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

共 7177 条记录，以下是4211-4220 订阅

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I Don't Have that Much data! Reusing User Behavior Models for Websites from Different Domains 20th

I Don't Have That Much Data! Reusing User Behavior Models fo...

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20th international conference on Web engineering (ICWE)

作者： Bakaev, Maxim Speicher, Maximilian Heil, Sebastian Gaedke, Martin Novosibirsk State Tech Univ Novosibirsk Russia C&A Europe Dusseldorf Germany Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9783030505783;9783030505776

User behavior models see increased usage in automated evaluation and design of user interfaces (UIs). Obtaining training data for the models is costly, since it generally requires the involvement of human subjects. For interaction's subjective quality parameters, like aesthetic impressions, it is even inevitable. In our paper, we study applicability of trained user behavior models between different domains of websites. We collected subjective assessments of Aesthetics, Complexity and Orderliness from 137 human participants for more than 3000 homepages from 7 domains, and used them to train 21 artificial neural network (ANN) models. the input neurons were 32 quantitative metrics obtained via computer vision-based analysis of the homepages screenshots. then, we tested how well each ANN model can predict subjective assessments for websites from other domains, and correlated the changes in prediction accuracies with the pairwise distances between the domains. We found that the Complexity scale was rather domain-independent, whereas "foreign-domain" models forAesthetics and Orderliness had on average greater prediction errors for other domains, by 60% and 45%, respectively. the results of our study provide web designers and engineers with a first framework to assess the reusability and difference in prediction accuracy of the models, for more informed decisions.

关键词： Web design User experience Machine learning Training data

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Collecting and analysing learners data in a massive open online course for mathematics 11

Collecting and analysing learners data in a massive open onl...

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11th international conference on Computer Supported Education, CSEDU 2019

作者： Azevedo, Ana Oliveira, Marisa Barreiras, Alcinda Azevedo, Jose Manuel Marcos, Graça Ferreira, Hermínia CEOS.PP / ISCAP / P. PORTO Instituto Politecnico do Porto Portugal Institute of Engineering of Porto P. PORTO Instituto Politecnico do Porto Porto Portugal

ISBN: (纸本)9789897583674

Massive Open Online Courses (MOOCs) are online courses with an unlimited number of participants and no entry requirements. Due to their massive and open nature, MOOCs have a high potential to offer access to education to millions of people worldwide. However, there are several challenges in MOOCs such as huge drop-out rates, improper automated assessments, diverse student engagement, and attention, etc. learning Analytics is "the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs" (Society of learning Analytics Research (SoLAR)) which can help us to contain such issues. this paper presents an initial analysis, using descriptive analytics, of the students’ activity in a MOOC for Mathematics. the analysis allowed the developers of the course to better understand some of the limitations and also some of the strengths of the course in order to continuously adapt it to the users’ needs and interests. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Students

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Predicting Bridge Damage During Earthquake Using Machine learning Algorithms

Predicting Bridge Damage During Earthquake Using Machine Lea...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Yash Garg Arpit Masih Utkarsh Sharma B. Tech CSE ASET Amity University Noida India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

the more the magnitude of the earthquake, the more is the damage. During an earthquake, the mobility of the soft soil gets augmented and because most of the bridges are built on soft soil, there are even more chances of the bridge behaving like a ship in the sea. the stability of the bridges is the most crucial task to avoid all disasters. Many bridges collapse during earthquake because their mobility, and sustainability cannot stand the magnitude of the earthquake. In this paper, we have proposed a method to predict whether a bridge will sustain damage or not after an earthquake by using factors like magnitude of the earthquake, distance to epicenter of the bridge, bridge type, material used to make bridge and many more, using many classification algorithms like Logistic Regression, Decision Tree, Random Forest, XGBoost, and KNN. this prediction in turn will help to improve bridge sustainability during an earthquake, which in turn will help in saving many lives.

关键词： Bridges Earthquakes Soil Sustainable development Task analysis Regression tree analysis Random forests

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An Innovative Deep-learning Algorithm for Supporting the Approximate Classification of Workloads in Big data Environments 20th

An Innovative Deep-Learning Algorithm for Supporting the App...

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

作者： Cuzzocrea, Alfredo Mumolo, Enzo Leung, Carson K. Grasso, Giorgio Mario Univ Calabria Arcavacata Di Rende Italy Univ Trieste Trieste Italy Univ Manitoba Winnipeg MB Canada Univ Messina Messina Italy

ISBN: (纸本)9783030336172;9783030336165

In this paper, we describe AppxDL, an algorithm for approximate classification of workloads of running processes in big data environments via deep learning (deep neural networks). the Deep Neural Network is trained with some workloads which belong to known categories (e.g., compiler, file compressor, etc...). Its purpose is to extract the type of workload from the executions of reference programs, so that a Neural Model of the workloads can be learned. When the learning phase is completed, the Deep Neural Network is available as Neural Model of the known workloads. We describe the AppxDL algorithm and we report and discuss some significant results we have achieved with it.

关键词： Workload classification Virtualized environment Deep learning

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Meta-learning Based Evolutionary Clustering Algorithm 20th

Meta-learning Based Evolutionary Clustering Algorithm

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

作者： Tomp, Dmitry Muravyov, Sergey Filchenkov, Andrey Parfenov, Vladimir ITMO Univ Machine Learning Lab 49 Kronverksky Pr St Petersburg 197101 Russia ITMO Univ Informat Technol & Programming Fac 49 Kronverksky Pr St Petersburg 197101 Russia

ISBN: (纸本)9783030336073;9783030336066

In this work, we address the hard clustering problem. We present a new clustering algorithm based on evolutionary computation searching a best partition with respect to a given quality measure. We present 32 partition transformation that are used as mutation operators. the algorithm is a (1 + 1) evolutionary strategy that selects a random mutation on each step from a subset of preselected mutation operators. Such selection is performed with a classifier trained to predict usefulness of each mutation for a given dataset. Comparison with state-of-the-art approach for automated clustering algorithm and hyperparameter selection shows the superiority of the proposed algorithm.

关键词： Clustering Evolutionary clustering Meta-learning Evolutionary computation

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Early Prediction of Sepsis using Machine learning

Early Prediction of Sepsis using Machine Learning

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Anuraag Shankar Mufaddal Diwan Snigdha Singh Husain Nahrpurawala Tanusri Bhowmick Pune Institute of Computer Technology Pune India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Sepsis is a fatal disease caused by infection. It has a significantly high mortality rate, particularly for patients in the ICU. the early and accurate detection of Sepsis is crucial as delayed treatment causes a sharp increase in the mortality rate. the proposed research aims to develop a classifier that accurately predicts Sepsis up to six hours before the clinical diagnosis of the disease. this is achieved using the patient's EMR, vital signs and demographics. the research shows several imputation techniques and proposes a new filling algorithm known as Mixed Filling. the main features contributing to the classifier's predictions have been described, thereby making the model more interpretable for medical personnel. Six models namely Random Forest, Logistic Regression, Light Gradient Boosting Machine, eXtreme Gradient Boosting, Neural Network and Long Short-Term Memory have been investigated for the classification of patients. the evaluation metrics that have been obtained are unprecedented and can be extremely useful for the timely and accurate prediction of Sepsis.

关键词： Measurement Prediction algorithms Boosting Filling Classification algorithms Random forests Diseases

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On the use of nonlinear normal modes for nonlinear reduced order modelling 11

On the use of nonlinear normal modes for nonlinear reduced o...

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11th international conference on Structural Dynamics, EURODYN 2020

作者： Simpson, thomas E. Dervilis, Nikolaos Chatzi, Eleni Institute of Structural Engineering Department of Civil Environmental and Geomatic Engineering ETH Zürich Stefano-Franscini Platz 5 Zürich8093 Switzerland Dynamics Research Group Department of Mechanical Engineering University of Sheffield SheffieldS1 3JD United Kingdom

ISBN: (纸本)9786188507210

In many areas of engineering, nonlinear numerical analysis is playing an increasingly important role in supporting the design and monitoring of structures. Whilst increasing computer resources have made such formerly prohibitive analyses possible, certain use cases such as uncertainty quantification and real time high-precision simulation remain computationally challenging. this motivates the development of reduced order modelling methods, which can reduce the computational toll of simulations relying on mechanistic principles. the majority of existing reduced order modelling techniques involve projection onto linear bases. Such methods are well established for linear systems but when considering nonlinear systems their application becomes more difficult. Targeted schemes for nonlinear systems are available, which involve the use of multiple linear reduction bases or the enrichment of traditional bases. these methods are however generally limited to weakly nonlinear systems. In this work, nonlinear normal modes (NNMs) are demonstrated as a possible invertible reduction basis for nonlinear systems. the extraction of NNMs from output only data using machine learning methods is demonstrated and a novel NNM-based reduced order modelling scheme introduced. the method is demonstrated on a simulated example of a nonlinear 20 degree-of-freedom (DOF) system. © 2020 European Association for Structural Dynamics. All rights reserved.

关键词： Nonlinear systems

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Applying item response theory in adaptive tutoring systems for thai language learners 11

Applying item response theory in adaptive tutoring systems f...

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11th Annual international conference on Knowledge and Smart Technology (KST)

作者： Rungrat, Saowalak Harfield, Antony Charoensiriwath, Supiya Naresuan Univ Dept Comp Sci & Informat Technol Tha Pho Phitsanulok Thailand Natl Elect & Comp Technol Ctr Khlong Nueng Thailand

ISBN: (纸本)9781538675120

Traditional computerized tutoring systems typically suffer from issues of mismatched content, where the difficulty, sequence and amount of content is not matched to the pace of the learner. Adaptive algorithms can overcome these problems by using data collected by the system to reactively deliver the most appropriate content to the learner. Item response theory (IRT) is a technique from computer aided assessment for estimating student ability and predicting the outcome of student responses. While it is used extensively for assessment, the application of IRT to tutoring systems is relatively new. the contribution of this paper is to propose an algorithm for applying IRT to tutoring systems. the algorithm is validated by considering its application on the practical problem of supporting children with language learning difficulties as part of the "KidArn" project.

关键词： Adaptive learning Item Response theory intelligent Tutoring Systems

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Short Interval Solar Power Prediction for Energy Harvesting with Low Computation Cost on Edge Computation Network

Short Interval Solar Power Prediction for Energy Harvesting ...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Yue Chen Xing Cheng Yanxin Yao Bei Wan School of Information and Communication Engineering Beijing Information Science and Technology University Beijng China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science & Technology University Advanced Equipment Intelligent Perception and Control Beijing International Cooperation Base for Science and Technology Beijing Information Science & Technology University

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

Solar energy forecasting methods including artificial neural network (ANN) are mostly for planning large or medium scale grid connected photovoltaic (PV) systems. the small prediction step will cause expensive computational cost with ANN. Using the concept of edge computing (EC), the paper proposes an effective short time step PV energy predicting model I based on ANN and wavelet transformation to reduce the computation costs. Sparse coefficients, which are tried by both different wavelet types and similar day classification, and a few forecast meteorological data are used as inputs to reduces the scale of ANN sharply. the simulation results demonstrate that, when the proposed method processes the whole year data, the proposed ANN model I has low computation complexity and superior precision.

关键词： Training Photovoltaic systems Computational modeling Artificial neural networks Solar energy Predictive models Wavelet coefficients

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Quality of Wikipedia Articles: Analyzing Features and Building a Ground Truth for Supervised Classification 11

Quality of Wikipedia Articles: Analyzing Features and Buildi...

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11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge Discovery and Information Retrieval (KDIR)

作者： Bassani, Elias Viviani, Marco Univ Milano Bicocca Dept Informat Syst & Commun Edificio U14-Viale Sarca I-20126 Milan Italy Consorzio IT Trasferimento Tecnol C2T Milan Italy

ISBN: (纸本)9789897583827

Wikipedia is nowadays one of the biggest online resources on which users rely as a source of information. the amount of collaboratively generated content that is sent to the online encyclopedia every day can let to the possible creation of low-quality articles (and, consequently, misinformation) if not properly monitored and revised. For this reason, in this paper, the problem of automatically assessing the quality of Wikipedia articles is considered. In particular, the focus is (i) on the analysis of groups of hand-crafted features that can be employed by supervised machine learning techniques to classify Wikipedia articles on qualitative bases, and (ii) on the analysis of some issues behind the construction of a suitable ground truth. Evaluations are performed, on the analyzed features and on a specifically built labeled dataset, by implementing different supervised classifiers based on distinct machine learning algorithms, which produced promising results.

关键词： data Quality Wikipedia Supervised Classification Feature Analysis Ground Truth Building

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