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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是461-470 订阅

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Design and Simulation of international Propagation Path of Chaozhou Opera Based on Improved Genetic Algorithm 24

Design and Simulation of International Propagation Path of C...

引用

4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Lin, Jiling School of Foreign Languages Guangdong University of Science& Technology Dongguan Guangdong China

ISBN: (纸本)9798400710247

this study focuses on the design and simulation of an international transmission path for Chaozhou Opera, a traditional Chinese performing art form. the goal is to optimize the transmission path using an improved genetic algorithm to ensure efficient and reliable communication between different regions. the proposed algorithm takes into account factors such as transmission distance, signal strength, and network congestion to determine the most optimal path for transmitting Chaozhou Opera performances internationally. the simulation results demonstrate that the improved genetic algorithm outperforms traditional routing algorithms in terms of both efficiency and reliability. the proposed transmission path not only minimizes transmission delay and packet loss but also maximizes network throughput and utilization. Overall, this study provides valuable insights into the design and optimization of international transmission paths for cultural communication, with potential applications in other traditional art forms and cultural exchanges. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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Prediction of the Groundwater Levels Based on Random Forest Regression Algorithm 4

Prediction of the Groundwater Levels Based on Random Forest ...

引用

4th IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Wang, Pengbo Shanghai Zhongqiao Vocational and Technical University Shanghai201514 China

ISBN: (纸本)9798350363586

Random forest regression is a widely used machine learning algorithm. In this study, random forest regression is employed to predict groundwater levels. Five influencing factors are considered: river flow, temperature, saturation deficit, precipitation, and evaporation capacity. Each factor is regarded as a feature. Twenty-four sets of data were collected: eighteen sets for training and six for testing. the groundwater levels predicted using the proposed approach agree well with the corresponding true values. the relative errors are less than 5%, which satisfies the requirements of engineering precision. A feature importance analysis is also conducted. the precipitation has the most significant influence on the groundwater levels, with a degree of more than 50%, and the saturation deficit has the least influence, less than 10%. the results of this study not only provide an effective approach for groundwater level prediction but also demonstrate the encouraging prospects of machine learning algorithms in hydrological engineering applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering Healthcare Systems Using Machine learning: Working, Classification and Application 4

Empowering Healthcare Systems Using Machine Learning: Workin...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： thakur, Keshav Raj Bhushan, Bharat Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

Empowering Healthcare Systems with Machine learning: Mechanisms, Classifications, and applications' examines how machine learning (ML) and healthcare are dynamically combining. It explains how ML algorithms manage healthcare data in detail, classifies ML's many healthcare applications, and emphasizes the significant impact of these applications. ML transforms healthcare systems, improving everything from the accuracy of disease diagnosis to the creation of individualized treatment approaches. To ensure appropriate integration, ethical and legal aspects are emphasized. In addition to redefining patient care, this ML and healthcare synergy improves decision-making and improves patient outcomes. the paper's main contribution is its discussion of an overview of machine learning (ML), which includes its needs, features, many applications and challenges and difficulty of (ML) in health sector. this paper highlights how (ML) have the ability to improve healthcare efficiency in general and the diagnosis and personalization of patient care. But it also acknowledges problems like data accessibility, data quality, high dimensionality, efficiency, and privacy issues that demand careful thought. © 2023 IEEE.

关键词： Machine learning

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Machine learning-based Employee Turnover Prediction 4

Machine Learning-based Employee Turnover Prediction

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Deng, Zhenchang Wang, Hao School of Management Wuhan University of Technology Wuhan China School of Entrepreneurship Wuhan City Polytechnic Wuhan China

ISBN: (纸本)9798331528676

Given the current challenging labor market, many organizations are grappling with significant issues related to employee turnover. this turnover can lead to considerable economic detriments for businesses, rendering the reduction of turnover rates a critical priority. this research leverages an open-source dataset on employee turnover, executing comprehensive data preprocessing and feature engineering. It conducts descriptive statistical analysis and correlation assessments and explores the application of various clustering algorithms such as K-means and DBSCAN to HR data. the study evaluates the effectiveness of these methods through comparative analysis, providing insights into the most suitable clustering techniques for handling HR datasets. the study implements a strategy based on the Random Forest algorithm, coupled with a Grid Search technique, to enhance both the accuracy and generalizability of the classification model. this approach not only boosts the predictive capabilities of the model but also showcases an innovative application of machine learning techniques in model selection and optimization. the findings of this study serve as a valuable resource for corporate management, offering strategic guidance to mitigate unnecessary employee turnover, thereby fostering a more stable workforce environment. ©2024 IEEE.

关键词： Forestry

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Graph Cluster Analysis of Transformer Oil Chromatographic Data Based on Deep Reinforcement learning 4

Graph Cluster Analysis of Transformer Oil Chromatographic Da...

引用

4th international conference on Energy Engineering and Power Systems, EEPS 2024

作者： Liu, Meng Yang, Yi Chen, Hao Chen, Cheng Wang, Zhuo Wang, Siyuan State Grid Shandong Electric Power Research Institute Jinan China Nanjing China State Grid Institute of Electrical Engineering Wuhan Nari Co. Ltd. Wuhan China

ISBN: (纸本)9798350366914

Dissolved Gas Analysis (DGA) in transformer oil is an important technical means for the operation and maintenance of transformers, and clustering algorithms are an important intelligent algorithm for oil chromatography analysis. However, the objective function of clustering algorithms is a typical non-convex function, and its optimization solution process is a local search hill-climbing algorithm. Traditional clustering algorithms have problems with computational interruption and non-convergence of iterations when the amount of data increases sharply. they lack sufficient generalization ability during fault diagnosis, making it difficult to adapt to the dissolved gas analysis of transformer faults under different environments and attributes. there are still technical and engineering challenges in system integration and application. this paper uses a graph clustering method based on deep learning, utilizing deep learning methods to generate a class integrated sequence, and using this integrated sequence as the initial clustering center for clustering. A graph clustering algorithm based on the complexity of node clustering is proposed to perform cluster analysis on dissolved gas samples, assigning pseudo-labels to data nodes with low clustering complexity, and using the supervisory information provided by pseudo-labels to reduce the clustering complexity of other data nodes, thereby obtaining the clustering results of dissolved gases in transformer oil. Relevant case analysis shows that the method in this paper has a high data processing speed and strong generalization ability, improving the ability to recognize fault patterns in the analysis of dissolved gases in transformer oil, and has strong practical value. © 2024 IEEE.

关键词： Deep reinforcement learning

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Elastic Stabilization Control for Polynomial Parameter-varying Systems with applications to a Turbofan Engine 4

Elastic Stabilization Control for Polynomial Parameter-varyi...

引用

4th international conference on Control theory and applications, ICoCTA 2024

作者： Su, Chuzheng Chen, Jialin Wu, Shenghua Zeng, Jianping Xiamen University School of Aerospace Engineering Xiamen China

ISBN: (纸本)9798331506155

Aiming at the problem of the controller parameter perturbation and exponential stabilization of controlled plants in complex working environments, an elastic stabilization control method, which is based on polynomial parameter-varying (PPV) model is proposed. To address the parameter perturbation problem of controller in the controlled plants, the conditions for the existence of the elastic controller that ensures the exponential stability of the PPV systems are derived using Lyapunov stability theory and state-And-parameter-dependent (SPD) linear matrix inequalities. the controller is solved by using the sum of squares (SOS) convex optimization technique. Finally, the proposed method is applied to the control problem of PPV model of turbofan engine. the numerical simulation results show that the designed controller has good control performance, and is robust in the large envelope range of the engine. © 2024 IEEE.

关键词： Convex optimization

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Efficient Deepfake Detection Using AI 4

Efficient Deepfake Detection Using AI

引用

4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Barik, Pradip Kumar Pandya, Param Gaur, Saksham Shivansh Pandit Deendayal Energy University Department of ICT Gandhinagar India

ISBN: (纸本)9798331528171

Deepfake technology poses a significant threat to the integrity of multimedia content, raising urgent concerns for media authentication and trustworthiness. this paper addresses the challenges of detecting deep fake videos, focusing on the impact of different dataset characteristics and the effectiveness of cross-dataset testing. We explore the application of reinforcement learning (RL) techniques for deepfake detection, particularly focusing on cross-dataset training. Two Convolutional Neural Network (CNN) models are employed, and the RL agent is trained using ten augmentations as actions. the Proximal Policy optimization (PPO) and Deep Q Network (DQN) algorithms are evaluated for learning the policy, showcasing the potential of RL in enhancing the robustness of deep fake detection models. Furthermore, the paper investigates the influence of dataset char-acteristics, such as size, diversity, and quality, on the performance of deepfake detection models. It analyzes the challenges posed by datasets with varying characteristics and explores strategies to improve model generalization and adaptability across different datasets. the findings underscore the importance of considering dataset characteristics in the design and evaluation of deepfake detection systems, highlighting the need for standardized datasets and evaluation protocols in the field of multimedia forensics. the results demonstrate the efficacy of the proposed methodologies, highlighting the importance of dataset characteristics and cross-dataset testing in improving the reliability of deepfake detection systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Focal Iou loss: More attentive learning for bounding box regression 24

Focal Iou loss: More attentive learning for bounding box reg...

引用

4th international conference on Internet of things and Machine learning

作者： Liao, Yuan Cao, Peixia Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China Jiangxi Prov Ctr Dis Control & Prevent Finance Off Nanchang Peoples R China

ISBN: (纸本)9798400710353

In this paper, we investigate a more efficient IoU loss based bounding box localization mechanism on top of end-to-end target detection frameworks to further improve the regression accuracy of object detection methods. Aiming at the limited spatial perception ability of deep convolution features, this paper proposes an optimization strategy by combining shallow spatial feature feed-forward mechanism (SSFF) with focal IoU loss function for bounding box regression tasks in target detection. this strategy firstly constructs a channel to transfer important location information in shallow spatial features to deep spatial features to reduce the loss of spatial details. Secondly, it constructs a focal IoU loss function based on the IoU loss, which dynamically adjusts loss weights according to the regression difficulty of different bounding boxes, to improve the positioning ability of regression networks on difficult bounding boxes. the experimental results show that the proposed methods can effectively improve the regression accuracy of target detection models.

关键词： Target detection Bounding box regression IoU loss function Focal loss function

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Research on Path Planning Algorithm Based on the Integration of Ant Colony Algorithm and AI Decision 4

Research on Path Planning Algorithm Based on the Integration...

引用

4th IEEE international conference on Data Science and Computer Application, ICDSCA 2024

作者： Zhao, Yan Zhao, Wanfang Chen, Lili Beijing Polytechnic Beijing China

ISBN: (纸本)9798350368239

How to plan the vehicle path efficiently and accurately is a key problem in route planning. To solve this problem, in this paper, an Algorithm combining Ant Colony Algorithm (ACA) and Proximal Policy optimization (Proximal Policy optimization) is proposed, PPO's new approach-optimization-Ant Colony Policy (OACP). OACP method realizes the preliminary exploration and optimization of the path through ACA, and uses the pheromone left by ants in the search process to guide the path selection. At the same time, PPO is used for strategy learning and dynamic decision-making, and the path planning strategy is constantly adjusted through reinforcement learning mechanism, so as to ensure high planning accuracy and improve the real-time adaptability and robustness of the path. Finally, through the comparison experiment and analysis with the traditional path planning algorithm learning, the experimental results show that O-ACP method has significant advantages in improving the precision of path planning and the number of iterations. Especially in the complex road environment, O-ACP method can more accurately plan the optimal path and reduce the uncertainty in the decision-making process. this fully verifies the reliability and effectiveness of the architecture in intelligent driving path planning. ©2024 IEEE.

关键词： Ant colony optimization

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learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order optimization 27

Learning Sampling Policy to Achieve Fewer Queries for Zeroth...

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27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Zhai, Zhou Shi, Wanli Huang, Heng Chang, Yi Gu, Bin Nanjing Univ Informat Sci & Technol Nanjing Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Univ Maryland College Pk MD USA Jilin Univ Sch Artificial Intelligence Changchun Peoples R China

Zeroth-order (ZO) methods, which use the finite difference of two function evaluations (also called ZO gradient) to approximate first-order gradient, have attracted much attention recently in machine learning because of their broad applications. the accuracy of the ZO gradient highly depends on how many finite differences are averaged, which are intrinsically determined by the number of perturbations randomly drawn from a distribution. Existing ZO methods try to learn a data-driven distribution for sampling the perturbations to improve the efficiency of ZO optimization (ZOO) algorithms. In this paper, we explore a new and parallel direction, i.e., learn an optimal sampling policy instead of using a random strategy to generate perturbations based on the techniques of reinforcement learning (RL), which makes it possible to approximate the gradient with only two function evaluations. Specifically, we first formulate the problem of learning a sampling policy as a Markov decision process. then, we propose our ZO-RL algorithm, i.e., using deep deterministic policy gradient, an actor-critic RL algorithm to learn a sampling policy that can guide the generation of perturbed vectors in getting ZO gradients as accurately as possible. Importantly, the existing ZOO algorithms for learning a distribution can be plugged in to improve the exploration of ZO-RL. Experimental results with different ZO estimators show that our ZO-RL algorithm can effectively reduce the query complexity of ZOO algorithms and converge faster than existing ZOO algorithms, especially in the later stage of the optimization process.

关键词： Function evaluation

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