) in climate change prediction offers a transformative approach to environmental monitoring, enabling improved accuracy, scalability, and actionable insights. This study presents an integrated framework that combines the strengths of big data technologies and advanced ML algorithms to address the complexities of climate systems. Diverse datasets from satellites, IoT sensors, and historical records were preprocessed using normalization, feature engineering, and dimensionality reduction techniques to ensure data quality and compatibility. ML models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures, were developed to tackle spatial and temporal prediction tasks. The models demonstrated robust performance, with CNNs achieving 93.5% accuracy in deforestation detection and hybrid CNN-RNN models delivering 95% accuracy in cyclone trajectory prediction. Real-time monitoring applications, such as flood risk assessment and air quality forecasting, showcased low latency and scalability, reinforcing the framework's operational viability. Despite these advancements, challenges such as data bias, computational demands, and algorithmic transparency remain significant. To address these, solutions like dataset augmentation, edge computing, and explainable AI are proposed. This research highlights the potential of integrating big data and ML for dynamic and precise climate predictions while emphasizing the need for ethical and equitable deployment of these technologies. By fostering interdisciplinary collaboration and leveraging emerging computational innovations, this study contributes to advancing climate resilience and sustainable environmental management. © 2025 ieee.

关键词： Environmental monitoring

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Comprehensive optimization method for power quality based on deep reinforcement learning

Comprehensive optimization method for power quality based on...

引用

3rd international conference on Smart Energy and electrical engineering, SEEE 2024

作者： Zhang, Yan Xu, Shuangting Lin, Yan Lin, Qinan Zhang, Yi State Grid Fujian Electric Power Co. Ltd. Fujian Fuzhou350001 China State Grid Fujian Electric Power Research Institute Fujian Fuzhou350007 China School of Electrical Engineering and Automation Fuzhou University Fujian Fuzhou350116 China

With the ongoing advancement of carbon peaking and carbon neutrality goals, a large number of renewable energy generation resources are integrated into the grid, which will influence the power quality and jeopardize the stable operation of the power system. In response, this paper proposes a power quality comprehensive optimization method, which considers SVGs and PV grid-connected inverters (PVGCI) to optimize voltage deviation and harmonic distortion issues. First, based on practical engineering, a control framework is designed where the PVGCI only participates in voltage regulation, while the SVGs are responsible for both reactive power and harmonic compensation. Second, the mathematical optimization model is established, and the optimization problem is formulated as a Markov Decision Process (MDP), to be solved by the Deep Deterministic Policy Gradient (DDPG) algorithm. Finally, a simulation based on the ieee-33 node distribution network model under typical time profiles is performed to verify the optimization effect. The result shows that the proposed method can effectively control the devices' compensation output and achieve significant power quality comprehensive optimization. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Power quality

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Research on User Demand Mining and Repurchase Behavior Prediction Algorithm Selection Based on big data 3

Research on User Demand Mining and Repurchase Behavior Predi...

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3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Zhou, Tingwei Shanghai University Shanghai China

ISBN: (纸本)9798350380989

Based on the text data of authoritative skin care community, this study analyzes the user's needs and summarizes six topics on the premise that it has the option of repurchase intention. We need to calculate the emotional scores of six topics of each text data and contact the repurchase intention, thus constructing a feature data set, and then exploring which machine learning method is used to predict the repurchase behavior of online community review texts, which is relatively more accurate and reliable. © 2024 ieee.

关键词： big data machine learning sentiment analysis text mining topic clustering

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Research on Disease Prediction Based on Daily data Analysis of the Elderly Based on Neural Network algorithms 3

Research on Disease Prediction Based on Daily Data Analysis ...

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3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Lei, Liu Guangdong Open University Guangdong Polytechnic Institute Guangzhou China

ISBN: (纸本)9798350380989

In recent years, with the gradual arrival of an aging society, the health status of the elderly has attracted widespread attention. Against this background, this study aims to explore the potential value of these data for disease prediction by collecting and analyzing daily data of the elderly based on neural network algorithms. First, by using technical means such as smart sensing devices and mobile applications, we collected multi-dimensional data on the elderly's daily life, including but not limited to physiological indicators, activity levels, social interactions, and sleep patterns. These data are considered potential disease predictors because they provide comprehensive information about the physical condition and lifestyle of older adults. Second, we apply the collected data to a neural network algorithm, which has powerful learning and pattern recognition capabilities. By training models, neural networks can learn potential patterns and regularities from large amounts of complex data, thereby providing strong support for disease prediction. We pay special attention to the superiority of neural networks in processing multi-source heterogeneous data to more comprehensively understand the multi-faceted information of the elderly's health status. Through experiments and verification, we evaluate the performance of neural network algorithms in disease prediction in the elderly. This study explores disease prediction based on the daily data of the elderly based on neural network algorithms, aiming to provide scientific basis for establishing a more intelligent and accurate elderly health management system to promote the health and well-being of the elderly. © 2024 ieee.

关键词： data analysis elderly health neural networks

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Research on Intelligent algorithms Based on Computer big data Technology 3

Research on Intelligent Algorithms Based on Computer Big Dat...

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3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Li, Yuan Yu, Xin Modern Finance Industry School Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350380989

An intelligent search method based on PSO is proposed. By weighting the proportion of the difference between the current fitness and the largest fitness in the whole particle population, the position of the particle in the global optimal location is determined. This method can improve the existing search engine cannot accurately obtain the required information from the user, so as to greatly improve the search efficiency. This paper aligns data between the client database and the server database. The database of the client is constructed and the file tree of the client is created. It greatly reduces the interaction between client and server. The effectiveness of the proposed algorithm is verified by simulation experiments. It can work out the optimal scheduling scheme for multiple tasks in the cloud computing environment. © 2024 ieee.

关键词： big data Computer intelligent search system particle swarm optimization

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Research on big data Retrieval System of Intelligent Computer AI in Post-Moore Era 3

Research on Big Data Retrieval System of Intelligent Compute...

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3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Guo, Jun Cui, Yunkun College of Physical Education Taishan University Tai'an271000 China

ISBN: (纸本)9798350380989

This paper proposes a big data search platform in the post-Moore period. The algorithm can adapt to the storage and calculation of large-scale data, and can search video data quickly. The aim of this research is low accuracy and low recall rate of existing retrieval algorithms based on artificial neural networks. Cite Space technology is used to obtain the corresponding information through the statistical analysis of the keywords in the massive data. This project intends to MapReduce to analyze the correlation characteristics of massive data. The large-scale data analysis method of MapReduce is studied, so as to achieve the automatic query of massive data. And we pre-inspect it before entering the warehouse and use a variety of tools for data access and management. Simulation results show that the new method proposed in this project can effectively improve the computational performance of spatiotemporal big data, and improve its application in massive, multi-source, heterogeneous and other complex data. © 2024 ieee.

关键词： big data computer data mining data retrieval intelligence

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Application of Computer big data Technology in Marine Ecological Diversity Detection 3

Application of Computer Big Data Technology in Marine Ecolog...

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3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Yubai, Zhang Daohuan, Xu Xiangjun, Zhou Shandong Marine Forecast and Hazard Mitigation Service Qingdao China

ISBN: (纸本)9798350380989

In this paper, the Access2000 software is used for classification entry, statistical analysis, comprehensive assessment and external input of Marine ecological data. VB-ADO data access mode is used to manage the database. We call the corresponding data for processing to build a complete Marine fishery ecological environment information database. This database can realize the input of phytoplankton, zooplankton, fish eggs and larvae, residual toxins of organisms and so on. It has many services such as statistical query, comprehensive evaluation and output printing. The system can carry out the investigation and assessment of the fish resources in the sea area of China. This system has important theoretical and practical significance for the ecological environment assessment and management of Marine areas in China. © 2024 ieee.

关键词： database ecological environment monitoring ecological sounding Marine fishery

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