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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3332 条记录，以下是81-90 订阅

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Assessment and Prediction of Corrosion Rate of Marine Railway Bridges Based on Ridge Regression Model 4

Assessment and Prediction of Corrosion Rate of Marine Railwa...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Wang, Huan Chen, Zhengbin Rail Transit Branch of Sinohydro 7th Engineering Bureau Co. LTD Chengdu China

ISBN: (纸本)9798350324303

this investigation employs ridge regression analysis to assess and forecast the corrosion rate of maritime railway bridges. We have gathered and organized pertinent data, devising a regression model encompassing multiple attributes, intending to scrutinize seawater's influence on railway bridge corrosion. the model's performance is appraised using cross-validation techniques, and the significance of each point is measured via ridge regression coefficients. Experimental findings demonstrate that the ridge regression model excels in predicting the corrosion rate of maritime railway bridges, exhibiting minimal discrepancies between predicted and actual values. Upon analysis of the model's attributes, we discern that salinity and sulfide content in seawater are critical determinants of railway bridge corrosion rates. the findings of this study provide valuable reference points for marine engineering and railway bridge construction while offering novel insights into the application of ridge regression models for predicting and analyzing maritime environmental issues. © 2023 IEEE.

关键词： Corrosion rate

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Study on Wind Forecasting for Beijing 2022 Winter Olympic Games Based on Unsupervised machine learning 4

Study on Wind Forecasting for Beijing 2022 Winter Olympic Ga...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Liu, Haoye Duan, Yuhui Li, Jiangbo Li, Tongtong Qinhuangdao Meteorological Bureau Hebei Qinhuangdao China Hebei Meteorological Observatory ShijiazhuangHebei China

ISBN: (纸本)9798350324303

the accurate prediction of wind speed is an important part of the weather forecasting service for the Winter Olympic Games. the Zhangjiakou zone of the Beijing 2022 Winter Olympic Games is located in the complex terrain of the Dama Mountains, and the current numerical forecasts have large deviations in forecasting wind speed in small-scale complex terrain. Based on the unsupervised machine learning method, this paper performs historical modeling on the basis of five element forecasts of two numerical forecasts for nine stations in the Zhangjiakou Zone of the Beijing 2022 Winter Olympic Games, thus achieving objective correction to the numerical forecasts of average winds and extreme winds. the results show that the unsupervised machine learning-based winds forecasts during the Beijing 2022 Winter Olympics Test Games are better, with significant improvements over the original numerical forecasts in terms of mean absolute error and mean squared error. © 2023 IEEE.

关键词： Weather forecasting

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MCDPS: Enhancing Clinical Decision Support for Multiple Chronic Disease Prediction Systems Using Ensemble machine learning Approaches 4th

MCDPS: Enhancing Clinical Decision Support for Multiple Chro...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Kumar, Kanak Verma, Anshul Verma, Pradeepika Banaras Hindu Univ Dept Comp Sci Varanasi 221005 Uttar Pradesh India Indian Inst Technol Technol Innovat Hub Patna 801103 Bihar India

ISBN: (纸本)9783031821554;9783031821561

People today suffer from various chronic diseases due to lifestyle choices and environmental conditions. Early prediction of these diseases is crucial to prevent them from worsening. However, it is often challenging for physicians to accurately diagnose these issues independently. A computational model based on big data analytics has numerous applications in the medical field. Advancements in machine learning (ML) and information technology have enabled more accurate recognition of diseases, health emergencies, and conditions. the advent of predictive algorithms has transformed disease diagnosis in the medical field. Previously, patient data records were meticulously examined to predict diseases and develop advanced models for trend analysis. However, with the rise of data analytics-driven intelligence, the evaluation of disease symptoms has become more accurate and efficient. these technologies enable the creation of predictive models capable of analyzing vast amounts of patient data to identify hidden patterns, aiding in early disease detection and diagnosis. this study explores the development of an ensemble ML-based multiple chronic disease prediction system (MCDPS) focused on three major diseases: chronic kidney disease (CKD), liver disease, and heart disease. the system utilizes ensemble ML models, including Decision Tree (DT), Gradient Boosting Classifier (GBC), Extreme Gradient Boosting (XGBoost), and Extra-Trees classifiers (ETC), to predict diseases. Its user-friendly and straightforward design makes it accessible to beginners. the system's performance in predicting the three chronic diseases has been evaluated and compared with other traditional models. the results show that the DT algorithm achieved an accuracy of 99%, 85%, and 83% for predicting CKD, heart disease, and liver disease, respectively.

关键词： Multiple Chronic Disease Prediction System machine learning Decision Tree Gradient Boosting Classifier Extreme Gradient Boosting Extra-Trees classifiers

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2023

4th International Conference on Recent Trends in Machine Lea...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2023

ISBN: (纸本)9789819994410

the proceedings contain 69 papers. the special focus in this conference is on Recent Trends in machine learning. the topics include: Implementation of Dual-Band Dielectric Resonator Antenna for 5G Applications;defect Detection in Metal Surfaces Using Computer Vision;liver Cirrhosis Prediction Using machine learning Classification Techniques;a Recent Survey on Risk Factors Affecting the Blood Pressure in India;Real-Time Monitoring System for Breakdown Analysis and OEE in the Wire Drawing Industry;tooth Sensitivity Device—Detection and Diagnosing of Sensitivity in the Dental Pulp;recognition of Skin Cancer;ioT-Based Smart Street Lighting Surveillance System;rock Segmentation of Real Martian Scenes Using Dual Attention Mechanism-Based U-Net;IAAS: IoT-Based Automatic Attendance System with Photo Face recognition in Smart Campus;hardware Implementation of Moving Object Detection Using Background Subtraction Algorithm;crime pattern Identification and Prediction Using machine learning;IMICE: An Improved Missing data Imputation Using machine learning;analyzing Students’ Opinion on E-learning—Indian Students’ Perspective;rash Driving Detection and Alerting System;Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images;Comparative Study of CNNs for Camouflaged Object Detection;3D Avatar Reconstruction Using Multi-level Pixel-Aligned Implicit Function;Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded Systems;defogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss;Text-to-Image Generation Model with DNN Architecture and Computer Vision for Embedded Devices Using Quantization Technique;one-Shot learning for Archaeological Site data Using Deep Neural Network on Embedded Systems;enhanceNet: A Deep Neural Network for Low-Light Image Enhancement with Image Restoration;intelligent Prediction of Cardiac Abnormality.

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Building 2D skeletal animation for body control in unity with machine learning 4

Building 2D skeletal animation for body control in unity wit...

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4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Li, Ran Qi, Jiangyuan School of Art and Design Changjiang Polytechnic China

ISBN: (纸本)9781510682528

machine learning has emerged as a popular technique for generating realistic animations in the video game industry. this paper aims to explore the application of machine learning in creating 2D skeletal animation for body control in Unity. the goal is to develop a model that can accurately predict the movements of the character's skeleton based on input parameters. To achieve this, the approach involves collecting a dataset of motion capture data, selecting an appropriate machine learning algorithm, creating an animation environment in Unity, training the machine learning model, and testing its accuracy. Each step is described in detail, along with an evaluation of the effectiveness of the method. Overall, the paper presents a promising approach to improve the efficiency and realism of 2D skeletal animation in video games. © 2024 SPIE.

关键词： Animation

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Student Performance Prediction Using Stacking Ensemble learning 4

Student Performance Prediction Using Stacking Ensemble Learn...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Tao, Yibo Yang, Yong Qu, Fuheng Liu, Yuyao Changchun University of Science and Technology School of Computer Science and Technology Changchun China

ISBN: (纸本)9798331534622

Student grade prediction is an important research topic in educational data mining. To address the issues of low prediction accuracy and poor generalization encountered by single models in predicting student grades, this paper proposes a Stacking ensemble learning model that integrates multiple individual models. the proposed model uses Support Vector machine (SVM), Decision Tree, and Random Forest as base learners, with Logistic Regression (LR) serving as the metalearner, forming a two-layer Stacking ensemble learning framework. the results demonstrate that the Stacking ensemble learning model achieves an accuracy of 91.6%, precision of 92.1%, recall of 93.8%, and an F1-score of 93.0%. these performance metrics are significantly better than those of individual learning models, indicating that the proposed Stacking model can effectively predict students' academic performance. © 2024 IEEE.

关键词： Contrastive learning

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Electronic Music Signal Identification Model Based on machine learning Algorithm 4

Electronic Music Signal Identification Model Based on Machin...

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4th international conference for Emerging Technology, INCET 2023

作者： Dongfang, Li University of Jinan School of Music Jinan250002 China

ISBN: (纸本)9798350335750

the main idea of this model is to recognize sound signals and classify them into musical instruments. the working principle of the model is that different sounds have different characteristics and can be used for classification. the algorithm is based on machine learning algorithm, which can learn from data and improve performance over time. the concept of the model is to automatically identify music signals so that they can be used as inputs to other models. the model is based on machine learning algorithm and uses artificial neural network (ANN) and machine learning technology. Artificial neural network is a computer algorithm that simulates the working mode of human brain neurons. machine learning algorithms are more complex than artificial neural networks, which use different types of layers to process data and learn from it. they have been successfully applied in many fields, such as image recognition, speech recognition, text classification and so on. © 2023 IEEE.

关键词： Music

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machine learning Approach for Detection of Speech Emotions for RAVDESS Audio dataset 4

Machine Learning Approach for Detection of Speech Emotions f...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rochlani, Yogesh R. Raut, Anjali B. H. V. P. m'S College of Engineering and Technology Department of Computer Science and Engineering Maharashtra Amravati India

ISBN: (纸本)9798350343670

the most effective technique to convey one's thoughts and actions to another is through the use of emotion. Emotion recognition from one's own voice is the most urgently needed technology right now. Emotions are attached to everything that a person owns. Every customer's feelings might provide light on what they really need from the customer service professional. Since human communication relies heavily on the ability to read one another's emotions, speech emotion recognition is crucial for understanding another person's inner workings. the time and frequency-based acoustic characteristics integrated with machine learning classifiers that can categorize emotions to enhance the performance of the proposed intelligent system. this research makes use of the audio records available in the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS). Emotion is gleaned from speech via temporal and spectral characteristics. three machine learning classifiers, the SVM, DT, and RF classifiers, are then trained on the retrieved data. the RF classifier achieved 88.78% higher accuracy rate than the state-of-the-art approaches. © 2024 IEEE.

关键词： Classification Decision Tree Emotion recognition Mel frequency cepstral coefficients Pitch Random Forest Classifier Short term energy Support Vector machine

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Explainable machine-learning for identifying the genetic biomarker MGMT in brain tumors using magnetic resonance imaging radiomics 14

Explainable Machine-Learning for identifying the genetic bio...

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14th international conference on pattern recognition Systems (ICPRS)

作者： Ponce, Sebastian Chabert, Steren Mayeta, Leondry Franco, Pamela Plaza-Vega, Francisco Querales, Marvin Salas, Rodrigo Univ Valparaiso Hlth Sci & Engn Valparaiso Chile Millennium Inst Intelligent Healthcare Engn iHEAL Ctr Interdisciplinary Biomed & Engn Res Hlth Valparaiso Chile Univ Valparaiso Sch Biomed Engn Valparaiso Chile Ctr Interdisciplinary Biomed & Engn Res Hlth Valparaiso Chile Univ Santiago Chile Dept Matemat & Ciencia Comp Santiago Chile Univ Valparaiso Sch Med Technol Valparaiso Chile

ISBN: (纸本)9798350375657

Brain tumors often feature the genetic biomarker O6-Methylguanine-DNA-Methyltransferase (MGMT) associated with a favorable response to chemotherapy and an improved prognosis. Currently, detecting MGMT presence relies on invasive brain biopsy procedures. thus, this study aims to develop a data mining-based radiomics methodology for non-invasive identifying and evaluating brain tumor genetic biomarkers using radiomics in magnetic resonance images (MRIs). MRIs with segmentation masks were used to extract variables and employ feature selection techniques. Several machine learning models were trained, where Logistic Regression, employing LASSO selection, emerged as the best-performing model, achieving 61% accuracy. Additionally, an explainability module utilizing Shap values identified three significant variables: a T1CE sequence variable related to texture, a FLAIR sequence variable of first-order statistics, and a T1 sequence variable of first-order statistics. this radiomic methodology, with its performance and explainable nature, could offer diagnostic support to clinicians in tumor management.

关键词： O6-Methylguanine-DNA-Methyltransferase (MGMT) methylation genetic biomarkers machine learning radiomics explainability magnetic resonance imaging brain tumors

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Zeroth Order GreedyLR: An Adaptive learning Rate Scheduler for Deep Neural Network Training 4

Zeroth Order GreedyLR: An Adaptive Learning Rate Scheduler f...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Subramanian, Shreyas Ganapathiraman, Vignesh Amazon Web Services Seattle United States

ISBN: (纸本)9798350324303

Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing the right learning rate / schedule for a machine learning task is still more art than science. In this paper, we introduce Zeroth order GreedyLR, a novel scheduler that adaptively adjusts the learning rate during training based on the current loss and gradient information. To validate the effectiveness of our proposed method, we conduct experiments on several NLP and CV tasks. the results show that our approach outperforms several state-of-the-art schedulers in terms of accuracy, speed, and convergence. Furthermore, our method is easy to implement, computationally efficient, and requires minimal hyperparameter tuning. Overall, our study provides a useful tool for researchers and practitioners in the field of deep learning. © 2023 IEEE.

关键词： Deep neural networks

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