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

共 763 条记录，以下是81-90 订阅

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Malicious network traffic detection method based on traffic behavior characteristics and machine learning 6

Malicious network traffic detection method based on traffic ...

引用

6th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Huang, Changhui Wang, Rui Zhao, Jiaxuan Zhang, Lijuan First Research Institute Ministry of Public Security of Prc Beijing China

ISBN: (纸本)9798350305623

this paper first analyzes the research background and current situation of unknown network threat detection, points out the main problems existing in the current method, and clarifies the research scope and basic ideas of this paper. On this basis, the main research contents and innovations of this paper are as follows: 1) In response to the lack of implementation ideas for detecting unknown network threats in large-scale network systems, a coarse-grained extraction approach for network traffic load characteristics is proposed. Based on the load, it provides the most valuable feature information for subsequent machine learning models, reduces learning costs, and improves model fitting ability;2) For coarse-grained load information, we use fine-grained text2vec statistical feature construction techniques for processing;3) Based on the text2vec feature vectors, we select the optimal dimension;4) Using machine learning models based on high-dimensional statistical features to perform complex calculations and output classification results. © 2023 IEEE.

关键词： machine learning malicious traffic identification statistical learning unknown network threat

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machine Vision Applied to Backscattered Electron Imaging 6

Machine Vision Applied to Backscattered Electron Imaging

引用

6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Huang, Junyuan Zhang, Wei Chen, Jingyun Beihang University School of Mechanical Engineering and Automation Beijing China Beijing Institute of Space Launch Technology Beijing China

ISBN: (纸本)9798350325485

Electron beam processing usually needs to be carried out in a vacuum environment. the influence of high temperature and high concentration of metal vapor makes it difficult for traditional machine vision observation systems to perform long-Term observation. In this paper, a machine vision algorithm was used, a quadrupole plate sensor and image fusion algorithm were adopted, and the combination of software and hardware was realized. Finally, we solved the problems of scale imbalance and slice model scanning order in the imaging process and successfully realized clear observation of the surface in all directions. the backscattered electron signal imaging technology has been successfully applied to the electron beam processing process, which meets the online observation requirements of the electron beam processing process. © 2023 IEEE.

关键词： Computer vision

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A New Approach for Estimating Sleep Efficiency from Electromyography signal 6

A New Approach for Estimating Sleep Efficiency from Electrom...

引用

6th international conference on Electrical Information and Communication Technology, EICT 2023

作者： Nijhum, Sadia Nawsheen Chowdhury, Mohammad Anamul Haque Uddin, Md Bashir Khulna University of Engineering & Technology Department of Biomedical Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350370836

the diagnosis of many sleep disorders mostly depends on the accurate estimation of sleep efficiency (SE) that results from the calculation of total sleep time (TST). By using electromyography signal and machine learning techniques, this study aims to develop a method for estimating SE by accurately predicting TST and total wake time (TWT). A dataset of 97 individuals suspected to have sleep issues was employed, with 81 cases serving as training data and 16 as testing data. After pre-processing, nine different features with 511 unique combinations were utilized to find out the best indicator of sleep duration. A total of 12 machine learning algorithms were applied to evaluate performance metrics such as sleep time accuracy, sensitivity, specificity, and F1 score. In addition, TST, TWT, and hence SE were automatically calculated from the classified sleep/wake state. the overall optimal result was obtained by the model 'Decision Tree with a depth of 3' for the feature combination of the 'fast Fourier transform Magnitude Sum' and the 'Mean'. Epoch-by-epoch analysis resulted in sleep time accuracy, sensitivity, specificity, and F1 score of 91.16%, 89.09%, 62.19%, and 85.32%, respectively. In addition, this new approach resulted in an overall relative detection rate of 98.7% for estimating SE. this approach accomplished better results than the existing studies and, hence, would be applicable in diagnosing sleep-related problems, especially sleep apnea. © 2023 IEEE.

关键词： Wakes

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A Method of Predicting the Deposited Film thickness in IC Fabrication Based on Automatic machine learning 6

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

引用

6th international conference on Artificial Intelligence and Big Data, ICAIBD 2023

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

ISBN: (纸本)9781665491259

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. © 2023 IEEE.

关键词： Forecasting

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Experimental design of emotion recognition based on machine learning 6

Experimental design of emotion recognition based on machine ...

引用

6th international conference on machine learning and Natural Language processing, MLNLP 2023

作者： Yu, Jintao Jiang, Mingze Liang, Tingwei School of Computer and Information Engineering Harbin University of Commerce 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 © 2023 ACM.

关键词： Emotion Recognition

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A Quantifiable Detection of voids/Pores and Cracks in Scanning Electron Microscopic images using machine learning Techniques 6

A Quantifiable Detection of voids/Pores and Cracks in Scanni...

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Sornalakshmi, M. Elizabeth Rani, G. Sakthimohan, M. Karthiga Devi, K. Ponsuresh, M. Devadharshni, M. Arulmigu Kalasalingam College of Arts and Science Department of Computer Science Pg Tamilnadu Krishnankoil India KalaignarKarunanidhi Institute of Technology Department of Cse Tamilnadu Coimbatore India KalaignarKarunanidhi Institute of Technology Department of Ece Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Krishnan Kovil India Kalasalingam Academy of Research and Education Department of Cse Tamilnadu Krishnan Kovil India KGiSL Institute of Technology Department of Csbs Tamilnadu Coimbatore India

ISBN: (纸本)9798350394702

Recently, Image processing and analysis is an important in all fields such as science, mechanical and medical field. SEM (Scanning Electron Microscopic) read images and extracts the useful information from the samples. the mechanical expert's analysis the samples in qualitative. they are missing many pores/voids and cracks in the SEM images so the strength of material is not identified properly. By taking this issue, a quantitative analysis is done by so can identify exact pores and cracks present in the SEM image. Here applied machine learning algorithm such as classification and regression algorithm. It classifies the images in the form of RGB where masking techniques is applied. It can analyse the edges of cracks, pores/voids in SEM image. Linear regression predicts the material strength as strong and weak. Here we used improvised classification and regression algorithm. It helps to predict material strength at initial stage. Hence this algorithm is works for both metal and non-metal Images. © 2023 IEEE.

关键词： machine learning

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InterosML @ Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive learning for Event Classification 6

InterosML @ Causal News Corpus 2023: Understanding Causal Re...

引用

6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2023

作者： Patel, Rajat Interos Inc. ArlingtonVA22203 United States

ISBN: (纸本)9789544520892

Causal events play a crucial role in explaining the intricate relationships between the causes and effects of events. However, comprehending causal events within discourse, text, or speech poses significant semantic challenges. We propose a contrastive learning-based method in this submission to the Causal News Corpus - Event Causality Shared Task 2023, with a specific focus on Subtask 1 centered on causal event classification. In our approach we pre-train our base model using Supervised Contrastive (SuperCon) learning. Subsequently, we fine-tune the pre-trained model for the specific task of causal event classification. Our experimentation demonstrates the effectiveness of our method, achieving a competitive performance, and securing the 2nd position on the leaderboard with an F1-Score of 84.36. © CASE 2023 - Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, associated with 14th international conference on Recent Advances in Natural Language processing, RANLP 2023.

关键词： Semantics

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A Survey of Sarcasm Detection Techniques in Natural Language processing 6

A Survey of Sarcasm Detection Techniques in Natural Language...

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6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Singh, Bhuvanesh Sharma, Dilip Kumar University of St. Thomas United States Gla University Mathura India

ISBN: (纸本)9798350346961

Sarcasm is a linguistic style that is often employed in regular conversation and that natural language processing (NLP) systems may find difficult to recognize. the use of sarcasm in social media, online reviews, and other digital communication has increased in recent years, making it essential for NLP systems to detect sarcasm accurately. In this survey, we provide an overview of the current state of the art in sarcasm detection using NLP techniques. We discuss the various approaches to detect sarcasm, including machine learning, deep learning, and lexicon-based methods. We also review recent research on sarcasm detection in various languages and contexts, such as social media, customer reviews, and online forums. We also identify opportunities for future study and address the difficulties and limits of the present sarcasm detection techniques. the overall goal of this survey is to further this field of study by providing a thorough grasp of sarcasm detection in NLP.. © 2023 IEEE.

关键词： Natural language processing systems

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Research on Optimizing Real-Time Data processing in High-Frequency Trading Algorithms using machine learning 6

Research on Optimizing Real-Time Data Processing in High-Fre...

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6th international conference on Intelligent Control, Measurement and signal processing, ICMSP 2024

作者： Fan, Yuxin Hu, Zhuohuan Fu, Lei Cheng, Yu Wang, Liyang Wang, Yuxiang University of Pennsylvania School of Engineering and Applied Science Toronto Canada San Francisco United States Changshu Institute of Technology School of Computer Science and Engineering Suzhou China Mclean VA22102 United States Washington University in St. Louis Olin Business School St. LouisMO63130 United States Institute of Technology School of Business Stevens New YorkNY10018 United States

ISBN: (纸本)9798331516550

High-frequency trading (HFT) represents a pivotal and intensely competitive domain within the financial markets. the velocity and accuracy of data processing exert a direct influence on profitability, underscoring the significance of this field. the objective of this work is to optimise the real-time processing of data in high-frequency trading algorithms. the dynamic feature selection mechanism is responsible for monitoring and analysing market data in real time through clustering and feature weight analysis, with the objective of automatically selecting the most relevant features. this process employs an adaptive feature extraction method, which enables the system to respond and adjust its feature set in a timely manner when the data input changes, thus ensuring the efficient utilisation of data. the lightweight neural networks are designed in a modular fashion, comprising fast convolutional layers and pruning techniques that facilitate the expeditious completion of data processing and output prediction. In contrast to conventional deep learning models, the neural network architecture has been specifically designed to minimise the number of parameters and computational complexity, thereby markedly reducing the inference time. the experimental results demonstrate that the model is capable of maintaining consistent performance in the context of varying market conditions, thereby illustrating its advantages in terms of processing speed and revenue enhancement. © 2024 IEEE.

关键词： Marketplaces

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AF-KNN: A machine learning-based Algorithm for Automatic Recognition of Logistics Numbering Images 6

AF-KNN: A Machine Learning-based Algorithm for Automatic Rec...

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6th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Yang, Lanqi Wang, Jianjun Chen, Ao Ma, Wenjia University of Chinese Academy of Sciences Institute of Cyberspace Security Beijing China China United Network Communications Co. Ltd. Heilongjiang Branch Harbin China Donghua University College of Information Science and Technology Shanghai China Tiangong University School of Electronics & Information Engineering Tianjin China

ISBN: (纸本)9798350305623

With the development of IoT intelligence and automation, the demand for efficient and accurate recognition of logistics numbering image processing is increasing. In response to the high computational complexity of IoT environments and the need for higher efficiency, precision, and real-time recognition, this paper proposes an adaptive ensemble algorithm called Adaptive Fusion-KNN, based on KNN. AF-KNN integrates two weak classifiers based on KNN and utilizes machine learning methods to adaptively train these weak classification models. It dynamically adjusts its own weight in the voting process based on the error rate from the previous round of training, thereby ensuring more reliable label prediction results from the strong classifier. the introduction of this algorithm provides a theoretical basis for efficient, accurate, and automated classification of data in IoT application scenarios. © 2023 IEEE.

关键词： automatic recognition of images IoT KNN logistics number machine learning

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