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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是51-60 订阅

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Optimal Scheduling for Integrated Energy System via Model-Based Reinforcement learning 24

Optimal Scheduling for Integrated Energy System via Model-Ba...

引用

2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Cheng, Kexin Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao Zhang, Huaqin School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

The Integrated Energy System (IES) integrates energy consumption, conversion, and storage to increase the utilization of renewable energy sources and fossil fuels. When using heuristic algorithms and mathematical optimization methods for optimal scheduling of IES, these methods often face challenges of high computational cost and insufficient model accuracy, for which Reinforcement learning (RL) offers a promising solution. However, most studies have used Model-Free Reinforcement learning (MFRL) for optimal scheduling, which is limited by problems of low sampling efficiency and long training time. Model-Based Reinforcement learning (MBRL) is an effective alternative to accelerate learning and facilitate RL applications. Therefore, this paper compares the performance gap between MFRL and MBRL in the optimal scheduling problem of IES to determine the superiority of MBRL. Experimental results show that the MBPO algorithm significantly improves sampling efficiency and learning speed at high prediction accuracy, outperforming the Soft Actor-Critic (SAC) algorithm. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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SACL: Sequential Augmentation with Curriculum learning in dataset Level 13th

SACL: Sequential Augmentation with Curriculum Learning in Da...

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13th international conference on Natural Language Processing and Chinese computing

作者： Mae, Biao Kong, Fang Soochow Univ Suzhou Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Curriculum learning, as a deep learning training strategy, has proven its effectiveness across many Natural Language Processing (NLP) and Multi-modal tasks. However, traditional Curriculum learning is often instance-level, which requires manually classifying each instance in the data into different difficulty level. This progress is not only time-consuming but also costly in labor. To outcome this challenge, we introduce an innovative training approach named Sequential Augmentation with Curriculum learning (SACL). SACL is a curriculum learning approach in dataset-level, which utilizes various data augmentation techniques to generate data in various difficulty levels and feeds this data to the model in an order of increasing difficulty. This approach effectively avoids the process of manually classifying data in traditional curriculum learning which greatly reduces the consumption of time and resources. To validate our method, we conduct experiments on the MET-MEME dataset for a multimodal emotion classification task and deeply analyze the impact of curriculum learning on the training results. The results prove the effectiveness of our method.

关键词： Curriculum learning data Augmentation Knowledge System

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Use neural Networks to Recognize Students' Handwritten Letters and Incorrect Symbols

Use neural Networks to Recognize Students' Handwritten Lette...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options. © 2025 SPIE.

关键词： Education and training machine learning data modeling Neural networks Image classification Visual process modeling Computer vision technology Deep learning Performance modeling Transformers

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Fault Detection in Semiconductor Manufacturing Using Advanced machine learning Techniques with Interpretability 4th

Fault Detection in Semiconductor Manufacturing Using Advance...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Srisailam, Swaroop Mandal, Pallab Aditya, Kalur Lalith Pankajakshan, Praveen Rajamani, Kumar T. CropIn Technol Bangalore Karnataka India Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Sri Sathya Sai Inst Higher Learning Anantapur Andhra Pradesh India

ISBN: (纸本)9783031837951;9783031837968

Fault detection in semiconductor manufacturing is complex due to class imbalance, missing values, outliers, and high data dimensionality. This study employs advanced machine learning techniques on the SECOM dataset to enhance fault detection accuracy and interpretability. Using oversampling methods like SMOTE and ADASYN, along with rigorous data cleaning and feature selection, we reduced features from 590 to 33. Principal Component Analysis (PCA) retained 91% of the dataset's variance. Several classifiers, including Random Forest, SVM, KNN, Logistic Regression, Gradient Boosting, and XGBoost, were employed. A Voting Classifier combining top-performing models achieved superior results, with an accuracy of 99.68%. Emphasizing interpretability, we applied Permutation Feature Importance (PFI) and Partial Dependence Plots (PDP) to understand the model's behavior and the significance of different sensors. Our approach offers a robust, accurate, and interpretable solution for fault detection in semiconductor manufacturing.

关键词： Semiconductor Manufacturing machine learning data Preprocessing Interpretable machine learning

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IoT-Based Convolutional Neural Networks in a Farm Pest Detection Using Transfer learning 5th

IoT-Based Convolutional Neural Networks in a Farm Pest Detec...

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5th international conference on computing science, Communication and Security

作者： Jani, Keyurbhai A. Chaubey, Nirbhay Kumar Panchal, Esan Tripathi, Pramod Yagnik, Shruti Gujarat Technol Univ Comp IT Engn Ahmadabad 382424 Gujarat India Ganpat Univ Comp Sci Mahesana 384012 Gujarat India Govt Polytech Informat Technol Gandhinagar 382027 Gujarat India Indus Univ Comp Engn Ahmadabad 382115 Gujarat India

ISBN: (纸本)9783031751691;9783031751707

In this study explores agriculture pest detection using transfer learning with IoT devices, evaluating VGG16, VGG19, Inception, and Xception CNN architectures with the agripest dataset. VGG16 and VGG19 show effective learning with consistent accuracy improvements. Inception V3 exhibits strong training but with variability in validation metrics, while Xception demonstrates robust performance and strong generalization to new data. The integrated system utilizes cameras, sensors, and drones for real-time image processing through a gateway and cloud server with a customized agripest dataset. Transfer learning generates a deployable.h5 file for pest identification. The generated custom model deployed on a gateway or server to classifies pests, alerting farmers through SMS, dashboard, or mobile app notifications. This synergy between machine learning and IoT offers rapid and precise pest detection in agriculture.

关键词： IoT deep learning CNN pest detection cloud farm gateway

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Application and research of particle swarm optimization algorithm in cloud data backup 2

Application and research of particle swarm optimization algo...

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

作者： Song, Tao Liu, Na Liaoning Economic Vocational Technological Institute Shenyang110122 China

ISBN: (纸本)9781510689053

With the rapid development of cloud computing technology, data security and backup have become an important guarantee for enterprises. The purpose of this study is to optimize the data allocation, storage strategy and resource management of cloud data backup through particle swarm optimization algorithm. In this paper, the basic principle and flow of Particle Swarm Optimization algorithm are introduced in detail, and a cloud data backup model based on Particle Swarm Optimization algorithm is proposed to solve the problems of resource allocation, unreasonable selection of storage nodes and optimization of backup time strategy in data backup. Through experimental comparison and performance analysis, this paper verifies the effectiveness and feasibility of Particle Swarm Optimization algorithm in improving cloud data backup efficiency, reducing backup cost and optimizing resource utilization. This study proves the important application value of Particle Swarm Optimization in cloud data backup optimization, which can improve backup efficiency and reduce resource consumption. This study also provides theoretical basis and practical guidance for further exploring the application of intelligent algorithms in cloud computing. © 2025 SPIE.

关键词： Particle swarm optimization data backup Clouds Particles data storage Mathematical optimization machine learning Cloud computing data modeling data processing

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Construction and realization of modern Chinese vocabulary base driven by big data 2

Construction and realization of modern Chinese vocabulary ba...

引用

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

作者： Luo, Zhong Faculty of Language and Literature Anhui Sanlian University Anhui Hefei230601 China

ISBN: (纸本)9781510689053

With the rapid advancement of big data technology, the construction of large-scale databases has become a critical focus in computer science. Existing research primarily explores the integration of big data with various computational methods, such as machine learning, data mining, and cloud computing, to enhance data processing efficiency and analytical capabilities. However, there is still a gap in the development of specialized databases that can effectively leverage big data for specific applications. This study aims to address this gap by proposing a novel approach to constructing a modern Chinese vocabulary database driven by big data techniques. The research focuses on the application of advanced computer methods, including data preprocessing, indexing, and retrieval algorithms, to optimize database performance. The ultimate goal is to develop a robust and scalable database system that can efficiently handle large volumes of data and provide accurate and timely information for various computational tasks. © 2025 SPIE.

关键词： data assimilation

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Privacy-Preserving Three-Factors Authentication and Key Agreement for Federated learning 6th

Privacy-Preserving Three-Factors Authentication and Key Agr...

引用

6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Wang, Guojun Jiang, Guixin Zhao, Yushuai Ji, Yinglin Yancheng Polytechnic College Yancheng224005 China School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

With the development of big data and distributed computing, federated learning has received widespread attention for protecting data privacy. Federated learning is a distributed machine learning framework that allows multiple participants to collaboratively train shared models with locally retained data. However, the issues of communication security and authentication between participants have emerged as key challenges affecting the security of federal learning systems. To address this challenge, we propose an authentication and key agreement protocol for federated learning environment. The proposed protocol enables authentication between edge devices and server, which establishes a secure session key to secure communication between participants. Additionally, we verify the security of the session key by the verification tool. Finally, experimental evaluation results show that the protocol performs well in terms of computational overhead and communication latency and is suitable for large-scale federated learning systems with resource-constrained devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

引用

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

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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Modal-Centric Insights Into Multimodal Federated learning for Smart Healthcare: A Survey 24th

Modal-Centric Insights Into Multimodal Federated Learning fo...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Di Liu, Wenjian Gao, Longxiang Qu, Y. Neil. Zhang, Hu Shi, Jihong The Faculty of Data Science City University of Macau Macau China Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Medical Integration and Practice Center Shandong University Jinan China

ISBN: (纸本)9789819615476

Federated learning (FL) has progressed, providing a distributed mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Multimodal federated learning (MFL) combines federated learning’s distributed nature with the flexibility to work with various data types. This capability is particularly relevant in medical contexts, where healthcare data can range from electronic health records (EHR) to radiology imaging, genomic data, and wearable device information. The key advantage of MFL in medical settings is the ability to create robust models without centralizing sensitive patient information. However, there are no existent research to comprehensively review the application status of MFL in smart healthcare systems from the modal-centric aspect. Motivated by this, we explore the systematization of knowledge about multimodal federated learning on medical data, find the challenges of this area and related solutions. With this research, we hope to provide a systematical review of this under-explored domain and provide insights for forthcoming researchers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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