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检索条件"任意字段=10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009"

共 105 条记录，以下是91-100 订阅

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Language Models for Automatic Distribution of Review Notes in Movie Production 24th

Language Models for Automatic Distribution of Review Notes...

引用

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

作者： Garcés, Diego Santos, Matilde Fernández-Llorca, David Computer Science Faculty Complutense University of Madrid Madrid Spain Skydance Animation Madrid Madrid Spain Institute of Knowledge Technology Complutense University of Madrid Madrid Spain Computer Engineering Department University of Alcalá de Henares Madrid Spain European Commission Joint Research Centre Seville Spain

ISBN: (纸本)9783031482311

During the several years of production of an animated movie, review meetings take place daily, where supervisors and directors generate text notes about fixes needed for the movie. these notes are manually assigned to artistic departments for them to fixed. Being manual, many notes are not properly assigned and are never fixed, lowering the quality of the final movie. this paper presents a proposal for automating the distribution of these notes using multi-label text classification techniques. the comparison of the results obtained by fine-tuning several transformer-based language models is presented. A highest mean accuracy of 0.776 is achieved assigning several departments to each of the review notes in the test set with a BERT Multilingual model. A mean accuracy of 0.762 was reached in just 10 epochs and 10 min of training on an RTX-3090 with a DistilBERT transformer model. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Design and Implementation of a Course Scheduling System Using Activation Spreading

Design and Implementation of a Course Scheduling System Usin...

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ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Yukio Hori Takashi Nakayama Yoshiro Imai Information Technology Center Kagawa University Takamatsu Kagawa Japan Kanagawa University Hiratsuka Kanagawa Japan Faculty of Engineering Kagawa University Takamatsu Kagawa Japan

this paper develops an automated tool for generating the student's course schedule. A lot of students course schedule are manually generated based on syllabus information, such as class title, summary and categories written by the teachers. However, at a large number of classes, it is very exhaustive to manually generate the course schedule forthe students, due to various constraint condition or criteria. thus, this paper has formalizes this problem as a constraintsatisfaction/optimization problem, develops an automated tool, and evaluates the effectiveness of our system.

关键词： Constraint optimization data visualization Software engineering Artificial intelligence intelligent networks Distributed computing Processor scheduling Information technology Design engineering Costs

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Exploiting safety constraints in fuzzy self-organising maps for safety critical applications

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

作者： Kurd, Zeshan Kelly, Tim P. Austin, Jim High Integrity Systems Engineering Group Department of Computer Science University of York YorkYO10 5DD United Kingdom Advanced Computer Architectures Group Department of Computer Science University of York YorkYO10 5DD United Kingdom

ISBN: (纸本)3540228810

this paper defines a constrained Artificial Neural Network (ANN) that can be employed for highly-dependable roles in safety critical applications. the derived model is based upon the Fuzzy Self-Organising Map (FSOM) and enables behaviour to be described qualitatively and quantitatively. By harnessing these desirable features, behaviour is bounded through incorporation of safety constraints – derived from safety requirements and hazard analysis. the constrained FSOM has been termed a ‘Safety Critical Artificial Neural Network’ (SCANN) and preserves valuable performance characteristics for nonlinear function approximation problems. the SCANN enables construction of compelling (product-based) safety arguments for mitigation and control of identified failure modes. Illustrations of potential benefits for real-world applications are also presented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Safety factor

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intelligent IoT-based Combined Crop-type and Disease Prediction System with Different Machine learning & Deep learning Techniques

Intelligent IoT-based Combined Crop-type and Disease Predict...

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international conference on Electrical engineering, Computer Science and Informatics (EECSI)

作者： Md. Ishan Arefin Hossain Ahmed Kiser Israt Jahan Mitu Syeda Mahin Binta Haque Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

Agriculture contributes significantly to economic and social prosperity around the world. the world's population is increasing rapidly, so the food demand is also growing. Farmers must use harmful pesticides more frequently and intensively on the soil to meet the increased demand since more than previous methods are required. Also, this strategy can occasionally result in agricultural disaster, resulting in a considerable waste of crops, money, and effort. New automated edge intelligence technology sparks a farming revolution to meet that need. An intelligent IoT-based application is a comprehensive solution that predicts crop yields and detects illness in crops by utilizing machine learning and deep learning techniques with the most popular fashionable algorithms. To create a system, we use edge intelligence. this research focuses on integrating Raspberry Pi, sensor, and Azure IoT Hub. We successfully prevented crop waste by training 2000+ data points with a 94.18% accuracy rate. In contrast, this system is automated and software-based since it is lightweight, the costs are low, and the study reacts significantly better than other existing studies.

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A Machine-learning Based Approach for data-Driven Identification of Heating Dynamics of Buildings' Living-Spaces

A Machine-Learning Based Approach for Data-Driven Identifica...

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IEEE international conference on intelligent data Acquisition and Advanced Computing Systems

作者： Roozbeh Sadeghian Broujeny Kurosh Madani Abdennasser Chebira Veronique Amarger Laurent Hurtard Université Paris-Est LISSI Laboratory EA 3956 Senart-FB Institute of Technology Lieusaint France

Modeling the heating dynamics of a given living-space of a real building remains a challenging engineering-science problem because of the quite large number of diverse kinds of involved parameters and their usually nonlinear interdependency. However, the need of such living-spaces' heating dynamics modeling appears as a foremost requirement for designing adaptive controllers scheming the complex behaviors of nowadays' smart buildings. In this context and through considering the above-mentioned complex dynamics' modeling within the slant of "Time-Series Prediction" paradigm, in this paper we propose a Machine-learning-based data-driven approach for overcoming difficulties inherent to the aforementioned challenging engineering-science problem. the proposed approach takes advantage from the nonlinear autoregressive exogenous (NARX) model's capabilities in time-series' forecasting and the Multi-Layer Perceptron's (MLP) learning and generalization skills. the proposed approach has been applied for living-space heating dynamics identification of a fully automated four-floor real building located at Senart Campus of University Paris-Est Creteil (UPEC). the obtained results assessing the accuracy of the investigated Machine-learning-based approach are reported and discussed.

关键词： Mathematical model Space heating Object oriented modeling Energy consumption Smart buildings

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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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data-Driven Force Control of an automated Scratch Test

Data-Driven Force Control of an Automated Scratch Test

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international conference on Control, Mechatronics and Automation (ICCMA)

作者： Florian Diepers Dominik Polke Elmar Ahle Dirk Söffker Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Dynamics and Control University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781665490498

the development of chemicals, like coatings, is increasingly automated in high-throughput plants. the assessment of the quality of the coating formulation is also automated to achieve precise and reproducible results. Usually, a cross-cut test is performed to characterize the adhesion of coatings. To obtain comparable conditions and comply with standards, the force during scratching has to be controlled. In this paper, the force control of an automated testing equipment is optimized. there are different substrates, coating formulations and process parameters influencing the quality of the adhesion of the coatings. Because of this, the force controller has to cover a variety of dynamics. the different coatings and nonlinear behavior of the scratching makes modeling the system extremely difficult. therefore, a data-driven approach is used. Two data-driven methods, the intelligent PID (iPID) and Virtual Reference Feedback Tuning (VRFT), are applied to the field of force control. the results for both control methods are compared. Additionally, both methods are combined with an Iterative learning Control (ILC) to optimize the control performance over multiple iterations. the qualities of the data-driven controllers are evaluated. All results are compared to the currently implemented controller.

关键词： Mechatronics Adhesives Force Nonlinear dynamical systems Coatings Force control Tuning

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the influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

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8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. these measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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Employing Deep Unsupervised learning Method to Identify Testing Scenarios for automated Vehicles

Employing Deep Unsupervised Learning Method to Identify Test...

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international conference on Dependable Systems and their Applications (DSA)

作者： Shuai Liu Fan Ren Guo Yang Dingchang He Yonggang Liu State Key Laboratory of Vehicle NVH and Safety Technology Chongqing China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China Changan Automobile Intelligent Research Institute Chongqing China

the significance of naturalistic driving data (NDD) lies in its application for assessing autonomous driving systems across diverse driving conditions. this paper introduces an approach to identify testing scenarios for autonomous vehicles using deep unsupervised learning techniques. Initially, a deep regularized autoencoder (DRAE) network is designed to extract feature vectors from the NDD. Next, these feature vectors are then clustered by K-Means clustering (KMC) and seven types of scenarios are obtained. Finally, a comparison is made between the proposed DRAE-KMC approach and other clustering algorithms. the outcome demonstrates that the proposed DRAE-KMC method outperforms other methods. Furthermore, Google Earth is applied to project the identified scenarios. the result illustrates that the projected scenes align with the identified scenarios, which further validates the effectiveness of proposed DRAE-KMC method.

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Federated Vehicular Tailgating Detection Utilizing Distance and Speed data Analytics for intelligent Transport System

Federated Vehicular Tailgating Detection Utilizing Distance ...

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international conference on Communications and Signal Processing

作者： Subbulakshmi Velumail Shilpa Shashikant Chaudhari T S Sai Kiran Department of Computer Science and Engineering) M. S. Ramaiah Institute of Technology (Affiliated to VTU) Bengaluru Karnataka India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Connected and automated Vehicles (CAVs) are designed with sensors, cameras, and other computing devices to collect data to support cooperative intelligent Transportation Systems (ITS). Many potential applications offered by CAVs exist for researchers and road safety stakeholders. Vehicle tailgating is one such application to analyze risky driving behavior. Reliable detection of such behavior is hard and expensive. this paper focuses on addressing the tailgating issue in CAVs using the dataset generated using Simulation Of Urban Mobility (SUMO) for Indian roads. the proposed model of this study uses the Relative speed and Euclidean distance of the preceding and rear vehicle as parameters on the distributed Federated learning (FL) framework to incorporate security for sensitive vehicular data. We verified our prediction model in the TensorFlow Federated framework, and our results show an accuracy of 96 percent.

关键词： Federated learning Transportation Signal processing Predictive models Sensor systems Road safety Stakeholders

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