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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3137 条记录，以下是101-110 订阅

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Research on three-Dimensional Failure Envelope Surface of Composite Materials Based on data-driven 5

Research on Three-Dimensional Failure Envelope Surface of Co...

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5th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Wei, Jingchao Cao, Su Wang, Zhe Yang, Yu Aircraft Strength Research Institute of China National Key Laboratory of Strength and Structural Integrity Xi'an China

ISBN: (纸本)9798350368284

data-driven methods based on machine learning have been successfully used in composite material design, structural analysis, data processing, material performance characterization, prediction and testing, and other fields. there is no theory in the field of composite material applications that can provide high-precision failure analysis and prediction results in all situations. this paper designed a biaxial compression-compression test of composite materials to obtain multi-variable data such as DIC digital speckle data, testing machine load-displacement data, and strain data. For the multidimensional data generated by the biaxial test, a fusion algorithm based on machine learning was used to construct a three-dimensional failure envelope of the composite material. the results show that the data mining methods can identify correlations in failure data and express them in the form of three-dimensional stress function equations. data-driven methods based on physical information can further improve the accuracy of composite material failure behavior prediction and provide a technical basis for failure analysis and strength prediction of composite material structures. © 2024 IEEE.

关键词： Compression testing

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Linear Regression Approach for Stock Chart pattern recognition 5

Linear Regression Approach for Stock Chart Pattern Recogniti...

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5th Biennial international conference on Nascent Technologies in Engineering, ICNTE 2023

作者： Tripathi, Amay Mathure, Jignesh Deotarse, Shivam Rai, Darshak Gadhikar, Lakshmi Fr. C. Rodrigues Institute of Technology Department of Information Technology Navi Mumbai India

ISBN: (纸本)9781665465045

Finding stock chart patterns, like channels, triangles, head and shoulders, etc., in high-dimensional stock data is arduous. Technical analysis experts can visualize these patterns in stock candlestick charts based on experience and predict stock price movements. the subjectivity of visualizing these stock patterns by different experts increases the possibility of drawing different variations of the same patterns based on scale and time. Stock market pattern recognition is a complex challenge due to the volatile nature of the stock price. Various machine-learning algorithms can be used to detect stock chart patterns. A linear regression approach for forming these patterns can minimize human errors by automating the process. In this paper, we have implemented linear regression, which is an efficient approach for handling dynamic stock data and drawing a triangle pattern on charts. It has an average accuracy of 98.60% when the top ten stocks of the Information Technology sector and Pharmaceutical sector in terms of market capitalization in the Indian National Stock Exchange are analyzed. © 2023 IEEE.

关键词： pattern recognition

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Study on Classification Models Utilizing the Multilayer Perceptron Feature Selection and Ensemble Voting Classifier 24

Study on Classification Models Utilizing the Multilayer Perc...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Zhu, Xiaohang Fang, Huan Zhao, Xinli School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan China School of Petroleum and Natural Gas Engineering Changzhou University Jiangsu Changzhou China

ISBN: (纸本)9798400709999

the existing classification prediction models increase in training time and cost with the increase in the number of features, significantly increase in computational complexity, and irrelevant or redundant features interfere with the model's learning process and affect the accuracy of classification prediction *** this study, a model combining the multilayer perceptron (MLP) feature selection with an ensemble voting classifier (MRS) is *** MLP feature selection method demonstrates superiority in screening important features, which can effectively extract key information from the original ***, the classification prediction performance of the model is further improved by feeding the filtered features into an ensemble voting classifier that incorporates Random Forest (RF) and Support Vector machine (SVM).the model enhances the processing efficiency of classification problems in the field of data mining, especially in the context of confronting multi-featured and high-dimensional data, and more accurately performs classification ***, the accuracy of the model exceeds that of many other models is proved by the experiments, demonstrating its great potential in solving the classification and prediction problems in the field of data mining. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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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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Efficient recognition of Broken Road Surface for Autonomous Driving using Broad learning System 5

Efficient Recognition of Broken Road Surface for Autonomous ...

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5th international conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2024

作者： Zhu, Junhao Ji, Tong Liu, Hui Jinan University College of Information Science and Technology Guangzhou China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China

ISBN: (纸本)9798331533991

In the era of artificial intelligence, assessing road damage in autonomous driving applications is of paramount importance. Accurate and timely identification of road surface damage is critical for ensuring safety and maintaining vehicle performance. this paper proposes the utilization of Broad learning System (BLS) for rapid identification and classification of road surface damage, with a strong emphasis on real-time processing capabilities. Our approach leverages the efficiency and adaptability of BLS to provide swift and reliable road condition assessments, demonstrating significant potential for enhancing the safety and reliability of autonomous driving systems. © 2024 IEEE.

关键词： Adversarial machine learning

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SSL400 - A Comprehensive Word Level dataset for Sinhala Sign Language recognition 5

SSL400 - A Comprehensive Word Level Dataset for Sinhala Sign...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Abhishek, Yohan Sumanathilaka, T.G.D.K. Informatics Institute of Technology School of Computing Colombo006 Sri Lanka

ISBN: (纸本)9798331530983

In the context of increasing digital accessibility and the need for inclusive communication technologies, the demand for automated sign language recognition has grown significantly. However, Sinhala Sign Language (SSL), used by the Sri Lankan deaf community, remains underexplored due to the lack of labelled datasets and the challenges of developing recognition models for low-resource languages. this study addresses this gap by introducing an SSL dataset, specifically curated to support real-time recognition of 384 commonly used words in daily and work-related contexts. the methodology for dataset creation involved expert consultations, a structured data collection process, and multi-step labelling. these steps ensured the accurate labeling of video data, which can be used for machine learning tasks like gesture recognition, sentence formation, and speech translation. the proposed dataset lays the foundation for SSL recognition models that are fast, lightweight, and optimized for low-resource environments. Additionally, we explore the performance of initial recognition models using this dataset, demonstrating its applicability in real-world scenarios. the dataset and research outcomes provide valuable resources for the development of inclusive AI-driven communication tools for the Sinhala-speaking deaf community. this contribution addresses the pressing need for better technological support for underrepresented languages in the field of sign language recognition and processing. © 2025 IEEE.

关键词： Multi-task learning

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data Analysis and Visualization for Electrical Safety Tools and Equipment 5

Data Analysis and Visualization for Electrical Safety Tools ...

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5th international conference on Computer Engineering and Application (ICCEA)

作者： Xue, Liang Yang, Lei Suzhou City Univ Comp Sci & Artificial Intelligence Coll Suzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Electric safety tool data has the characteristics of large data volume, high dimensionality, and strong effectiveness. Traditional data analysis still has problems such as insufficient accuracy, slow processing speed, and imprecise feature selection when processing this type of data. this paper proposes a method for predicting the loss of safety tools based on classification algorithms, which provides data support for procurement by predicting the loss. In addition, a supplier scoring model based on scoring indicators was constructed to explore the potential value of the data. First, the data is characterized by construction, elimination, fusion, and reconstruction, and correlation coefficients are used for feature selection and tested on different prediction models;Secondly, a data classification model based on scoring indicators is established to form a new data dimension, including after-sales service, product quality, scale indicators, and customer feedback;Finally, visualize the data set from the perspective of the overall table and sub-tables, and the changing trends of key evaluation indicators were analyzed. this method can not only provide data for predicting the loss of electric power tools but also solve the problem of supplier evaluation and realize multi-dimensional in-depth mining and comprehensive visualization of data.

关键词： component machine learning Feature engineering Classification algorithm data analysis data visualization

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Deep learning: Exploring the Depths of Artificial Neural Networks for Advanced data Analysis and pattern recognition 5

Deep Learning: Exploring the Depths of Artificial Neural Net...

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5th IEEE Global conference for Advancement in Technology, GCAT 2024

作者： Ravikumar, Rajesh Jagadeesan Ammupriya, A. Devi, P.P. Pandi, V.Samuthira Akula, Akhila Sankar, K. Evernorth Health Services ChattanoogaTN37402 United States St Joseph's College of Engineering Department of Mba Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Avadi Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350376685

this research provides a comprehensive exploration of LSTMs, CNNs, and RNNs, with a focus on their application in advanced data analysis and pattern recognition. We begin by examining the theoretical underpinnings of each architecture, highlighting their unique features and the mechanisms by which they process and learn from data. In the process of collecting long-term relationships in sequential data, LSTMs have been demonstrated to perform exceptionally well, making them an excellent choice for applications such as language modeling and time series prediction. CNNs are discussed in the context of their ability to extract hierarchical features from spatial data, rendering them particularly effective for image and video analysis. RNNs, with their inherent ability to process sequences of varying lengths, are analyzed for their applications in speech recognition and machine translation. Following this, the research begins to conduct a comparative examination of different architectures, examining how well they perform on benchmark datasets as well as challenges that are encountered in the real world. We assess their strengths and limitations, considering factors such as accuracy, computational efficiency, and scalability. Additionally, we explore the challenges associated with training deep neural networks, including the need for extensive data preprocessing, the risk of overfitting, and the importance of hyperparameter tuning. Finally, we discuss the implications of these findings for future research and practical applications in data analysis and pattern recognition. the study concludes that while each architecture has its niche, the combination of LSTMs, CNNs, and RNNs offers a powerful toolkit for tackling complex problems across diverse domains. the research underscores the potential of deep learning to unlock insights from data at unprecedented scales and complexities, paving the way for innovative solutions in fields ranging from healthcare to finance. © 2024 IE

关键词： Deep neural networks

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5th international conference on Trends in Computational and Cognitive Engineering, TCCE 2023

5th International Conference on Trends in Computational and ...

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5th international conference on Trends in Computational and Cognitive Engineering, TCCE 2023

ISBN: (纸本)9789819719228

the proceedings contain 49 papers. the special focus in this conference is on Trends in Computational and Cognitive Engineering. the topics include: BScFilter: A Deep learning Approach for Sports Comments Filtering in a Resource Constraint Language;HABE Secure Access at Cloud-Healthcare database;comparative Analysis of Nature-Inspired Algorithms for Task Assignment Problem;A Novel Approach for Object recognition in Hazy Scenes: Integrating YOLOv7 Architecture with Boundary-Constrained Dehazing;dynamic Multihead Attention for Enhancing Neural machine Translation Performance;scratch Vision Transformer Model for Diagnosis Grape Leaf Disease;multilevel Association mining with Particle Swarm Optimization: A Comprehensive Approach for High-Utility Itemset Discovery;attention-Based Neural machine Translation for Multilingual Communication;use of Deep learning to Handle Early-Stage Business data;an Efficient Text-Based Document Categorization with k-Means and Cuckoo Search Optimization;potato Leaf Disease Detection and Classification Using Deep learning;parkinson’s Disease Detection Using machine learning;Comparative Analysis of ML Models for Brain Tumor Detection;machine learning-Based Prediction of COVID-19: A Robust Approach for Early Diagnosis and Treatment;the Prediction of Diabetes Using machine learning in the Healthcare System;a Fuzzy-Based Vision Transformer Model for Tea Leaf Disease Detection;towards Federated-Deep learning-Based Glaucoma Detection from Color Fundus Images;Inventing the Potential of a High-Frequency EEG, Namely Dodecanogram (DDG): Human Subjects’ Study;real-Time Prediction of Diabetes Complications Using Regression-Based machine learning Models;exploring Open Access Cybersecurity datasets for machine learning-Based Cyberattack Detection;design and Development of a Reliable Blockchain-Based Pension System;Comparative Analysis of CatBoost Against machine learning Algorithms for Classification of Altered NSL-KDD;enhancing Scalability in Sharding Blo

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A Comprehensive Review on Bio-Inspired Optimization Techniques 5th

A Comprehensive Review on Bio-Inspired Optimization Techniqu...

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5th international conference on data Science, machine learning and Applications

作者： Nimmala, Satyanarayana Samya, Banoth Reddy, Jakkala Ravichandra Ramana, T. Venkata Ch, Ravikumar CVR Coll Engn Dept CSE DS Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vijay Rural Engn Coll Dept CSE Nizamabad Telangana India CVR Coll Engn Dept Comp Sci & Engn AIML Hyderabad Telangana India Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the concept of optimization has gained widespread recognition in numerous industries worldwide. To maximize resources like time, money, distance, options, and searches, people and organizations are actively looking for the best answers to particular problems. the traditional methods of tackling optimization problems are no longer considered adequate since they frequently produce suboptimal results or take too long. Using heuristic and meta-heuristic methods is now necessary to solve optimization problems. To avoid becoming trapped in locally optimal solutions, meta-heuristic approaches are made to thoroughly investigate and utilize the entire search space. Meta-heuristic algorithms are remarkably adept at solving optimization issues;they take their cues from the behavior of real organisms such as fish, birds, insects, and bees. In almost every modern field, optimization is a crucial factor to take into account while attempting to resolve real-world issues. An in-depth analysis of bio-inspired optimization methods and their real-world applications for a variety of optimization problems is presented in this paper.

关键词： Optimization Heuristic methods Meta-heuristic methods Trade-off Bio-inspired optimization

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