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检索条件"任意字段=2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024"

共 25993 条记录，以下是21-30 订阅

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Attribute Extraction of Geographic Entities Based on the LERT Pre-trained Model 24

Attribute Extraction of Geographic Entities Based on the LER...

引用

3rd international conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Li, Zeyu North China Institute of Computing Technology Beijing China

ISBN: (纸本)9798400707308

Geographic information systems(GIS) are now changing from two-dimensional expression to three-dimensional expression, and the geographic data gradually transforms into a more intuitive and more applied method of expression. In order to support the data expression in GIS, attributes extraction from unstructured Chinese geographic texts is needed. Manual extraction or pattern-matching methods have been insufficient to get geographic attributes, therefore deep learning methods for attribute extraction are required to meet the needs of the supplementary for entity properties. This paper took LERT for pre-training in the word-embedding layer and the BiLSTM module combined with attention mechanism for the feature extraction layer, finally obtained the attribute-value pairs as the output. The experimental results show that the accuracy, recall rate and F1 value of the proposed model on the test set improved compared with the existing model, proving the effectiveness of the proposed model. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Application and optimization of networked battery maintenance mode in power system

Application and optimization of networked battery maintenanc...

引用

2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Duan, Xuesong Zhang, Jinjia Li, Yuhao Li, Hongchao Wang, Wei State Grid Henan Electric Power Company State Grid Zhenping County Power Supply Company 474250 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Traditional battery maintenance methods have some problems, such as low efficiency, difficult to find potential problems in time and accurately, which affect the stability and reliability of power system. This paper studies the application and optimization of networked battery maintenance mode in power system. By integrating sensor technology, network communication and data analysis, this mode realizes real-time monitoring and maintenance automation of battery state. Aiming at the challenges of lack of real-time and accuracy, optimization strategies such as deploying 5G base stations, adopting advanced algorithms and machine learning technologies are proposed. In addition, a big data analysis platform based on cloud computing is built, and machine learning and data mining are used to predict battery performance decline, and intelligent maintenance algorithms are developed. Experiments show that this mode improves voltage stability, capacity retention and internal resistance control, reduces maintenance cost and improves efficiency. These achievements provide theoretical and practical guidance for the stable operation of power system and efficient maintenance of battery. © 2025 SPIE.

关键词： Batteries Data transmission Resistance Data analysis Data modeling machine learning Telecommunications Cloud computing Decision making Reliability

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Risk Resolution of Wisdom Education in the Era of Digital Intelligence 24

Risk Resolution of Wisdom Education in the Era of Digital In...

引用

7th international conference on Educational Technology Management, ICETM 2024

作者： Peng, Shaodong School of Education Science Hunan Normal University Hunan Changsha China

ISBN: (纸本)9798400717468

Wisdom education in the era of digital intelligence emphasizes the use of intelligent technology to empower education. It can integrate data wisdom, educational wisdom, and cultural wisdom to achieve the precision, creation, and optimization of education, and cultivate learners’ wisdom literacy. Due to the potential problems in algorithm, calculation data, and computing power and other aspects of problems in intelligent technology, as well as due to human-related factors such as emotional barriers and management’s losing control, wisdom education in the era of digital intelligence faces many risks. This paper applies the methods of philosophical speculation, ethical analysis, emotional analysis, and teaching pathology analysis to study the four major risks in them. (alienation of educational nature, human-machine ethical anomie, lack of artificial emotion, and pathological teaching), and intending to find the resolutions of the corresponding risks in wisdom education. The supposed resolutions are empowering the human-like nature of educational artificial intelligence and combining it with moral education to obtain a complete educaitonal nature of wisdom education;strengthening the governance of human-machine ethics from the source of risk (participants) through the utilization of intelligent educational ethical norms;combining human emotions with artificial emotions to establish a soul that is inclined to goodness of intelligent education;inhibiting pathogenic factors and enhancing disease resistance factors treat teaching diseases and prevent effectively through various therapies. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

19th International Conference on Soft Computing Models in In...

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

ISBN: (纸本)9783031750090

The proceedings contain 61 papers. The special focus in this conference is on Soft computing Models in Industrial and Environmental Applications. The topics include: Improving the Efficiency of Production Processes by Reducing Human Errors Using intelligent Methods;automated Semantic Labelling of Images Generated with Deep Diffusion Probabilistic Models;wastewater Quality Indicator Estimation Using machine learning and Data Augmentation Techniques;a Virtual Avatar for Sign Language Signing;comparison of D-Wave Quantum computing Environment Solvers for a Two-machine Jobs Scheduling Problem;hybrid Fuzzy-Based Method for Optimally Selecting Aircraft for Low-Cost Carriers Operating Medium-Haul Flights;two Way Ranging Positioning Methods Analysis Under Optimized Sensor Deployments;human Acoustic Events Detection as Anomalies in Industrial Environments Using Shallow Unsupervised Techniques;machine learning in the Last-Mile Delivery: Modified Q-learning for the TSP;IoT and AI Based Security System – Design and Implementation;characterization of Cattle Behavior Based on Dimensional Reduction Techniques;time Series Forecasting in Agriculture: Explainable Deep learning with Lagged Feature Selection;artificial Intelligence Methodology for Run-to-Target Manufacturing;multi-robot Path Planning Problem Applied to Real World Enviroment;an Approach of Optimization in Capacitated Vehicle Routing Problem in Logistics Distribution;bayesian Generation of Synthetic Data;learning-Based Models for intelligent Control Over Air Conditioning Units in a Smart Building;predicting Power Generation from Photovoltaic Energy System;photovoltaic Generation Forecasting for intelligent Management in Energy Communities;application of Neural Models in Solar Radiation Measurements for a Better Data Gathering Quality;caravels: A Decentralized Container-Based Infrastructure for Sustainable Human-Centric intelligent Energy Communities;Production Scheduling for Energy Cost Optimization with Constraints: Com

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Research on crime risk prediction model based on machine learning 24

Research on crime risk prediction model based on machine lea...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Hou, Xuehui Rexiti, Kudelaiti Aizezi, Yasen School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan China Faculty of Information Security and Engineering Xinjiang Police College Urumqi Xinjiang China

ISBN: (纸本)9798400717482

Crime prediction is an important method for public security departments to conduct crime early warning and investigation. According to the multidimensional characteristics of criminals, machine learning classification algorithm is used to establish a prediction model of whether a criminal commits a crime again, so as to quantitatively evaluate the possibility of the criminal commits a crime again, and provide a feasible scheme reference for preventing and reducing the recurrence of crime. Analysis of urban crime data in Boston, using Naive Bayes, Random Forest and XGboost algorithms to build a crime type prediction model, we can get the urban crime type tendency, help the public security department to predict the future crime, take effective measures to reduce the urban crime rate. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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Comparative Study on machine learning Algorithm Prediction of Soybean Protein Localization

Comparative Study on Machine Learning Algorithm Prediction o...

引用

2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Liu, Zhiqiang Tang, Yulan Chongqing Sinokonjac Biotechnology Co. Ltd. China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Soybean protein is one of the important components of modern human diet. How to accurately and quickly determine the location of soybean protein is the main topic of research scholars. In order to understand the soybean genome, improve human dietary health behavior, explore effective methods of soybean protein subcellular localization prediction with different deletion degrees, and improve the level of soybean protein subcellular localization prediction, this paper aims to understand the current situation of machine learning algorithms and applications and, according to the research and application characteristics of protein localization technology in the new era, This paper mainly discusses the application advantages of four kinds of machine learning algorithms in predicting soybean protein localization, so as to provide technical support for soybean protein data prediction analysis. © 2025 SPIE.

关键词： Proteins machine learning Evolutionary algorithms Detection and tracking algorithms Biological samples Decision trees Random forests Lithium Analytical research Data modeling

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Exploring intelligent Feedback Systems for Student Engagement in Digitized Physical Education learning through machine learning 24

Exploring Intelligent Feedback Systems for Student Engagemen...

引用

2024 international conference on Artificial Intelligence and Future Education, AIFE 2024

作者： Liu, Yanjun Lu, Ding Xu, Dan Zheng, Yiwen Zhou, Tong Li, Nianhong Feng, Ruisen College of Sports and Health Guangzhou University of Chinese Medicine Guangzhou Guangdong China College of Physical Education Yunnan University Kunming Yunnan China College of Humanities Beijing Sport University Beijing China Shenzhen Middle School Shenzhen Guangdong China International Affairs Division Shanghai Jiao Tong University Shanghai China Faculty of Education University of Macau China

ISBN: (纸本)9798400710650

Purposes: Through machine learning analysis of objective data from digital physical education learning (DPEL) platforms, explore the relationship and degree of correlation between student engagement and performance assessment, and predict final grades. Utilize grounded theory analysis to investigate the reasons, phenomena, and experiences of students participating in DPEL. The goal is to understand and clarify the factors influencing student engagement in DPEL, forming a theoretical framework and action guidelines. Develop an intelligent learning and feedback system for students participating in DPEL based on artificial intelligence. Utilize a comprehensive understanding of student engagement to create a more sophisticated and accurate intelligent learning and assessment ***: Utilizing a multifaceted approach, the research comprehensively investigated student engagement in DPEL. The quantitative aspect involved collecting and analyzing data from a digitized learning system. It utilized correlation analysis and machine learning algorithms to explore the relationship between participation, performance, and predict final grades. Qualitative insights were gathered through in-depth, semi-structured interviews, applying rigorous analysis methods to form a robust theoretical framework. Additionally, the study innovatively integrated machine learning and natural language processing techniques to develop an intelligent digital sports learning and feedback ***: The relationship between the variables of student engagement, specifically dwell time and visit frequency, is highly significant, showing a strong positive correlation. Dwell time emerges as a robust indicator among these variables, and there is a positive correlation among the three engagement variables. Each cluster created exhibits significant differences in characteristics, confirming that as learning engagement increases, assessment scores also rise. The predictive performance of the const

关键词： Adversarial machine learning

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The fusion application of intelligent perception and data analysis in product design innovation 2

The fusion application of intelligent perception and data an...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Wu, Liting Yu, Dali Art and Design College Guangzhou Vocational College of Technology & Business Guangdong Guangzhou511442 China

ISBN: (纸本)9781510689053

With the rapid development of intelligent perception technology and the continuous advancement of data analysis algorithms, the field of product design has ushered in a golden age of innovation. This study focuses on the integrated application of intelligent perception and data analysis in product design innovation, aiming to explore how to capture user behavior and environmental data through advanced perception technology and optimize the design process by combining data analysis algorithms. In the study, we used machine learning algorithms, and intense learning techniques, to analyze user interaction data and identify key improvement points in the design. Through experimental verification, we found that integrated applications can significantly enhance the user experience and market competitiveness of products. This research underscores the transformative potential of integrating intelligent perception and data analysis in product design, leading to substantial improvements in user engagement and market adaptability. © 2025 SPIE.

关键词： Adversarial machine learning

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intelligent scheduling and resource prediction framework for multidimensional data streams in cloud computing environments 2

Intelligent scheduling and resource prediction framework for...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Deng, Wei Qiu, Ziliang Liu, Weifeng Huang, Juzheng Ren, Binhua Qin, Siyuan Shenzhen Power Supply Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9781510689053

Cloud computing has become a foundational technology for handling the growing demands of data-intensive applications. Efficient resource allocation and scheduling are critical challenges, particularly when managing multidimensional data streams in dynamic environments. This paper introduces an intelligent framework for scheduling and resource prediction tailored to cloud computing systems. The proposed method integrates advanced machine learning techniques to predict resource demands accurately, enabling dynamic scheduling that optimizes resource utilization while reducing delays. The framework also includes an energy-aware optimization module that minimizes power consumption without compromising performance. The methodology leverages historical and real-time data to ensure adaptability and scalability across diverse workloads. Experimental results demonstrate the framework's high prediction accuracy, with minimal mean absolute error and root mean square error, its ability to maintain over 91% resource utilization under varying workload intensities. Moreover, the energy efficiency improvements of up to 15.4% highlight the system's sustainability. This study contributes a robust and scalable solution to modern cloud resource management, addressing both operational and sustainability goals. © 2025 SPIE.

关键词： Clouds Cloud computing Energy efficiency Data modeling Sustainability Modeling machine learning Mathematical optimization Performance modeling Evolutionary algorithms

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CIIS 2024 - 2024 the 7th international conference on Computational Intelligence and intelligent Systems

CIIS 2024 - 2024 the 7th International Conference on Computa...

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7th international conference on Computational Intelligence and intelligent Systems, CIIS 2024

ISBN: (纸本)9798400717437

The proceedings contain 24 papers. The topics discussed include: are large language models good at fuzzy reasoning?;machine learning-based predictive model for fabric wastage in the apparel industry due to defective fabric raw materials;optimizing neural network training efficiency through spectral parameter-based multiple adaptive learning rates;machine learning-based patent transferability forecasting for emerging technology: a case study of graphene-based supercapacitors;automated pest control: computer vision for wildlife surveillance;anomaly detection in retinal imagery using variational autoencoders;evaluating the suitability of inception score and frechet inception distance as metrics for quality and diversity in image generation;and continuous estimation of distribution algorithms for the parametric optimization of geothermal power plants.

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