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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401-410 订阅

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Predicting Endotracheal Suctioning Activities Based On Skeletal Coordinates: A Novel Approach Combining Handcrafted Feature Engineering and Ensemble learning

Predicting Endotracheal Suctioning Activities Based On Skele...

引用

IEEE international conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Shadid Yousuf Fabliha Noshin Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Biomedical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Endotracheal Suctioning (ETS) is a critical procedure typically carried out to patients with artificial airways in intensive care units. It is an extremely delicate procedure requiring adequate expertise to minimize the risk of complication and ensure patient safety. Although numerous studies have applied machine learning techniques for the purpose of nurse-care activity recognition, little works focus specifically on endotracheal suctioning activity recognition. Utilizing a model proficient in categorizing endotracheal suctioning activities can serve as a valuable tool for evaluating nursing proficiency. In this paper we use skeletal coordinate data obtained from the 6th ABC Challenge (Activity Recognition of Nurse Training Activity) and apply machine learning techniques to predict activities associated with ETS. Following some data pre-processing steps including data smoothing, we extracted joint angles, velocity and several time-domain features and performed data augmentation. We split the data into training and test set and finally, we employed an ensemble model for classifying the ETS activities. the ensemble model yielded superior results than conventional machine learning approaches with an accuracy of 68% and a macro average f1-score of 71 %.

关键词： Training Accuracy Smoothing methods Medical services Activity recognition Writing signal processing Feature extraction Safety Time-domain analysis

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MAFT: An Image Super-Resolution Method Based on Mixed Attention and Feature Transfer 1

引用

6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint international conference on Web and Big Data (APWeb-WAIM)

作者： Liu, Xin Li, Jing Cui, Yuanning Zhu, Wei Qian, Luhong Nanjing Univ Aeronaut & Astronaut Coll Artificial Intelligence Coll Comp Sci & Technol Nanjing 211106 Peoples R China Nanjing Univ Nanjing 210023 Peoples R China Kunshan Huaheng Welding Co Ltd Suzhou 215300 Peoples R China

ISBN: (数字)9783031251986

ISBN: (纸本)9783031251979;9783031251986

Reference-based image super-resolution methods, which enhance the restoration of a low-resolution (LR) images by introducing an additional high-resolution (HR) reference image, have made rapid and remarkable progress in the field of image super-resolution in recent years. Most of the existing methods use an implicit correspondence matching approach to transfer HR features from the reference image (Ref) to the LR image. However, these methods lack the further judgment and processing of the HR features from Ref, which limits them in challenging cases. In this paper, We propose an image super-resolution method based on mixed attention and feature transfer (MAFT). First, we obtain the deep features of the LR and Ref images through the encoder network, then extract the transferred features from Ref through the attention network, and perform adaptive optimization processing on the features, and finally fuse the transferred features with LR features to achieve a high-quality image reconstruction. the quantitative and qualitative experiments on these benchmarks, i.e., CUFED5, Urban100 and Manga109, show that MAFT outperforms the state-of-the-art baselines with significant improvements.

关键词： Computer vision machine learning Super-resolution Attention mechanism

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Rotor Faults diagnosis in PMSMs Based on Branch Current Analysis and machine learning

Rotor Faults diagnosis in PMSMs Based on Branch Current Anal...

引用

China international Electrical and Energy conference (CIEEC)

作者： Yinquan Yu Haixi Gao School of Mechatronics and Vehicle Engineering East China Jiaotong University Nanchang China

To solve the problem that it is difficult to accurately identify the rotor eccentric fault, demagnetization fault and mixed fault of permanent magnet synchronous motor (PMSM) with slot pole ratio of 3/2 and several times of it, this paper proposes a method to identify the rotor fault based on the combination of branch current analysis and machine learning algorithm. First, in this paper, we select the stator branch current fluctuation in time domain without mutual cancellation as the feature of fault classification. Secondly, after signal preprocessing, feature factors are extracted and several fault feature factors with large differences are selected to construct feature vectors. Finally, genetic algorithm is used to optimize the parameters of support vector machine (SVM), and GA-SVM model is constructed as a classifier for multi fault classification of permanent magnet synchronous motor to classify these three types of faults. the fault classification results show that the proposed method using branch current signal combined with GA-SVM can be effectively diagnose faulty PMSM.

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machine Vision Applied to Backscattered Electron Imaging

Machine Vision Applied to Backscattered Electron Imaging

引用

Pattern Recognition and Artificial Intelligence (PRAI), international conference on

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

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.

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Radar Sensing via Geometric machine learning Over Riemannian Manifolds

Radar Sensing via Geometric Machine Learning Over Riemannian...

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

作者： Joarder Jafor Sadique Imtiaz Nasim Ahmed S. Ibrahim Department of Electrical and Electronic Engineering Florida International University Miami FL USA Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

the imperative for autonomously detecting radar signals is paramount in the context of emerging shared-spectrum wireless networks, such as the Citizens Broadband Radio Service (CBRS) band. the dynamic allocation of this spectrum hinges upon a specialized sensor network tasked with identifying the presence of federal incumbent radar signals. In this paper, we propose a radar sensing strategy using received signals at base stations. More specifically, the sample covariance matrices of received signals lie over Riemannian manifolds (i.e., curved surfaces) thanks to their symmetric positive definite (SPD) properties. Consequently, we propose to use support vector machine (SVM) learning models over Riemannian manifolds for classification of radar existence. Our findings reveal that the model attains more than 90% radar detection accuracy considering signal-to-noise ratio (SNR) values up to 14 dB.

关键词： Support vector machines Manifolds Measurement Base stations Radar detection Radar Radar antennas Sensors Kernel signal to noise ratio

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DSSS signal Jamming Method Based on DCGAN

DSSS Signal Jamming Method Based on DCGAN

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Jiangfei Ren Kui Xu Yang Liu Rui Lu Mi Zhang College of Communications Engineering Army Engineering University Nanjing China

With the continuous development of anti-jamming technology for the direct sequence spread spectrum (DSSS) communication, all existing efficient jamming methods have certain limitations. Aiming at this problem, a DSSS signal jamming method based on deep convolutional generative adversarial network (DCGAN) is proposed. Firstly, the correlation jamming model of the DSSS signal is studied, and the feasibility of the proposed method is qualitatively analyzed from the perspective of theoretical derivation. Secondly, based on the analysis of the principle of DCGAN, the generative adversarial network model is designed, and the optimized learning rate generates the jamming signal with highly similar time-frequency, frequency and correlation domain characteristics as that of the DSSS signal. Finally, the jamming effect of the proposed method is quantitatively analyzed through simulation experiments and comparison with other four jamming methods. the experimental results show that the proposed method is more threatening to the DSSS signal, and the jamming performance is significantly better than the traditional suppression jamming, slightly inferior to the best correlation jamming, but the implementation difficulty is much lower than the correlation jamming.

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Implementation of Burmese Language News Classification System by Using SVM and LSTM machine learning Algorithm

Implementation of Burmese Language News Classification Syste...

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IEEE international conference on Computer and Communication Engineering Technology (CCET)

作者： Kyaw Zaw Ye Ye Myint Naing Ye Naung Khant Phyo Nyein thant Zaw Lin Science and Technology Research Center Pyin Oo Lwin Myanmar

With the increasing availability of documents in social media and the rapid growth in the number of data exchange systems, the task of automatically categorizing documents has become a key method for organizing information and knowledge retrieval. News classification is the process of sorting text documents into one or more predefined categories or classes of similar documents. the correct classification of electronic documents, online News, blogs, electronic letters and digital libraries requires intellectual analysis of the text, the use of machine learning and methods of a natural language processing to obtain significant information. the article aims to analyze the methods and methodologies that are used in the classification of text documents. the purpose of the work is to classify news in social media that need to be organized and retrieved information using machine learning methods. In this paper, Experiments were done on Myanmar local and international news dataset using TF-IDF feature extraction techniques and the results are compared with performance of Support Vector machine (SVM) machine learning algorithm and long short-term memory (LSTM) recurrent neural network.

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

Malicious network traffic detection method based on traffic ...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

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

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.

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IoT Enabled Health Monitoring System using machine learning Algorithm 6

IoT Enabled Health Monitoring System using Machine Learning ...

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6th international conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sheik, Mohammed.S Sheela, T. Muthumanickam, T. Vinayaka Mission's Kriupananda Variyar Engineering College Vinayaka Mission's Research Foundation - Deemed to Be University Dept of Ece Salem India

ISBN: (纸本)9781665482714

Now-a-davs Internet of things (IoT) is used in various real-time applications, Including smart health monitoring. the existing health monitoring system can only collect the basic information about heat. heartbeat. and BP (Blood Pressure). this research study proposes an effective examination of patient's brain signals and detect the health status of the patient in real time. the main objective of the proposed study is to provide a proper optimized value about the mentally challenged patients by collecting the data information from brain signals with 24 channels and study the body parameters through each EEG (Electroencephalography) signal channel. Here, the collected data is pre-processed by using machine Learninz (ML) tools and Neural Networks (NN) with Python programming language, By collecting the data information from brain signals with EEG sensors, an optimized value and solution can be provided to the patients suffering from Cerebral Palsy (CP). the collected data is then stored in a cloud storage platform and it can be accessed from any remote location. the stored data is then collected and filtered by using PCA techniques and further the Artifact siznals (Noise) are removed to diagnose seizures by Identifying brain signal parameters (Alpha, Beta, Delta and theta). Further, a novel model has been designed by using python programming languaze for training the machine with a maximum number of datasets in order to check accuracy and predict the seizure levels of any CP patient. Neural Network (NN) algorithms were applied here by using python programming language in order to check the percentage error in the data processing mechanism. Once the data is analyzed with the proposed model it suggests the CP patient for Tentative Treatment. © 2022 IEEE.

关键词： Electroencephalography

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A Comparative Analysis of Early Stage Diabetes Prediction using machine learning and Deep learning Approach 6

A Comparative Analysis of Early Stage Diabetes Prediction us...

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Refat, Md Abu Rumman Amin, Md. Al Kaushal, Chetna Yeasmin, Mst Nilufa Islam, Md Khairul Islamic University Information Communication Technology Kushtia Bangladesh Prime University Computer Science And Engineering Dhaka Bangladesh Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9781665425520

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. the common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms, indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0%, while the rest of the algorithms were over 90.0% accurate. © 2021 IEEE.

关键词： Forecasting

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