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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33936 条记录，以下是1311-1320 订阅

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An Overview of data-Driven Methods for the Online State of Charge Estimation

An Overview of Data-Driven Methods for the Online State of C...

引用

IEEE international conference on Environment and Electrical engineering (EEEIC) / IEEE Industrial and Commercial Power Systems Europe (I and CPS Europe) conference

作者： Eleftheriadis, Panagiotis Dolara, Alberto Leva, Sonia Politecnico Milano Dept Energy Milan Italy

ISBN: (纸本)9781665485371

In the last years, the drift toward electric mobility and the need for renewable energy penetration placed the batteries and their control in a prominent position. A critical parameter for the battery management system (BMS) is the state of charge (SOC) of the battery pack. This paper gives an overview of the trends of the last 5 years of SOC estimation, using data-driven estimation methods. Due to the evolution of electronic materials and the abundance of available data, data-driven methods became popular and advantageous.

关键词： battery model data-driven estimation machine learning state of charge

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Differentially Private Auction for Federated learning with Non-IID data

Differentially Private Auction for Federated Learning with N...

引用

2022 CCF international conference on Service Science, CCF ICSS 2022

作者： Ren, Kean Sun Yat-sen University School of Software Engineering Zhuhai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

With the increase in clients' concerns about their privacy, federated learning, as a new model of machine learning process, was proposed to help people complete learning tasks on the basis of privacy protection. But the large-scale application of federated learning depends on the extensive participation of individual clients. This motivates the incentive mechanism design to increase clients' willingness to participate. However, the incentive mechanism should take into account non-IID issues and privacy protection of clients' sensitive information of data distribution. These two aspects are not well studied jointly in the existing incentive mechanism design. In this paper, we propose a differentially private auction for federated learning with non-IID data. It can not only protect clients' private information of data distribution with differential privacy but also incentivize clients with suitable data distribution to deal with non-IID issues. Finally, we prove that the designed mechanism meets the design objective through detailed theoretical analysis. © 2022 IEEE.

关键词： machine design

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An MCTS-Based Recommender System for Education Complex

An MCTS-Based Recommender System for Education Complex

引用

2022 international conference on machine learning and Knowledge engineering, MLKE 2022

作者： Zhao, Debin Hu, Zhengyuan Yang, Yinjian Institute of Big Data Kunshan Xuanlife Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9781665495677

Although recommender systems in the education industry filter overload information and bring convenience to both learners and education facilities, challenges such as the coldstart problem and issue of timeliness, however, are more significant than recommending traditional goods while providing educational advice for students. To address this issue, this paper proposes an innovative recommender system based on Monte Carlo tree search (MCTS), which performs root cause analysis of the increased number of students during a certain period in each education facility, followed by computation of similarities between root cause groups and the rest of the students using Mahalanobis Distance. An experiment with a six-month dataset obtained from an education complex named Luoyang City Hall was conducted, and the results of A/B testing indicate that the treatment group experiences a higher student attendance rate than samples using traditional recommendation methods. © 2022 IEEE.

关键词： Recommender systems

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Advances in Electronic Health Record Analysis for Diabetes Risk Prediction: A machine learning-Enhanced Framework 5

Advances in Electronic Health Record Analysis for Diabetes R...

引用

5th international conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Ye, Bingqing Shen, Zhangyi Ji, Aohan Jiao, Yu Niu, Yunfei Zuo, Kaizhong Hu, Peng Li, Wenjie School of Computer and Information Anhui Normal University Wuhu China

ISBN: (纸本)9798331528911

Background: Diabetes mellitus is a prevalent chronic condition with escalating global incidence, underscoring the urgency for efficient early detection and management strategies to ameliorate healthcare burdens and enhance patient quality of life. Methods: We conducted a systematic data processing pipeline on electronic health examination data, which included de-identification, imputation of missing data, and feature scaling. Feature selection was performed using Recursive Feature Elimination (RFE) complemented by a literature review to identify the most informative predictors. We developed five machine learning models: Logistic Regression (LR), Support Vector machine (SVM), Random Forest (RF), XGBoost, and LightGBM (LGBM). Model performance was quantitatively assessed using a comprehensive set of metrics: accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The LGBM model, which demonstrated the highest predictive accuracy, was subjected to hyperparameter optimization and cross-validated on an independent public dataset to ensure its generalizability. Results: The initial evaluation of the LGBM model yielded an accuracy of 90.52%, precision of 89.06%, recall of 88.97%, F1-score of 88.67%, and an AUC-ROC of 0.965. After hyperparameter tuning, these metrics were further improved to 91.38% accuracy, 89.87% precision, 90.13% recall, 89.94% F1-score, and an AUC-ROC of 0.969. The model's predictive capabilities were corroborated through successful cross-validation on external datasets, indicating its robustness and reliability. Conclusion: The study presents a sophisticated diabetes prediction model leveraging machine learning algorithms on electronic health examination data. The model's superior predictive performance and validated generalizability suggest its potential as a valuable asset for healthcare providers, aiding in the early identification of diabetes risk, thereby facilitating more informed clinical

关键词： Electronic health record

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Local Density-Based Adaptive Undersampling Approach for Handling Imbalanced and Overlapped data 4

Local Density-Based Adaptive Undersampling Approach for Hand...

引用

4th international conference on Computer Communication and Artificial Intelligence (CCAI)

作者： Liu Yi Huang Xian Cao Zhen Li Honglu Univ Elect Sci & Technol China Chengdu Peoples R China Chengdu Jwell Grp Co Ltd Chengdu Peoples R China

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770;9798350362763

The issue of class imbalance poses a significant challenge to conventional machine learning. When processing imbalanced data sets, classical supervised learning algorithms often skew towards the majority class, causing inaccurate predictions for the minority class. The consequences of inaccurate prediction are severe when the information of the minority class becomes crucial. Moreover, when samples of varying classes with severe proportions overlap, the problem escalates. The majority of existing algorithms that focus on class imbalance and class overlap have demonstrated promising results but most of them are built upon the hypothesis that instances are uniformly distributed in the data space, ignoring the complexity of real-world data distribution. When real world data is incorporated, this deficiency hinders their capability to effectively identify overlapping regions, resulting in the loss of sample information. To address this concern, we incorporate density factors into classical methods for identifying overlapping regions, proposing the Local density-based adaptive undersampling approach for handling imbalanced and overlapped data (LDAU). LDAU first computes the local density for each instance, and then select the extent to which majority class instances should be removed for each overlapping region based on a comparison of the densities of the majority and minority classes. In doing so, it achieves clear boundaries for both the majority and minority classes while minimizing the degradation of majority class instance information.

关键词： class imbalance class overlap machine learning

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Indoor Location Algorithm Based on Migration PSO-ELM in the Absence of Training Sets

Indoor Location Algorithm Based on Migration PSO-ELM in the ...

引用

international conference on Guidance, Navigation and Control, ICGNC 2022

作者： Wu, Yifan Wang, Wenhao Zhang, Jing School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

Ultra-wideband technology has its unique advantages and is widely used in indoor localization. UWB positioning methods based on machine learning can significantly improve localization accuracy. However, the algorithms are generally trained based on experimental data and indoor features, which is difficult to applicate in other indoor places in the absence of scene training sets. In this work, a migration localization scheme for particle swarm algorithm to optimize the extreme learning machine (Migration PSO-ELM) is presented. This positioning algorithm generates virtual training datasets to achieve accurate localization in new indoor places without experimental data. According to the simulation results, Migration PSO-ELM can maintain high accuracy and shorten the deployment time when applied in new scenes without training sets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th international conference on Signal Processing and machine learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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Optimization of Intze Type Water Tank Using machine learning

Optimization of Intze Type Water Tank Using Machine Learning

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international conference on Recent Advancements in Civil engineering, RACE 2022

作者： Ganesh Reddy, S. Komaravolu, Aditya Devi, Kamalini Saduwale, Shrihari Obulesh, A. Telangana Hyderabad India Telangana Hyderabad India Telangana Hyderabad India

ISBN: (纸本)9789819926756

India has an urban population that makes up more than 34% of the country's total population. Water, petroleum, oil products, and other liquids are kept in storage tanks and overhead tanks. These tanks have been employed in several different fields, including agriculture, the food industry, paper mills, firefighting, and water distribution. It is well known in the current situation that nearly every industry is heading toward machine-based automation, starting with the most basic systems up to master-level ones. Among them, machine learning (ML) is one of the crucial tools that resembles Artificial Intelligence (AI) the most. It does this by allowing some well-known data or experience to automatically enhance or estimate the behaviour or condition of the provided data using a variety of methods. To avoid leaks, every tank is designed with a framework free of cracks. The objective of the structural design process is to provide a safe design that complies with all design code criteria while keeping design expenses to a minimum. A recent idea, structural design optimization is based on random information interchange, determining the best design using more precise mathematical algorithms, and methodologies as opposed to human judgment. This study uses machine learning (ML) to bring down the price of planning and designing a tank to a specific water tank capacity. In this study, programs are created to promote the Intze type reinforced concrete water tank design with the lowest possible cost. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Structural design

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Optimization of Virtual machine Layout in Cloud data Center Based on Server Power Consumption Characteristics 1

Optimization of Virtual Machine Layout in Cloud Data Center ...

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1st international conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

作者： Huan, Zhou Donghao, Liu Mingrui, Li Tianjin University School of Electrical and Information Engineering Tianjin China

ISBN: (纸本)9781665452151

Cloud data centers consume huge amounts of energy. More and more research focus on energy conservation and consumption reduction in data centers, and one of the important directions is the layout optimization of virtual machines running on servers. However, this paper finds that in the models and frameworks of existing research, almost no server power consumption characteristics are directly considered in the energy conservation problem. In fact, servers are the most important energy consumption units in the data center, of which the work characteristics and power consumption characteristics directly affect the energy consumption laws of other major energy consumption devices in the data center. Therefore, the real-time power consumption characteristics of the server should be emphatically considered in the energy conservation optimization problem. In this way, the best energy-saving effect can be achieved. Therefore, based on the characteristics of server power consumption, this paper establishes a cloud data center virtual machine layout optimization problem model, and takes the server cluster energy consumption directly as the goal of energy saving optimization. Then we use the Q-learning algorithm to solve the problem, and compares it with many popular optimization algorithms such as the best fitting descending method(BFD), particle swarm optimization(PSO), etc., in order to verify the effectiveness of the model. At the same time, it also proves that the model has good algorithm universality. © 2023 IEEE.

关键词： Virtual machine

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An Efficient Federated Meta Unlearning Algorithm with Enhanced Privacy Protection 20th

An Efficient Federated Meta Unlearning Algorithm with Enhan...

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20th IFIP WG 10.3 international conference on Network and Parallel Computing, NPC 2024

作者： Wang, Yani Ying, Zuobin Pan, Zijie Zhu, Enmin Zhou, Wanlei City University of Macau Macau S.A.R. Taipa China University of Macau Macau S.A.R. Taipa China

ISBN: (纸本)9789819628636

In machine learning, the ability for models to selectively forget specific data is increasingly crucial. Traditional "machine unlearning" methods often involve extensive retraining, which is computationally expensive and impractical for real-time applications. This paper introduces Federated Meta Unlearning (FMU), a novel approach within the Federated learning (FL) framework that merges the adaptability of meta-learning with the precision of machine unlearning. FMU utilizes a modified Model-Agnostic Meta-learning algorithm, employing model decomposition and weighted aggregation techniques. This approach enables efficient data influence removal while minimizing impact on overall model performance. The key innovation of FMU is the decomposition of the federated model into adjustable components, allowing targeted modifications or removal based on unlearning requests, followed by weighted re-aggregation to maintain model integrity and flexibility. Extensive experiments across diverse datasets show that FMU reduces the computational burden of traditional unlearning while preserving high model performance and adhering to privacy standards. Additionally, FMU enhances adaptability to new data and tasks, making it suitable for dynamic and privacy-sensitive settings. In summary, FMU offers a substantial advancement in addressing the challenge of selective forgetting in machine learning. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning

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