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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010"

共 74 条记录，以下是61-70 订阅

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GPU-Based Acceleration of the Rao Optimization Algorithms: Application to the Solution of Large Systems of Nonlinear Equations 24th

GPU-Based Acceleration of the Rao Optimization Algorithms:...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Silva, Bruno Lopes, Luiz Guerreiro Doctoral Program in Informatics Engineering University of Madeira Madeira Is. Funchal Portugal Regional Secretariat for Education Science and Technology Regional Government of Madeira Funchal Portugal Faculty of Exact Sciences and Engineering University of Madeira Madeira Is. Funchal9020-105 Portugal

ISBN: (纸本)9783031482311

In this paper, parallel GPU-based versions of the three Rao metaphor-less optimization algorithms are proposed and used to solve large-scale nonlinear equation systems, which are hard to solve with traditional numerical methods, particularly as the size of the systems get bigger. the parallel implementations of the Rao algorithms were performed in Julia and tested on a high-performance GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB of GDDR6X VRAM using a set of challenging scalable systems of nonlinear equations. the computational experiments carried out demonstrated the efficiency of the proposed GPU-accelerated versions of the Rao optimization algorithms, with average speedups ranging from 57.10 × to 315.12 × for the set of test problems considered in this study. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graphics processing unit

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Pruning adaptive boosting ensembles by means of a genetic algorithm

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7th international conference on intelligent data engineering and automated learning, ideal 2006

作者： Hernández-Lobato, Daniel Hernández-Lobato, José Miguel Ruiz-Torrubiano, Rubén Valle, Ángel Escuela Politécnica Superior Universidad Autónoma de Madrid C/ Francisco Tomas y Valiente 11 Madrid 28049 Spain Cognodata C/ Caracas 23 4aplanta Madrid 28010 Spain

ISBN: (纸本)3540454853

this work analyzes the problem of whether, given a classification ensemble built by Adaboost, it is possible to find a subensemble with lower generalization error. In order to solve this task a genetic algorithm is proposed and compared with other heuristics like Kappa pruning and Reduce-error pruning with backfitting. Experiments carried out over a wide variety of classification problems show that the genetic algorithm behaves better than, or at least, as well as the best of those heuristics and that subensembles with similar and sometimes better prediction accuracy can be obtained. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Adaptive systems

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Unsupervised dense regions discovery in DNA microarray data

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5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Yip, Andy M. Wu, Edmond H. Ng, Michael K. Chan, Tony F. Department of Mathematics University of California 405 Hilgard Avenue Los AngelesCA90095-1555 United States Department of Mathematics The University of Hong Kong Pokfulam Road Hong Kong

ISBN: (纸本)3540228810

In this paper, we introduce the notion of dense regions in DNA microarray data and present algorithms for discovering them. We demonstrate that dense regions are of statistical and biological significance through experiments. A dataset containing gene expression levels of 23 primate brain samples is employed to test our algorithms. Subsets of potential genes distinguishing between species and a subset of samples with potential abnormalities are identified. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Gene expression

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Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm for Few-shot learning 11

Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm...

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11th international conference on Machine Intelligence theory and Applications, MiTA 2024

作者： Zhang, Xinyuan Yang, Boda Ou Hu, Yue Jinan University School of Intelligent Systems Sciences and Engineering Guang Zhou China Jinan University School of Humanities Guang Zhou China

ISBN: (纸本)9798350385670

In recent years, the advancement of artificial intelligence algorithms, particularly in the domain of semantic segmentation within computer vision, has been noteworthy. this progress is attributed to the availability of diverse large-scale datasets and the continual improvement of computational resources, with deep learning serving as a representative paradigm. Consequently, numerous sophisticated segmentation models have emerged for visual tasks. However, the efficacy of these models hinges on the availability of substantial annotated datasets for training. A notable challenge arises in scenarios where few-shot datasets are prevalent due to inherent limitations. the sparse data volume in these instances often stems from difficulties in obtaining a sufficiently large and high-quality dataset. the process of image annotation is resource-intensive, demanding considerable time and human effort. Addressing the consequential shortage of sample size in few-shot datasets becomes a paramount concern. this paper tackles the aforementioned issues related to few-shot datasets and introduces the Hilbert encoded cooperative coevolutionary genetic algorithm (HCCGA), a novel image enhancement algorithm designed for the automated generation of 3D-Lookup Tables (3D LUTs). HCCGA leverages 3D LUTs generated automatically through the genetic algorithm to map the original few-shot dataset, thereby obtaining a more extensive set of high-quality samples. the methodology employed in this research encompasses the generation and optimization of 3D LUTs using a divide-and-conquer strategy based on genetic algorithms. Additionally, it involves the mapping of new images using 3D LUTs in conjunction with sampling and interpolation. the evaluation of the proposed approach encompasses comparisons based on various image binary classification evaluation metrics applied to images before and after mapping through the results of model inference. Experimental results obtained using HCCGA on diverse publ

关键词： Semantic Segmentation

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A genetic graph-based clustering algorithm

A genetic graph-based clustering algorithm

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13th international conference on intelligent data engineering and automated learning, ideal 2012

作者： Menéndez, Héctor Camacho, David Departamento de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid C/Francisco Tomás y Valiente 11 Madrid 28049 Spain

ISBN: (纸本)9783642326387

the interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. the approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. the main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. this algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC. © 2012 Springer-Verlag.

关键词： Genetic algorithms

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Block clustering for web pages categorization

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10th international conference on intelligent data engineering and automated learning, ideal 2009

作者： Charrad, Malika Lechevallier, Yves Ahmed, Mohamed Ben Saporta, Gilbert National School of Computer Sciences Manouba 2010 Tunisia INRIA-Rocquencourt Le Chesnay Cedex 78153 France Conservatoire National des Arts et Métiers 292 rue Saint-Martin Paris 75141 France

ISBN: (纸本)3642043933

With the growth of web-based applications and the increased popularity of the World Wide Web (WWW), the WWW became the greatest source of information available in the world leading to an increased difficulty of extracting relevant information. Moreover, the content of web sites is constantly changing leading to continual changes in Web users' behaviours. therefore, there is significant interest in analysing web content data to better serve users. Our proposed approach, which is grounded on automatic textual analysis of a web site independently from the usage attempts to define groups of documents dealing with the same topic. Both document clustering and word clustering are well studied problems. However, most existing algorithms cluster documents and words separately but not simultaneously. In this paper, we propose to apply a block clustering algorithm to categorize a web site pages according to their content. We report results of our recent testing of CROKI2 algorithm on a tourist web site. © 2009 Springer Berlin Heidelberg.

关键词： Websites

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Analyzing key factors of human resources management

Analyzing key factors of human resources management

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12th international conference on intelligent data engineering and automated learning, ideal 2011

作者： Sáiz, Lourdes Pérez, Arturo Herrero, Álvaro Corchado, Emilio Department of Civil Engineering University of Burgos C/ Francisco de Vitoria s/n Burgos09006 Spain Investigador Programa Torres Quevedo TTT Diseño Comunicación y Contenidos S.L. Reyes Católicos 41 1 Burgos09005 Spain Departamento de Informática y Automática Universidad de Salamanca Plaza de la Merced s/n Salamanca37008 Spain

ISBN: (纸本)9783642238772

this study presents the application of an unsupervised neural projection model for the analysis of Human Resources (HR) from a Knowledge Management (KM) standpoint. this work examines the critical role that the acquisition and retention of specialized employees play in Hi-tech companies, particularly following the configuration approach of Strategic HR Management. From the projections obtained through the connectionist models, experts in the field may extract conclusions related to some key factors of the HR Management. One of the main goals is to deploy improvement and efficiency actions in the implantation and execution of the HR practices in firms. the proposal is validated by means of an empirical study on a real case study related to the Spanish Hi-tech sector. © 2011, Springer-Verlag Berlin Heidelberg.

关键词： Human resource management

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the Potential of AI in Electrical and Electronic engineering Education: A Review

The Potential of AI in Electrical and Electronic Engineering...

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IEEE international conference on E-learning in Industrial Electronics, ICELIE

作者： Jiaqin Sun Chiew-Foong Kwong Giampaolo Buticchi Department of Electrical and Electronic Engineering University of Nottingham Ningbo China

ISBN: (数字)9798350363654

ISBN: (纸本)9798350363661

the rapid advancement of Artificial Intelligence (AI) technologies is transforming education, particularly in Electrical and Electronic engineering (EEE). this paper explores the potential applications, benefits, and challenges of Generative AI (GenAI) and Large Language Models (LLMs) in EEE education. Key areas include personalized learning, intelligent tutoring systems, automated grading, and predictive analytics. While these technologies offer significant enhancements in teaching and learning, they also present challenges such as data privacy, bias, and the need for human interaction. By examining current implementations and providing recommendations, this paper aims to guide educators and researchers in effectively integrating AI to improve EEE education.

关键词： Industrial electronics Electric potential data privacy Electronic learning Reviews Generative AI Large language models Education Predictive analytics Electronics engineering education

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intelligent Emergency Notification Mobile Service via Multi-task BERT Models

Intelligent Emergency Notification Mobile Service via Multi-...

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IEEE international conference on Mobile Cloud Computing, Services, and engineering (MobileCloud)

作者： Wenhao Tan Yanzhou Zhang Kaikai Liu Computer Engineering Department San José State University (SJSU) San José CA USA

Mobile social networks have the potential to become the most effective platform for delivering urgent information during natural disaster events. Social media applications like Twitter are ideal for real-time news delivery but lack many important features to become an effective emergency notification system. the most significant challenge is determining which news is an emergency and whether it is relevant to the user. In this project, we propose an intelligent emergency notification social network application that can automatically perform data mining from online social media platforms, filter out non-emergency and unrelated news, and deliver only classified emergency notifications to users. Rather than using multiple separate systems or machine learning models to achieve our goals, we propose a joint Multi-class Text Classification model based on BERT to filter out non-emergency tweets and classify the type of emergencies in one end-to-end neural network model. Additionally, we train a Named Entity Recognition (NER) model to extract locations from the classified emergency news. We evaluated our system in multiple historical emergency events and demonstrated the effectiveness of emergency news delivery using various performance metrics.

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An NTV-Light GBM-Based Method for Identifying Rice Pests with Small Samples in Complex Backgrounds

An NTV-Light GBM-Based Method for Identifying Rice Pests wit...

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Information Systems and Computing Technology (ISCTech), international conference on

作者： Xuanying Liao Hongyun Yang Aizhen Sun School of Computer and Information Engineering Jiangxi Agricultural University Nanchang China School of Software Jiangxi Agricultural University Nanchang China

To address the problems of complex background of rice pest images in natural scenes, few pest image data samples and low accuracy of pest image recognition. A Global-Local Feature Fusion of Integrated learning-Model is proposed for the accurate recognition of six common rice pests, including rice green stink bug, rice black stink bug, East Asian flying locust, Chinese rice locust, borer and giant stem borer. data enhancement and data expansion of the collected pest images were carried out by random rotation, left-right flip, changing brightness, contrast, chromaticity, saturation and adding noise; the global features of the image are extracted using the NetVLAD algorithm; the local features of the image are extracted using the migration learning model VGG16 network; the extracted global-local features are fused to reduce the interference of complex backgrounds on important features and enhance the specificity and generalisation of the features; the integrated learning model LightGBM is introduced for classification to improve the accuracy and generalisation of the whole model. the experimental results show that the model has a recognition accuracy of 100% in the training set; 99.77% in the validation set; 90% in the test set, with a Macro Precision value of 91.90%, a Macro Recall value of 90% and a Macro F1-score value of 89.72%. the model provides a reliable and universal method for fully automated and intelligent diagnosis of small samples of rice pests in real environments.

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