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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 777 条记录，以下是121-130 订阅

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Facial Expression Recognition in the Wild: Dataset Configurations 5

Facial Expression Recognition in the Wild: Dataset Configura...

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IEEE 5th international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Galea, Nathan Seychell, Dylan Univ Malta Dept Artificial Intelligence Msida Malta

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

Facial Expression Recognition (FER) in the wild has become an increasingly significant and focused area within computer vision, with many studies tackling different aspects to improve its recognition accuracy. this paper utilizes RAF-DB and AffectNet as the two leading datasets in the scene and compares the different experimental dataset configurations to state-of-the-art techniques referred to as Amend Representation Module (ARM) and Self-Cure Network (SCN). the paper demonstrates how different dataset configurations should be the main focal point of improving the FER task and how there cannot be significant improvements in the FER task with a lack of a favorable dataset.

关键词： machine learning Computer Vision Facial Expression Recognition

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Exploring Digital signal processing for Electromagnetic Compatibility in Network Applications 5th

Exploring Digital Signal Processing for Electromagnetic Comp...

引用

5th international conference on Data Science, machine learning and Applications, ICDSMLA 2023

作者： Chauhan, Aakriti Kapse, Vinod Mansiram Karthikeyan, M.P. Pandeya, Megha Department of Agri-Business Management Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

this paper explores the software of virtual sign processing (DSP) strategies to characterize and examine the effects of electromagnetic compatibility (EMC) in network packages. the paper discusses the fundamentals of EMC and then presents information of the basics of DSP. FM modulation and demodulation based totally communication structures, a spurious unfastened dynamic range generator, and antenna radiation field’s measurement gadget are developed to look at the consequences of EMC. the performance of the advanced structures is compared towards the expected results in order to assess the effectiveness of the approach. the paper then discusses the demanding situations and opportunities associated with the use of DSP for EMC research. the results of the paintings are discussed and guidelines for future studies are diagnosed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Demodulation

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Beyond Natural Language processing: Building Knowledge Graphs to Assist Scientists Understand COVID-19 Concepts 5

Beyond Natural Language Processing: Building Knowledge Graph...

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5th international conference on machine learning and Natural Language processing (MLNLP)

作者： Yu, Yishu Dana Hall Sch Wellesley MA 02482 USA

ISBN: (纸本)9781450399067

To combat COVID-19, scientists must digest the vast amount of relevant biomedical knowledge in the literature to understand disease mechanisms and related biological functions. Nearly 3,000 scientific papers are published on PubMed every day. this knowledge bottleneck has resulted in severe delays in developing COVID-19 vaccines and drugs. Our research produces a hierarchy of knowledge concepts related to COVID-19, designed to assist scientists in answering questions and generating summaries. It aims to discover scientific and comprehensive knowledge to extract fine-grained multimedia elements (i.e., physical and visual structures, relational events and events, and chemical knowledge). Our project is toward one step in natural language understanding: detailed contextual sentences, subgraphs, and knowledge subgraphs are the first time to be automatically generated, and relations and coreferences of COVID-19 mentions will be sketched. Extensive results show that our method outperforms other state-of-the-art methods. In addition, we have published the generated knowledge graph on Google Drive1 and released the source in the Github2.

关键词： COVID-19 Natural Language processing Knowledge Graphs

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High-Resolution Imaging from Gapped Data Based on Fast Sparse Bayesian learning 5

High-Resolution Imaging from Gapped Data Based on Fast Spars...

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5th international conference on Information Communication and signal processing, ICICSP 2022

作者： Wang, Yuanyuan Fu, Haosheng Dai, Fengzhou Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9781665485890

High-resolution imaging from gapped data has become a research hotspot in radar imaging field. Among many imaging algorithms, sparse Bayesian learning (SBL) is more robust and has greater estimation accuracy, which attracts active interest from researchers. Unfortunately, the inversion and multiplying operations are involved in each iteration of SBL lead to heavy computational complexity when they are implemented directly. In this paper, we propose a fast Fourier dictionary (FD)-based SBL algorithm to solve high-resolution imaging from gapped data, greatly reducing the calculation cost. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： learning algorithms

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Advances in Electronic Health Record Analysis for Diabetes Risk Prediction: A machine learning-Enhanced Framework 5

Advances in Electronic Health Record Analysis for Diabetes R...

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5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Ye, Bingqing Shen, Zhangyi Ji, Aohan Jiao, Yu Niu, Yunfei Zuo, Kaizhong Hu, Peng Li, Wenjie School of Computer and Information Anhui Normal University Wuhu China

ISBN: (纸本)9798331528911

Background: Diabetes mellitus is a prevalent chronic condition with escalating global incidence, underscoring the urgency for efficient early detection and management strategies to ameliorate healthcare burdens and enhance patient quality of life. Methods: We conducted a systematic data processing pipeline on electronic health examination data, which included de-identification, imputation of missing data, and feature scaling. Feature selection was performed using Recursive Feature Elimination (RFE) complemented by a literature review to identify the most informative predictors. We developed five machine learning models: Logistic Regression (LR), Support Vector machine (SVM), Random Forest (RF), XGBoost, and LightGBM (LGBM). Model performance was quantitatively assessed using a comprehensive set of metrics: accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). the LGBM model, which demonstrated the highest predictive accuracy, was subjected to hyperparameter optimization and cross-validated on an independent public dataset to ensure its generalizability. Results: the initial evaluation of the LGBM model yielded an accuracy of 90.52%, precision of 89.06%, recall of 88.97%, F1-score of 88.67%, and an AUC-ROC of 0.965. After hyperparameter tuning, these metrics were further improved to 91.38% accuracy, 89.87% precision, 90.13% recall, 89.94% F1-score, and an AUC-ROC of 0.969. the model's predictive capabilities were corroborated through successful cross-validation on external datasets, indicating its robustness and reliability. Conclusion: the study presents a sophisticated diabetes prediction model leveraging machine learning algorithms on electronic health examination data. the model's superior predictive performance and validated generalizability suggest its potential as a valuable asset for healthcare providers, aiding in the early identification of diabetes risk, thereby facilitating more informed clinical

关键词： Electronic health record

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Off-Grid Error Calibration for Single Snapshot DOA Estimation Based on Sparse Bayesian learning 5

Off-Grid Error Calibration for Single Snapshot DOA Estimatio...

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5th international conference on Information Communication and signal processing, ICICSP 2022

作者： Fu, Haosheng Wang, Yuanyuan Dai, Fengzhou Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9781665485890

In this work, the grid mismatch problem for direction of arrival (DOA) estimation of array is studied. To solve the grid mismatch impact on the DOA estimation accuracy, a novel off-grid model for DOA estimation using sinc interpolation is proposed in this paper. Specifically, the off-grid error is represented as parameter to be estimated in the observation model. Under the framework of sparse Bayesian learning (SBL), the off-grid error is estimated via the variational Bayesian expectation maximization (VBEM) and eliminated by updating the grids with sinc interpolation. the simulation results demonstrate superior performance of the proposed method over state-of-the-art methods are reported, as well. © 2022 IEEE.

关键词： Direction of arrival

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Melanoma Classification Using Feature Extraction Methods and machine learning Approaches 2

Melanoma Classification Using Feature Extraction Methods and...

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2nd international conference on signal processing and machine learning, CONF-spml 2022

作者： He, Sihui Huang, Zheyang Zhong, Xinjie Faculty of Science Western University LondonN6A 37K Canada Faculty of Engineering The University of Sydney SydneyNSW2006 Australia School of Electrical Engineering Zhejiang University Hangzhou30058 China

Due the unbalanced melanoma data and the complexity and resolution of the melanoma image backgrounds, classification of the melanoma regions is very challenging. In this paper, EffNet B5 models with different augmentation methods and machine learning models that use feature vectors from feature extraction method are proposed to solve the classification task. GLCM, LBP, SFTA, and ResNet18 are chosen as the feature extraction methods in this study, while SVM, Random Forest, and XGBoost are chosen to be the machine learning classification models. In this work, the most effective feature extraction algorithm and the classification algorithm with the best AUC scores are determined. © 2022 CEUR-WS. All rights reserved.

关键词： Classification Feature Extraction machine learning Melanoma

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What's the Key Environment Information that Makes the Blockage Prediction Work? 5

What's the Key Environment Information that Makes the Blocka...

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5th international conference on Electrical, Communication and Computer Engineering, ICECCE 2024

作者： Sun, Yutong Nan, Zuoyong Zhong, Zhigang Shen, Tao Ban, Rui China Information Technology Designing & Consulting Institute Co. Ltd. Software Development Center Beijing China

ISBN: (纸本)9798331529437

To meet the increased communication performance requirements defined in sixth-generation (6G) networks, out-of-band methods are gaining more attention from researchers. these methods consider adding extra information, such as sensing environmental data, to enhance performance. In this context, image-based physical environment blockage prediction methods have been proposed to avoid fast signal attenuation, particularly in millimeter wave (mmWave) bands. However, image data is high-dimensional and requires specialized hardware for fast processing. this paper proposes a real-time blockage prediction method that utilizes images containing only propagation-related foreground information. the method predicts two blockage states using a convolution-based machine learning (ML) approach. We evaluate the proposed method using real-world communication data from a vehicle-to-everything (V2X) scenario. A comparative experiment is conducted to compare the performance of using complete images versus foreground-only images. the neural net-work's max pooling layer is configured with different parameters to test and analyze the impact of non-foreground information. the results show that the foreground-only image-based prediction is more robust for detail extraction and requires less prediction time. As the pooling stride increases, the accuracy of the complete image-based prediction method decreases, while the proposed method maintains high accuracy. Moreover, the proposed can save about 30 % of the prediction time. © 2024 IEEE.

关键词： Blockage Prediction Environment Sensing Foreground Image machine learning mm Wave Transmission

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Hybrid Quantum-Based machine learning Algorithm for Amyotrophic Lateral Sclerosis Detection Using EMG signals 5th

Hybrid Quantum-Based Machine Learning Algorithm for Amyotrop...

引用

5th international conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Makam, Kiran Kumar Agarwal, Nisha Bhadauriya MCTE Mhow Indore India S D Bansal College of Technology Indore India

ISBN: (纸本)9789819791118

An electromyogram (EMG) is the most widely used diagnostic tool for Amyotrophic Lateral Sclerosis (ALS). these signals are employed to distinguish between different neuromuscular exertion levels that are indicative of both normal and aberrant circumstances. the use of machine learning in this work is predicated on the development of an efficient and non-invasive Hybrid Quantum machine learning (H-QML) algorithm for the diagnosis of ALS disease. In this work, we introduce Hybrid-QML employing a Quanvolutional layer, which works on the input data by performing similar transformations as random convolutional filter layers, but locally on the data using multiple random quantum circuits. We provide a Hybrid Quantum machine learning method to diagnose amyotrophic lateral sclerosis (ALS) using electromyography (EMG) data from the biceps brachii muscles. the results indicate that when compared to the purely classical machine learning approach, the hybrid QML model has faster training, reduced loss, and higher test set accuracy. In order to do automated screening for ALS disease using signal processing, basic analysis and classification of EMG data from both healthy and ALS participants were recorded. Neural network classifiers meticulously analyze and categorize specific aspects of EMG data, like the mean and maximum amplitude, in order to automate the diagnosis of ALS disease. the EMG dataset was used for the experiments, and results showed that of the classifiers assessed, the ensemble decision tree performed significantly better, with an accuracy of 98.34%, followed by hybrid quantum machine learning with a 98.38% accuracy. the results of the experiment demonstrate that there is enough variation between normal EMG signals and ALS signals to enable automated screening. Since this approach uses small quantum circuits with minimal to no error correction, it may find practical applications in near-term quantum computing. Hybrid quantum filters maximize both loss and precisio

关键词： Diagnosis

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A Tool Platform for Predicting the Quality of Production Based on machine learning Technology 5

A Tool Platform for Predicting the Quality of Production Bas...

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5th IEEE international conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Ding, Xin Zhou, Hanqing Lv, Haixiong China Aero-Polytechnology Establishment Beijing China

ISBN: (纸本)9781665499132

In order to meet the needs of China to improve quality of manufacturing by using information technology, we developed a user-friendly tool platform of quality prediction system that is and does not require a high level of knowledge related to machine learning. Users can pre-process and model the data generated in the general production process and then perform quality prediction. the tool platform includes six modules: data import, data pre-processing, data modeling, data prediction, root cause diagnosis and system management;it provides a variety of classification and regression algorithms such as multiple linear regression, SVR algorithm, KNN algorithm, decision tree algorithm, random forest algorithm, SVM algorithm, neural network algorithm, etc. © 2022 IEEE.

关键词： Manufacture

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