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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是421-430 订阅

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An Incentive Mechanism for Federated learning in IIoT: A Long-Term Perspective

An Incentive Mechanism for Federated Learning in IIoT: A Lon...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Chu Wang Shuo He Shupan Li Longlong Xing Saisai Zhu Gaohuan Xu School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Federated learning (FL) is a new machine learning paradigm that can protect data privacy while enabling data-driven insights. Its performance heavily relies on the quality of local models that participants contribute. However, devices may be reluctant to contribute their resources without sufficient incentives. A carefully crafted incentive mechanism is crucial to the practical application of FL. However, existing studies overlook the long-term performance, the requirement of data freshness in some Industrial Internet of things (IIoT) applications, and the capability that IIoT devices can engage in multiple tasks simultaneously. To address these issues, we formulate the incentive mechanism as a stochastic optimization problem which minimizes the long-term energy cost of IIoT devices. Lyapunov optimization is employed to design an online winner selection and resource allocation algorithm. Moreover, we consider the data freshness as a criterion to design reward for devices to motivate their participation. Simulation results show that the proposed mechanism can effectively reduce the average energy cost of devices and ensure continuous participation.

关键词： Performance evaluation Costs Federated learning Data integrity Simulation Stochastic processes signal processing algorithms Resource management Optimization Industrial Internet of things

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A Method of Predicting the Deposited Film thickness in IC Fabrication Based on Automatic machine learning

A Method of Predicting the Deposited Film Thickness in IC Fa...

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international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Yumeng Shi Yu Cai Yining Chen School of Micro-Nano Electronics Zhejiang University Zhejiang China School of Microelectronics University of Science and Technology of China Anhui China

the semiconductor IC (Integrated Circuits) industry involves a multitude of fabrication processes, and quality verification at critical stages is crucial for ensuring successful post-processing outcomes. However, conventional physical measurements can be time-consuming, and machine learning methods that predict thickness of deposited films require extensive effort in model selection and hyperparameter optimization. Automatic machine learning, a new intelligent learning approach that uses Bayesian optimization to automate feature extraction from input data, model selection and hyperparameter optimization, has the potential to minimize the time and cost required for training and model selection. this paper presents a novel approach for predicting film thickness of IC deposited films based on automatic machine learning. the study analyzes three key techniques including meta-learning, Bayesian optimization and model integration, and establishes a prediction model using the Auto-Sklearn system. the model's prediction performance is compared with three common machine learning algorithms, namely random forests, support vector machines and multilayer perceptron, and the results show that the automatic machine learning prediction model outperforms these algorithms with a coefficient of determination of 0.759 on the test set and a mean relative error of 0.262% for the prediction results. this paper demonstrates the efficacy of automatic machine learning for predicting film thickness and provides a promising direction for future work in the semiconductor industry.

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Experimental design of emotion recognition based on machine learning 23

Experimental design of emotion recognition based on machine ...

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Proceedings of the 2023 6th international conference on machine learning and Natural Language processing

作者： Jintao Yu Mingze Jiang Tingwei Liang School of Computer and Information Engineering Harbin University of Commerce China School of Astronautic Harbin Institute of Technology China

ISBN: (纸本)9798400709241

In this paper, a wrist bracelet was used to measure the subjects' heart rate signals and skin electrical signals, and three types of emotions (positive, neutral and negative) were induced by designing an experimental environment. the heart rate and skin electrical signals corresponding to emotions were collected through the bracelet, and the recognition accuracy of the three types of emotions was finally obtained through data preprocessing and the support vector machine model in machine learning. At present, how to build an experimental environment that can fully induce subjects' emotions is a major difficulty in emotion recognition, so this paper provides a specific emotion recognition environment design and detailed steps. Meanwhile, through a large number of experiments, the kernel function in the support vector machine model, namely Gaussian kernel function, is determined, and grid search is introduced to search for hyperparameter C. To get the optimal parameters and results. A total of 20 people were measured in this experiment. the experimental results obtained by SVM model were positive: the recognition accuracy of skin electrical signal was 0.8422, the recognition accuracy of heart rate signal was 0.8345, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion was 0.8832. Negative: the recognition accuracy of skin electrical signal is 0.9812, the recognition accuracy of heart rate signal is 0.9385, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion is 0.9902. Neutral: the recognition accuracy of skin electrical signal was 0.6403, the recognition accuracy of heart rate signal was 0.5308, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion was 0.5438

关键词： Emotion recognition Experimental design machine learning Physiological signal

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Joint Power Control and Device Scheduling in Federated learning with Energy Harvesting

Joint Power Control and Device Scheduling in Federated Learn...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Boyuan Zhang Ning Lu Department of Electrical and Computer Engineering Queen's University Kingston ON Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Federated learning (FL) is emerging as a promising approach for enabling collaborative machine learning without centralizing data, particularly within Internet of things (IoT) networks. Despite its potential, the challenge of reducing communication delay and energy consumption in client-server transmissions is considered a critical challenge in wireless network implementations of FL. Moreover, the capability of many IoT devices to harvest energy introduces a novel dimension to optimizing FL efficiency, which remains underexplored. In this paper, we present POLISH, a novel POwer controL, and devIce ScHeduling algorithm derived from an optimization problem designed to navigate a tripartite trade-off between minimizing communication delay (specifically, the transmission time for model updates), reducing total energy consumption, and maximizing device participation. through simulations, POLISH has shown superior performance in reducing communication delay and energy consumption when compared to existing benchmarks, highlighting its effectiveness in enhancing FL performance in energy-constrained IoT networks.

关键词： Performance evaluation Energy consumption Scheduling algorithms Federated learning Power control signal processing algorithms Delays Internet of things Energy harvesting Optimization

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Optimization of Antenna Dimensions for Improved Frequency Response Using machine learning: A Survey

Optimization of Antenna Dimensions for Improved Frequency Re...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Mona K. ElAbbasi Mervat Madi Karim Y. Kabalan Electrical and Computer Engineering Department American University of Beirut Beirut Lebanon Electrical and Electronics Engineering School Department Higher Colleges of Technology Abu Dhabi UAE

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

machine learning techniques have widespread applications in optimization across various domains. this survey delves into their specific utilization in antenna design, where dimensions such as height and width play critical roles in performance metrics. Additionally, this study investigates the performance of several regression methods for predicting nonlinear relationships within antenna design variables affecting resonant frequency. Our analysis highlights the superiority of support vector regression (SVR), polynomial regression, and neural networks over linear regression and genetic algorithms in terms of predictive accuracy. In essence, the paper underscores the importance of selecting appropriate regression techniques based on the complexity of antenna design variables, recommending SVR, polynomial regression, and neural networks for modeling intricate patterns effectively and providing accurate predictions.

关键词： Surveys Accuracy Design methodology Neural networks Linear regression Resonant frequency Predictive models Polynomials Antennas Genetic algorithms

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Cascade Temporal Convolutional Network for Multitask learning

Cascade Temporal Convolutional Network for Multitask Learnin...

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international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Huiyun Zhang Heming Huang Henry Han Department of Computer Science School of Engineering and Computer Science Baylor University USA School of Computer Science Qinghai Normal University Xining China School of Computer Science Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

Multi-task learning (MTL) is a powerful technique in machine learning that enables the simultaneous training of multiple tasks. In this paper, we propose a novel MTL approach for the recognition of emotions, speakers, and gender in speech signals. Our approach presents a novel deep learning model called the Cascade Temporal Convolutional Network (CTCN to handle MTL efficiently. CTCN is designed to effectively improve the parallel processing ability of the model in multitasking learning. It provides an effective solution for improving the performance of deep learning in multitasking scenarios. CTCN is particularly adept at processing sequences of data with varying lengths and extracting features that are relevant to multiple tasks. Our experimental results demonstrate that the proposed CTCN approach for MTL achieves significantly superior performance on all tasks on benchmark datasets compared to other deep learning models. this study will inspire more future work in the MTL field.

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Analytical Approach of machine learning for Prediction Models

Analytical Approach of Machine Learning for Prediction Model...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Mohit Kumar Sanjay Kumar Dubey Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

With the increasing availability of data, machine learning (ML) predictive models have become a popular tool for making informed decisions in various fields. However, choosing suitable algorithms and techniques to develop accurate forecasting models is a challenging task. In this study, we aim to compare different ML algorithms and methods to develop accurate prediction models. We analyzed the results to determine the most effective approach to developing accurate forecasting models. In addition, we investigated the effect of hyperparameter tuning on the performance of this model. the results of this study can provide valuable insights for practitioners and researchers in choosing the most appropriate machine learning algorithms and methods to develop accurate prediction models. Our study shows the impact of choosing the right features and choosing the method of data pre-processing and hyperparameter tuning on model performance. Finally, our results can inform the design of more accurate prediction models and contribute to the development of the field of machine learning.

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Detection of Stress Over Social Interactions Using machine learning and Natural Language processing

Detection of Stress Over Social Interactions Using Machine L...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Aishwarya G Kavitha B Purnima S Mittalakod Sunitha K Bhagyashree Ambore Information Science and Engineering RNS Institute of Technology Bengaluru India

In the field of natural language processing, the investigation of stress is turning out to be increasingly critical. the advancement of recommender frameworks and computerized emotional well-being assessments can profit from the investigation of stress discovery for psychological wellness expectation, as per earlier examination. In this study, we enhance sentiment and emotion analysis to measure the stress levels of people by analyzing their social networking posts and comments. Using machine learning techniques and the deep learning sentiment analysis model BERT, we analyze sentiment using enormous datasets of tweets. We also used an unsupervised machine learning technique called Latent Dirichlet Allocation to scan a set of papers to find word and phrase patterns and create word groups and related phrases that most accurately represented the documents. this aids in predicting the subject that is connected to the textual information. We will be able to identify users' emotions online with the help of these models. Additionally, stress or depression can be studied using these feelings. In summary, the BERT model and ML models have very high detection rates. the benefits of this research can be shown in terms of mental wellness.

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Modulation Recognition Method of Underwater Acoustic signal Based on Parallel Network

Modulation Recognition Method of Underwater Acoustic Signal ...

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IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Zuoqian Yang Wei Shi Fuqiang Wang Qingdao University of Science and Technology Qingdao China

In underwater non-cooperative communication systems, efficient and accurate identification of underwater acoustic signal modulation is a prerequisite for stable and reliable communication. However, the underwater environment is complex and changeable, and various noises seriously interfere with the correct identification of the modulation mode of underwater acoustic signals at the receiver. A parallel neural network model is proposed in this paper based on the Encoder module and convolutional module, which fuses the convolutional network and the attention mechanism. In the results, higher recognition accuracy than the traditional machine learning algorithms is demonstrated. It can achieve high accuracy in identifying eight underwater acoustic signal modulation methods on the South China Sea dataset with a recognition accuracy of 99.3%.

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Application process of machine learning in cyberspace security 6

Application process of machine learning in cyberspace securi...

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6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Bing, Han Hao, Sun Fangwei, Zhong Shuai, Zou Tao, Qian Changjiang, Yu Xinjiang Information Industry Co. Ltd XinJiang China

ISBN: (纸本)9780738143705

Under the influence of the rapid development of computer Internet technology, computers and computer technology have been widely spread and applied in various fields of society. Hundreds of millions of network access points, networked devices and network applications have produced massive amounts of Data has brought huge difficulties and challenges to cyberspace security, and the emergence of machine learning has provided a series of effective analysis and decision-making tools, especially its advocacy for the effective use of computer information. Have human thinking, become smarter and smarter, in order to help people solve various computer network security problems. Guided by the first-level discipline of cyberspace security, this article focuses on the latest research results of the application of machine learning technology in the field of cyberspace security, and elaborates the application process of machine learning technology in cyberspace security research. Finally, it summarizes the problems in existing solutions, as well as the future development direction and challenges of machine learning technology in cyberspace security research. © 2021 IEEE.

关键词： machine learning

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