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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31-40 订阅

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A Particle Swarm Clustering Algorithm with Fuzzy Weighted Step Sizes 16th

A Particle Swarm Clustering Algorithm with Fuzzy Weighted St...

引用

16th international conference on intelligent data engineering and automated learning (ideal)

作者： Szabo, Alexandre Delgado, Myriam Regattieri de Castro, Leandro Nunes Fed Univ ABC Santo Andre Brazil Univ Tecnol Fed Parana Curitiba Parana Brazil Univ Prebiteriana Mackenzie Nat Comp Lab Sao Paulo Brazil

ISBN: (纸本)9783319248349;9783319248332

this paper proposes a modification in the Fuzzy Particle Swarm Clustering (FPSC) algorithm such that membership degrees are used to weight the step size in the direction of the local and global best particles, and in its movement in the direction of the input data at every iteration. this results in the so-called Membership Weighted Fuzzy Particle Swarm Clustering (MWFPSC). the modified algorithm was applied to six benchmark datasets from the literature and its results compared to that of the standard FPSC and FCM algorithms. By introducing these modifications it could be observed a gain in accuracy, representativeness of the clusters found and the final Xie-Beni index, at the expense of a slight increase in the practical computational time of the algorithm.

关键词： Fuzzy clustering Particle swarm data mining FPSC

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learning negotiation policies using IB3 and bayesian networks

Learning negotiation policies using IB3 and bayesian network...

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11th international conference on intelligent data engineering and automated learning, ideal 2010

作者： Nalepa, Gislaine M. Ávila, Bráulio C. Enembreck, Fabrício Scalabrin, Edson E. Graduate Program on Applied Computer Science PUCPR Pontifical Catholic University of Paraná R. Imaculada Conceição Curitiba PR 1155 Brazil

ISBN: (纸本)3642153801

this paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Bayesian networks

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Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Saez, Jose A. Luengo, Julian Stefanowski, Jerzy Herrera, Francisco Univ Granada CITIC UGR Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Burgos Dept Civil Engn LSI E-09006 Burgos Spain Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

ISBN: (纸本)9783319108407;9783319108391

Imbalance data constitutes a great difficulty for most algorithms learning classifiers. However, as recent works claim, class imbalance is not a problem in itself and performance degradation is also associated with other factors related to the distribution of the data as the presence of noisy and borderline examples in the areas surrounding class boundaries. this contribution proposes to extend SMOTE with a noise filter called Iterative-Partitioning Filter (IPF), which can overcome these problems. the properties of this proposal are discussed in a controlled experimental study against SMOTE and its most well-known generalizations. the results show that the new proposal performs better than exiting SMOTE generalizations for all these different scenarios.

关键词： Classification imbalanced data SMOTE class noise noise filters

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Application of YOLO Object Detection Network in Weld Surface Defect Detection 11

Application of YOLO Object Detection Network in Weld Surface...

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11th IEEE Annual international conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2021

作者： Zuo, Yinlong. Wang, Jintao. Song, Jilai. Northeastern University Faculty of Robot Science and Engineering ShenYang110000 China Shenyang Institute of Automation Chinese Academy of Sciences ShenYang110000 China

ISBN: (纸本)9781665425278

As industrial production becomes more modern and intelligent today, the inspection of product quality of the workshop is becoming more and more accustomed to replacing the old manual visual inspection methods with automated inspection systems. In the welding field, automated welding robots are not only used in traditional large-scale automobile assembly lines. In more general welding work, welding robots also plays an important role. the inspection of the welding quality of the welding robot is mainly to detect the four main types of weld defects. Compared to traditional defect classification based on support vector machines and defect detection based on template matching, this paper uses a welding surface defect detection system designed based on deep learning methods. By working with workshop welding experts, a large-scale image of nearly 5000 pictures is built. Large-scale weld defect datasets, while using the real-time and accuracy of the YOLO series of deep learning object detection frameworks, the weld defects detection model reaches 75.5% mean average precision(mAP) in constructed weld defect data set. In addition, the construction cost of the detection model and the deployment time of the detection system are greatly reduced. During the field test of the system in the workshop, among a batch of welding workpieces provided by the factory, the detection accuracy of weld defects reached 71%, which initially met the requirements of the workshop for an automated defect detection system. © 2021 IEEE.

关键词： Object detection

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Branching to Find Feasible Solutions in Unmanned Air Vehicle Mission Planning

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Ramirez-Atencia, Cristian Bello-Orgaz, Gema R-Moreno, Maria D. Camacho, David Univ Autonoma Madrid Dept Ingn Informat C Francisco Tomas & Valiente 11 E-28049 Madrid Spain Univ Alcala de Henares Dept Automat E-28871 Madrid Spain

ISBN: (纸本)9783319108407;9783319108391

Mission Planning is a classical problem that has been traditionally studied in several cases from Robotics to Space missions. this kind of problems can be extremely difficult in real and dynamic scenarios. this paper provides a first analysis for mission planning to Unmanned Air Vehicles (UAVs), where sensors and other equipment of UAVs to perform a task are modelled based on Temporal Constraint Satisfaction Problems (TCSPs). In this model, a set of resources and temporal constraints are designed to represent the main characteristics (task time, fuel consumption, ... ) of this kind of aircrafts. Using this simplified TCSP model, and a Branch and Bound (B&B) search algorithm, a set of feasible solutions will be found trying to minimize the fuel cost, flight time spent and the number of UAVs used in the mission. Finally, some experiments will be carried out to validate both the quality of the solutions found and the spent runtime to found them.

关键词： unmanned aircraft systems mission planning temporal constraint satisfaction problems branch and bound

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the Use of Unified Activity Records to Predict Requests Made by Applications for External Services 20th

The Use of Unified Activity Records to Predict Requests Made...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Grzenda, Maciej Kunicki, Robert Legierski, Jaroslaw Warsaw Univ Technol Fac Math & Informat Sci Ul Koszykowa 75 PL-00662 Warsaw Poland City Warsaw Digitalisat Dept Pl Bankowy 2 PL-00095 Warsaw Poland IoT & Adv Technol Ul Wolumen 11 PL-01912 Warsaw Poland

ISBN: (纸本)9783030336172;9783030336165

Many modern applications use services and data made available by provisioning platforms of third parties. the question arises if the use of individual services and data resources such as open data by novel applications can be predicted. In particular, whether initial software development efforts such as application development during hackathons can be monitored to provide data for the models predicting requests submitted to open data platforms and possibly other platforms is not clear. In this work, we propose an iterative method of transforming request streams into activity records. By activity records, vectors containing aggregated representation of the requests for external services made by individual applications over growing periods of software development are meant. the approach we propose extends previous works on the development of network flows aggregating network traffic and makes it possible to predict future requests made to web services with high accuracy.

关键词： data aggregation Prediction Network flow Web service

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Federated learning for Resource Management in Edge Computing 11

Federated Learning for Resource Management in Edge Computing

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Abdelgaber, Yahya Essam Ahmed, Youssef Abdelrahman Salem, Mohammed Abdel-Megeed Salem, Mohamed Abdel-Ghany German University in Cairo Media Engineering and Technology Cairo Egypt German University in Cairo Information Engineering and Technology Cairo Egypt

ISBN: (纸本)9798350322101

Federated learning is an innovative machine learning technique that enables the collaborative training of a model on diverse datasets from multiple independent devices. By de-centralizing the training process, federated learning ensures data privacy and security while harnessing the collective intelligence of edge devices. Effective resource management is essential in federated learning frameworks, as it helps in achieving the peak performance while ensuring the maintenance of ideal temperature conditions. Existing research work tends to propose new solutions rather than improving the current hardware capabilities. this research explores the application of Federated learning for Resource Management in Edge Computing using Flower federated learning simulation framework. A deep learning model is used to predict the device's temperature after a specific time interval. this prediction can be used to adjust the CPU frequency. Various experiments are conducted using different datasets, deep learning models, edge devices, the number of edge devices, and the number of training rounds in the federated learning framework. Results show that our proposed approach with the CNN regression model is very accurate and effective for resource management in federated learning frameworks. © 2023 IEEE.

关键词： Edge computing

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A real-time data compression for ground-based 3D LIDAR data using wavelets and compressive sensing

A real-time data compression for ground-based 3D LIDAR data ...

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Joint 5th international conference on Soft Computing and intelligent Systems and 11th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2010

作者： Im, Jeong Joon Leonessa, Alexander Kurdila, Andrew Ryoo, Young-Jae Department of Mechanical Engineering Virginia Tech Blacksburg VA 24061 United States Department of Control and Robot Engineering Mokpo National University Jeonam Korea Republic of

A map generated from ground-based threedimensional LIDAR data is a critical component for autonomous vehicle navigation using a vision based sensor. When the size of a map is large and the number of grid cells is relatively big, managing the map associated with a dense data set from 3D LIDAR scanner is a demanding task. Wavelets serve as the basis for an efficient compression scheme which makes it possible to significantly reduce processing effort to generate and manage a grid map in real-time. this research proposes a novel approach to generate an occupancy map from compressed measurement signals. A one-dimensional Haar wavelet transform has been applied to compress 3D scan data, from which occupancy maps have been generated. Our experimental results show that this method performs well to provide an autonomous vehicle with rich 3D environment information. As an alternative method to reduce 3D scan data, Compressive Sensing is also adopted in this research. Compressive Sensing uses a small sub set of data to recover the unknown original data. Utilization of Compact Sensing might lead us to the development of a favorable sensing system;however, conventional compressive sensing recovery algorithms might require a non-trivial amount of computing. With sparse Bayesian learning algorithm, compressive sensing can provide a fast and cost effective solution for acquisition systems characterized by large data set.

关键词： Compressed sensing

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Using Preferences to Solve Student-Class Allocation Problem

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

作者： Cano, Juan I. Sanchez, Luis Camacho, David Pulido, Estrella Anguiano, Eloy Univ Autonoma Madrid Dept Comp Sci C Francisco Tomas & Valiente 11 E-28049 Madrid Spain Univ Autonoma Madrid Ctr Referencia Linux UAM IBM E-28049 Madrid Spain

ISBN: (纸本)9783642043932

Resource allocation is a form of Constraint Satisfaction Problem (CSP) in which a set of resources must be assigned to a set of agents. Multiagent Resource Allocation (MARA) makes possible solving CSP using qualitative parameters as, for example, reduce idle time in scheduling or preferences over the set of resources to be allocated. In this paper, we are going to use a MARA approach to find a, solution to the student-class allocation problem, where each class has a number of seats, each student has his preferences over the schedule and the institution requests a uniform distribution of the students in classes and some other rules.

关键词： Students

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Feature Selection and Interpretable Feature Transformation: A Preliminary Study on Feature engineering for Classification Algorithms 19th

Feature Selection and Interpretable Feature Transformation: ...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Tallon-Ballesteros, Antonio J. Tuba, Milan Xue, Bing Hashimoto, Takako Univ Seville Seville Spain Singidunum Univ Belgrade Serbia Victoria Univ Wellington Wellington New Zealand Chiba Univ Commerce Konodai Ichikawa City Chiba Japan

ISBN: (纸本)9783030034962;9783030034955

this paper explores the limitation of consistency-based measures in the context of feature selection. these kinds of filters are not very widespread in large-dimensionality problems. Typically, the number of selected of attributes is very small and the ability to do right predictions is a drawback. the principal contribution of this work is the introduction of a new approach within feature engineering to create new attributes after the feature selection stage. the experimentation on multi-class problems with a feature space in the order of tens of thousands shed light on that some improvements took place with the new proposal. As a final insight, some new relationships were discovered due to the combined application of feature selection and feature transformation. Additionally, a new measure for classification problems which relates the number of features and the number of classes or labels is also proposed.

关键词： Classification Feature engineering Feature selection data mining Feature discovery Feature transformation

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