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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023"

共 147 条记录，以下是31-40 订阅

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Impact of EMG signal Filters on machine learning Model Training: A Comparison with Clustering on Raw signal 3rd

Impact of EMG Signal Filters on Machine Learning Model Train...

引用

3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Barbosa, Ana Ferreira, Edilson Grilo, Vinicius Mattos, Laercio Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Ctr Fed Educ Tecnol Minas Gerais CEFET MG Belo Horizonte MG Brazil Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Reg Monta P-5300253 Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

Our current society faces challenges in integrating individuals with disabilities, making this process difficult and painful. People with disabilities (PwD) are often mistakenly considered incapable due to the difficulties they face in daily tasks due to the lack of adapted means and tools. In this context, assistive technologies play a crucial role in improving the quality of life for these individuals. However, assistive technologies still have various limitations, making research in this area essential to enhance existing solutions and develop new approaches that meet individual needs, aiming to promote inclusion and equal opportunities. This paper presents a research project that focuses on the study of electromyography (EMG) signal processing generated by individuals who have undergone amputations. These signals are essential in assistive technologies, such as myoelectric prostheses. The study focuses on the impact of different filters and machine learning training methods on this processing. The results of this study have the potential to provide relevant findings for the development of more efficient assistive technologies. By understanding the processing of EMG signals and applying machine learning techniques, it is possible to improve the accuracy and response speed of prosthetics, increasing the functionality and naturalness of movements performed by users, as well as paving the way for the emergence of new technologies.

关键词： Assistive technologies Electromyography (EMG) signal processing machine learning

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Shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform 3rd

Shaft Rotation Monitoring Using Radar Signal Processing and ...

引用

3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

作者： Valuyskiy, Denis Vityazev, Sergey Vityazev, Vladimir Ryazan State Radio Engineering University Gagarina. 59/1 Ryazan390005 Russia

ISBN: (纸本)9789819743582

This article discusses the problem of shaft rotation control for continuous testing of industrial equipment using a radar sensor. The FMCW radar with a frequency of 77 Hz is used to irradiate a rotating shaft and obtain information about the uniformity of its rotation. A test bench has been developed and radar reflections are recorded. Then the real signals are analyzed and an approach to signal processing and decision-making rules are proposed. The approach is based on wavelet transform and neural classification of rotating shifts with deep learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radar signal processing

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An Underwater Acoustic Channel Equalization Algorithm Based on RBF Neural Network 3

An Underwater Acoustic Channel Equalization Algorithm Based ...

引用

3rd international conference on Intelligent Communications and Computing, ICC 2023

作者： Zhang, Zhixuan Lv, Tingting Zhou, Manli Jin, Weibo Wang, Yang Ocean University of China Qingdao China

ISBN: (纸本)9798350308327

With the increasing demand for the performance of communication transmission system, new requirements are put forward for channel equalization technology. There are serious multipath interference and Doppler phenomena in the underwater acoustic channel. In view of this phenomenon, equalization technology is proposed to suppress it. The application of machine learning in image processing, machine vision, communication and other fields has achieved remarkable results. Based on this, this paper proposes a channel equalization algorithm which combines intelligent optimization algorithm and machine learning network. This algorithm utilizes the PSO (Particle Swarm Optimization) algorithm to optimize the RBF (Radial Basis Function) neural network and adjust its parameters for signal equalization under the underwater acoustic channel. The algorithm takes a QPSK (Quadrature Phase-Shift Keying) signal transmitted through the underwater acoustic channel as input to the network. During the adjustment of network parameters, the PSO algorithm is employed with a population size equal to the number of parameters requiring optimization. Simulation results have demonstrated that using this algorithm for equalization outperforms using a simple neural network. The constellation is more concentrated, and the error rate is reduced by approximately 5 %. © 2023 IEEE.

关键词： Image processing

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ENSEMBLE METHODS FOR ENHANCED COVID-19 CT SCAN SEVERITY ANALYSIS

ENSEMBLE METHODS FOR ENHANCED COVID-19 CT SCAN SEVERITY ANAL...

引用

IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Thyagachandran, Anand Murthy, Hema A. Indian Inst Technol Madras Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9798350302615

Computed Tomography (CT) scans provide a high-resolution image of the lungs, allowing clinicians to identify the severity of infections in COVID-19 patients. This paper presents a domain knowledge-based pipeline for extracting infection regions from COVID-19 patients using a combination of image-processing algorithms and a pre-trained UNET model. Then, an infection rate-based feature vector is generated for each CT scan. The infection severity is then classified into four categories using an ensemble of three machine-learning models: Random Forest, Support Vector machines, and Extremely Randomized Trees. The proposed system is evaluated on the validation and test datasets with a macro F1 score of 58% and 46.31%, respectively. Our proposed model has achieved 3rd place in the severity detection challenge as part of the IEEE ICASSP 2023: AI-enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition (AI-MIACOV19D). The implementation of the proposed system is available at https://***/aanandt/***

关键词： COVID-19 CT-Scans Infection Segmentation machine learning Methods Severity Analysis

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Unified Parsing Script using machine learning 3

Unified Parsing Script using Machine Learning

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Sudha Sadasivam, G. Barath Kumar, G. Tarun Visva, R. Mukesh, S. Shashaank, R. Muley, Preeti PSG College of Technology Computer Science and Engineering Coimbatore India Ugam Solutions Coimbatore India

ISBN: (纸本)9798350320749

Gathering noiseless data is an important phase in fields such as machine learning and target advertising and is also one of the most tedious processes. The evolution of the Internet has made it the largest source of data, in every domain. Web scraping can be defined as the process of extracting and parsing information from unstructured web pages. Advancement in web scraping has provided us with tools such as BeautifulSoup, lxml, and Selenium, which precisely extract information from webpages based on the knowledge of the HTML document to be parsed. Nevertheless, one major inconvenience associated with such primitive web scrapers is that they are site-dependent. They have to be adapted for each new website. In this paper, we propose a machine learning based approach that searches for patterns in the semantics of the webpages of a single domain, capable of acting as a site-independent web scraper. The strategy proposed involves an algorithm that utilizes the results of three independent machine learning models to extract key information from web pages belonging to a specific domain. The experimental analysis of the simulation demonstrates that the solution proposed enhances the process of web scraping by enabling a single web scraper to extract data accurately from multiple websites, thus improving productivity. © 2023 IEEE.

关键词： Websites

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Optimized SVM based on improved whale algorithm for EEG signal pattern classification 3

Optimized SVM based on improved whale algorithm for EEG sign...

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Peng, Junqiang Liu, Lijun Tiangong University Tianjin China

ISBN: (纸本)9781665472784

In order to improve the classification accuracy of moving image electroencephalogram ( EEG ) signals, aiming at the problem that the traditional support vector machine (SVM ) parameter value is not accurate, which leads to poor classification model, this study introduces Sobol sequence, reverse learning strategy and piecewise nonlinear time-varying factor. In order to improve the random layout strategy of boundary processing, Levy flight strategy improves the traditional whale algorithm, combines the improved whale algorithm with SVM, and compares the combined SVM classifier with other classifiers for EEG signal classification. The results show that the accuracy of the optimization algorithm is 2 ∼ 4 percentage points. © 2023 IEEE.

关键词： Electroencephalography

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Analysis of the Relationship Between COVID-19 and Diet Based on machine learning Algorithm 3

Analysis of the Relationship Between COVID-19 and Diet Based...

引用

3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Jie, Hao Yan, Li Yong, Liu School of Electrical Engineering Northwest Minzu University Gansu Engineering Research Center for Eco-Environmental Intelligent Networking Lanzhou China

ISBN: (纸本)9798350307023

In this paper, the data set of food and nutrition intake from the Food and Agriculture Organization of the United Nations (FAO) and COVID-19 case counts from the World Health Organization (WHO) are used to analyze the relationship between diet and COVID-19 infections in different countries. Different machine learning algorithms, like regression, clustering, and classification, are used to process and analyze the data set. In addition, an improved random forest method for feature selection is proposed. This paper has studied the relationship between food intake and obesity or undernourished, the association between the amount of food consumed and the amount of fat, protein, and calories consumed through that food, and the impact on the confirmed, deaths and recovery rate of COVID-19. The purpose is trying to find some foods that can prevent or assist in the treatment of COVID-19, and achieve a non-pharmaceutical intervention for COVID-19 through a healthy diet. © 2023 IEEE.

关键词： COVID-19

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VAG signal Classification Using Time Domain Statistical Features and machine learning 3

VAG Signal Classification Using Time Domain Statistical Feat...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Patankar, Sanika Durge, Gaurav Joshi, Ashutosh Jaid, Atharva Kalambe, Khomesh Dhale, Hrishikesh Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Maharashtra Pune India

ISBN: (纸本)9798350317022

This research paper presents a novel approach for the classification of vibroarthrographic (VAG) signals to aid in the early diagnosis of knee arthritis, a disease whose prevalence is increasing worldwide. VAG signals are non-invasive and cost-effective, making them a promising tool for screening and detection. In this study, a time-domain statistical feature approach is employed to extract relevant features from VAG signals. Analytical measures like Kurtosis, skewness, Standard Deviation have been used in this work. These features are then fed into machine learning algorithms for the automatic detection of knee arthritis. The classification accuracy of 2 algorithms has been compared in this work, namely SVM and Random Forest. The latter resulted in an overall accuracy of 89% and an AUROC of. 90. The results indicate the potential of this approach to provide an accurate and reliable means for early knee arthritis detection, which would allow the patient to be treated ahead of time. © 2023 IEEE.

关键词： Classification Knee arthritis machine learning signal processing Time-domain statistical features Vibroarthrography

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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network 3

Research on Intelligent Auxiliary Review of Power Grid 3D De...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Fan, Dongmei Yan, Wendou Liang, Yi Cheng, Zhi Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou102200 China

ISBN: (纸本)9798350319125

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid. © 2023 IEEE.

关键词： 3D model based on BP neural network computer Grid design power system

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Using machine learning Regression Model to Predict the Optimum Election Algorithm for Parallel and Distributed Computing Systems 3

Using Machine Learning Regression Model to Predict the Optim...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Dhariwal, Naman Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9798350370867

Parallel and distributed computing has become a necessity when it comes to high-performance computing, artificial intelligence, big data analytics, machine learning, deep learning, signal processing and bioengineering. System engineers connect distributed systems, with large number of nodes, that then compute the solution for a problem concurrently. The distributed system has a leader that manages the overall coordination of these connected nodes. This research provides a novel approach to predict the most efficient and time-feasible election algorithm, that must be ran on a distributed system, using linear regression machine learning approach. This research implements the prediction process for three election algorithms and successfully predicts the most efficient and least time-consuming algorithm with 94.98% accuracy. Initially, the dataset for the regression model training was created by executing the three election algorithms parallelly using MPI running 16 parallel processors (cores). The research also presents three regression line equations, trained and modelled on this dataset, that can be used to predict the execution time for the three algorithms for a system with any number of nodes. The results are duly visualized, and regression lines are plotted for comparison. © 2023 IEEE.

关键词： Forecasting

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