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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151-160 订阅

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Utilizing Self-Supervised learning Features and Adapter Fine-Tuning for Enhancing Speech Emotion recognition 5

Utilizing Self-Supervised Learning Features and Adapter Fine...

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5th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2023

作者： Li, Tangxun Hou, Junjie China Academy of Space System Science and Engineering Beijing China

ISBN: (纸本)9798350359930

this paper employs an innovative approach that combines self-supervised learning feature fusion and adapter fine-tuning for a speech emotion recognition task. the results show a 5.57% improvement in accuracy compared to the baseline (Hubert upstream model) in the speech emotion recognition task, reaching the highest level of 75.1%. this progress illustrates the significant improvement of adapter fine-tuning on the migration performance of self-supervised learning features in different tasks. In summary, this study introduces an innovative solution to address the discrepancy between self-supervised learning features and downstream tasks, offering valuable insights for enhancing performance in speech emotion recognition. the integration of self-supervised learning with adapter fine-tuning not only holds promise for achieving comparable success across diverse speech-related tasks but also contributes fresh perspectives to the advancement of speech processing. © 2023 IEEE.

关键词： Supervised learning

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5th international conference on Big data and Security, ICBDS 2023

5th International Conference on Big Data and Security, ICBDS...

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5th international conference on Big data and Security, ICBDS 2023

ISBN: (纸本)9789819743896

the proceedings contain 18 papers. the special focus in this conference is on Big data and Security. the topics include: Development of U-net Neural Network for Biomedical Images with Big data;Research on Implementation of BADGE Active learning to Increase Defect Detection Effectiveness;stationary Multi-scale Hierarchical Dilated Graph Convolution for Multivariate Time Series Anomaly Detection;design of Human Resource Optimal Allocation Model for Electric Power Construction Enterprises Based on machine learning;research on the Construction of Digital Education Cloud Resource database Based on Ant Colony Algorithm;simulation of Short-Term Prediction Model of Real Estate Price Index Based on Neural Network Algorithm;PLMR: An Efficient Pre-trained Model for Aircraft Maneuver recognition;blockchain-Based WeChat Small Program Platform: A New Type of data Security and Management Solution;a Survey of Edge Computing Resource Allocation and Task Scheduling Optimization;an Algorithm for Analyzing Psychological Characteristics in Music Education Based on Markov Model;enhancing Privacy Strategy in New Power Systems: Problem Assessment, Comparative Analysis and Suggestions;internet Public Opinion Evolution Model and Simulation Based on Big data and Grey Clustering Algorithm Under New Media Environment;simulation of Regional Financial and Economic Development Decision Model Based on Decision Tree Algorithm;simulation Research on Pre-alarm Model of Enterprise Shared Financial Crisis Based on Genetic Algorithm;research on Accurate Poverty Alleviation Algorithm of Rural Industry Based on Association Rules mining;preface.

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2024 5th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

2024 5th International Conference on Information Science, Pa...

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5th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

ISBN: (纸本)9798350352719

the proceedings contain 138 papers. the topics discussed include: research on near-infrared spectroscopy calibration transfer algorithm based on piecewise direct standardization and K-Nearest neighbor;research on intelligent data operation and maintenance management scheme;GAIPPTSC: an efficient container tasks orchestration and scheduling algorithm;enhancing network security: machine learning evaluation for intrusion detection in power load management system;UAV anomaly detection model based on integrated multi-modal neural network and neural architecture search;frequency hopping modulation format recognition based on higher-order cyclic cumulants and neural network classifier;cluster-based two-stage client selection strategy for personalized federated learning;and implementation of fir digital filter algorithm in C language on DSP platform.

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Leveraging the Power of Modern Technologies in Planning Geotechnical Site Investigations for Mega Transportation Projects 5th

Leveraging the Power of Modern Technologies in Planning Geot...

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5th international conference on Transportation Geotechnics

作者： Lim, Eugene Moore, Nicholas Ha Tran Hanraham, Caitlin Aurecon Australasia Pty Ltd Brisbane Qld 4006 Australia Aurecon Australasia Pty Ltd Melbourne Vic 3008 Australia Aurecon Australasia Pty Ltd Sydney NSW 2060 Australia

ISBN: (纸本)9789819782239;9789819782215;9789819782208

Planning a geotechnical site investigation for amega transportation project is a challenging task especially if a large quantum of information and data must be managed. Traditionally, the focus in planning investigations is on the product itself with little care given to the data and information used to get to the product. this paper explains how to simplify the delivery of products and reduce costs by managing the data and the information correctly. It describes: how large language processing, optical character recognition, and machine learning models can accelerate data extraction from geotechnical logs;the fundamental qualities and functions of a cloud-based geotechnical database to improve collaboration and configuration management;how parts of the reliability assessment of historic ground data can be automated;how geographical information system (GIS) can summarize the desk study information into a singular site plan to enable a more holistic interpretation of subsurface conditions. this paper discusses how novel methods can enhance the efficiency, accuracy, and precision of the desk study, geotechnical risk assessment and the scoping processes. It is based on a highway upgrade project in Queensland, Australia involving multiple new interchanges and bridges, and where novel methods were used to deliver a cost-effective scope.

关键词： Geotechnical investigation GIS data management

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Deep Reinforcement learning for Exact Combinatorial Optimization: learning to Branch 26

Deep Reinforcement Learning for Exact Combinatorial Optimiza...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zhang, Tianyu Banitalebi-Dehkordi, Amin Zhang, Yong Univ Alberta Edmonton AB Canada Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-theart handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy Optimization (PPO) to further explore global optimal actions. then, a value network is used to run Monte-Carlo tree search (MCTS) to enhance the policy network. We evaluate the performance of our method on four different categories of combinatorial optimization problems and show that our approach performs strongly compared to the state-of-the-art machine learning and heuristics based methods.

关键词： Deep learning Systematics Monte Carlo methods NP-hard problem Input variables Reinforcement learning pattern recognition

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5th Spatial data Intelligence China conference, SpatialDI 2024

5th Spatial Data Intelligence China Conference, SpatialDI 20...

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5th Spatial data Intelligence China conference, SpatialDI 2024

ISBN: (纸本)9789819729654

the proceedings contain 25 papers. the special focus in this conference is on Spatial data Intelligence. the topics include: Understanding Spatial Dependency Among Spatial Interactions;An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete Fréchet Distance;structure and Semantic Contrastive learning for Nodes Clustering in Heterogeneous Information Networks;an Accuracy Evaluation Method for Multi-source data Based on Hexagonal Global Discrete Grids;applying Segment Anything Model to Ground-Based Video Surveillance for Identifying Aquatic Plant;mining Regional High Utility Co-location pattern;local Co-location pattern mining Based on Regional Embedding;RCPM_RLM: A Regional Co-location pattern mining Method Based on Representation learning Model;construction of a Large-Scale Maritime Elements Semantic Schema Based on Heterogeneous Graph Models;OCGATL: One-Class Graph Attention Networks with Transformation learning for Anomaly Detection for Argo data;RGCNdist2vec: Using Graph Convolutional Networks and Distance2Vector to Estimate Shortest Path Distance Along Road Networks;self-supervised Graph Neural Network Based Community Search over Heterogeneous Information Networks;measurement and Research on the Conflict Between Residential Space and Tourism Space in Pianyan Ancient Township;spatio-Temporal Sequence Prediction of Diversion Tunnel Based on machine learning Multivariate data Fusion;dyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction;predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model;MADB-RemdNet for Few-Shot learning in Remote Sensing Classification;Convolutional Neural Network Based on Multiple Attention Mechanisms for Hyperspectral and LiDAR Classification;Few-Shot learning Remote Scene Classification Based on DC-2DEC;Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Autonomous Vehicles;trajectory data Semi-fragile Wat

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Research on Brand Type Decision in Regional Passenger Vehicle Additional Purchase and Trade-in Market Based on CART Decision Tree Algorithm 24

Research on Brand Type Decision in Regional Passenger Vehicl...

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

作者： Wang, Yimo Zhang, Danyang Chang, Kaidi Product and Technology Strategy Department Automotive Data of China Co. Ltd. Tianjin China

ISBN: (纸本)9798400709999

As passenger vehicle market entered the era of stock competition, seizing opportunities in additional-purchase and trade-in (APT) market is of significant importance for the growth of different brand-type *** study aims at investigating the decision-making of users in regional APT market regarding brand types and abstracting quantitative *** mining and machine learning are used to train CART decision tree models, and the probability of users in different regions and with different characteristics switching to luxury, joint venture, or domestic brands under different car ownerships and repurchase choices are *** and individual characteristics of brand type decisions among users in different regions are summarised, which provides finer grained and quantifiable references for different types of brands to plumb regional demand for APT market and deploy product layout. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sales

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Proceedings - 2023 5th international conference on Applied machine learning, ICAML 2023

Proceedings - 2023 5th International Conference on Applied M...

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5th international conference on Applied machine learning, ICAML 2023

ISBN: (纸本)9798350341416

the proceedings contain 103 papers. the topics discussed include: machine learning based core data information management system in oil and gas industry;analyze organizational internal threats using machine learning;designing a cooperative evolution mechanism based on deep q-network;classification and recognition of 6G space-ground integrated network modulation modes;a classification method for microseisms and blasts in mine based on time and frequency domains analysis;research on edge network topology optimization based on machine learning;wind power prediction based on machine learning;quality assessment model of middle school geography teaching practice course based on decision tree algorithm;the application of interest oriented machine learning teaching method in clinical internship;and study on influencing factors of regional adjustment coefficient for geological survey based on machine learning.

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An Optimized Semantic Segmentation Deep learning Model for Automated Coronary Artery Calcification Prediction 5

An Optimized Semantic Segmentation Deep Learning Model for A...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Yee, Nathan Harker School San Jose United States

ISBN: (纸本)9798350355925

Coronary artery disease (CAD) is the leading cause of death in the United States, accounting for 25% of all deaths. Early detection by Coronary Artery Calcium (CAC) scoring of chest computed tomography (CT) scans is crucial but remains a significant challenge due to high costs, time demands, and the need for specialized experts and equipment. While current AI-based methodologies are promising, significant hurdles have impeded their clinical adoption. this study is the first effort to benchmark image classification and image segmentation based deep learning models in direct comparison and demonstrates that image segmentation techniques outperform in automated CAC scoring when benchmarked with image classification. the image classification is performed using ResNet-50, and the image segmentation is performed with an architecture based on Attention U-Net. the study benchmarks the models based on the Stanford AIMI COCA database with gated computed tomography scans of 790 patients and nearly 40,000 total image files. Additionally, this study implements an image optimization strategy that utilizes contrast limited adaptive histogram equalization (CLAHE) and Gaussian blur specific to the CAC score range, thereby predicting a patient's risks for CAD and recommending the appropriate preventative action or treatment. this approach effectively enhances predictive accuracy to above 90% for most CAC score ranges and Dice Coefficient (DCE) to approximately 0.86, a substantial improvement compared to previous efforts using image segmentation deep learning. With increased reliability, the proposed deep learning system demonstrates the viability of AI-driven CAC monitoring for those at risk of CAD to decrease mortality rates. © 2024 IEEE.

关键词： Computerized tomography

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Accurate data Sampling Methods for Medical data - Survey 5

Accurate Data Sampling Methods for Medical Data - Survey

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5th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Ramya Shri, G. Devasena, T. Janani, M. Karthick, R. M.Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Thalavapalayam Karur639113 India

ISBN: (纸本)9798350395235

In the internet age, data generation is rapidly increasing. Artificial intelligence, particularly big data classification technology, is becoming crucial in aiding medical auxiliary diagnosis research. However, medical big data often faces imbalances due to varying sample collections. Imbalanced data generally refers to a condition in which multiple samples of data within a given problem are not evenly distributed, thereby leading to underrepresenting one or more classes in the dataset. Conventional classification methods typically assume that each class has a comparable number of samples and that training misclassification costs are the same for all classes. However, compared to samples from healthy individuals, the misclassification cost of sick samples is higher. therefore, it is a pressing issue to figure out how to improve patient identification without degrading the classification of healthy people. the study experimented with different SMOTE oversampling and under-sampling methods. the purpose of this work is to balance the imbalanced data using different sampling techniques. Two medical datasets i.e., PIMA INDIAN DIABETICS dataSET and CHRONIC KIDNEY DISEASE dataSET downloaded from the Kaggle repository are used. Consequently, there are two phases to this study. One is data sampling and the other one is the Prediction model. Various data sampling techniques, including SMOTE, K-means SMOTE, SMOTENC (SMOTE for Nominal and Continuous), and SMOTEN (SMOTE for Nominal), were employed in this study. To apply the various ML (machine learning) algorithms, after obtaining updated data sampling datasets, in order to forecast the early stages of diabetes and chronic kidney disease. the optimum sampling and machine learning methods are determined based on the implemented, validated accuracy, precision, and recall values. © 2024 IEEE.

关键词： data mining

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