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检索条件"任意字段=16th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2015"

共 86 条记录，以下是31-40 订阅

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Improving Time Series Prediction via Modification of Dynamic Weighted Majority in Ensemble learning 19th

Improving Time Series Prediction via Modification of Dynamic...

引用

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

作者： Loderer, Marek Pavlik, Peter Rozinajova, Viera Slovak Univ Technol Bratislava Fac Informat & Informat Technol Ilkovicova 2 Bratislava 84104 Slovakia

ISBN: (纸本)9783030034931;9783030034924

In this paper, we explore how the modified Dynamic Weighted Majority (DWM) method of ensemble learning can enhance time series prediction. DWM approach was originally introduced as a method to combine predictions of multiple classifiers. In our approach, we propose its modification to solve the regression problems which are based on using differing features to further improve the accuracy of the ensemble. the proposed method is then tested in the domain of energy consumption forecasting.

关键词： Ensemble learning Dynamic weighted majority method Time series prediction

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A Distributed Approach to Flood Prediction Using a WSN and ML: A Comparative Study of ML Techniques in a WSN Deployed in Brazil 16th

A Distributed Approach to Flood Prediction Using a WSN and M...

引用

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

作者： Furquim, Gustavo Pessin, Gustavo Gomes, Pedro H. Mendiondo, Eduardo M. Ueyama, Jo Univ Sao Paulo Inst Math & Comp Sci ICMC Sao Carlos SP Brazil Vale Inst Technol Belem Para Brazil Univ So Calif ANRG Los Angeles CA USA Univ Sao Paulo Sao Carlos Sch Engn EESC Sao Carlos SP Brazil Minist Sci Technol & Innovat Ctr Monitoring & Early Warning Disasters Sao Carlos SP Brazil

ISBN: (纸本)9783319248349;9783319248332

Natural disasters (e.g. floods, landslides and tsunamis) are phenomena that occur in several countries and cause a great deal of damage, as well as a serious loss of life and materials. Although very often these events cannot be avoided, their environments can be monitored and thus predictions can be made about their likely occurrence so that their effects can be mitigated. One feasible way of carrying out this monitoring is through the use of wireless sensor networks (WSNs) since these disasters usually occur in hostile environments where there is a lack of adequate infrastructure. this article examines the most recent results obtained from the use of machine learning techniques (ML) and adopts a distributed approach to predict floods using a WSN deployed in Brazil to monitor urban rivers. It also conducts a comparative analysis of ML techniques (e.g. Artificial Neural Networks and Support Vector Machines) for the task of flood prediction and discusses the results obtained from each type of technique explored so far. Finally, in the discussion of the results, a suggestion is made about how to improve accuracy in forecasting floods by adopting a distributed approach, which is based on allying computing intelligence with WSNs.

关键词： Wireless sensor networks Machine learning Distributed systems Flash flood forecast

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Active S-quiz: An intelligent Educational System for Basic Knowledge learning by Question-Posing 16

Active S-quiz: An Intelligent Educational System for Basic K...

引用

16th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Hayashi, Toshihiro Nakagawa, Shimpei Kishimoto, Takuya Hirai, Yuji Ura, Kazuhiro Yaegashi, Rihito Murai, Hiroshi Tarumi, Hiroyuki Kagawa Univ 2217-20 Hayashi Cho Takamatsu Kagawa 7610396 Japan

ISBN: (纸本)9781479986767

e-learning is strongly expected to be used for self-learning. Focusing on e-learning systems designed to realize student's self-learning, it is not always said that those systems can achieve expected learning. Our research aim is to enhance learning effects with navigating students to ideal learning status by adding learning assistance functions by system initiative into existing e-learning systems. We have developed Self Study Material Contribution and Sharing System "S-Quiz" that realizes student's self-learning. S-Quiz can provide a learning environment wherein students can make educational materials and the share them for their self-learning. It has revealed that preparing the learning environment for question poising and sharing gives little impacts to active question posing by students, from the results of several trail experiment and use in real class of S-Quiz. We are currently developing an intelligent educational system called "Active S-Quiz" which has learning assistance functions by using both S-Quiz and partial system based on system initiative.

关键词： Free learning e-learning Question posing Basic knowledge learning Active S-Quiz

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SMOTE Algorithm Variations in Balancing data Streams 20th

SMOTE Algorithm Variations in Balancing Data Streams

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

作者： Gulowaty, Bogdan Ksieniewicz, Pawel Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wroclaw Poland

ISBN: (纸本)9783030336172;9783030336165

From one year to another, more and more vast amounts of data is being created in different fields of application. Great deal of those sources require real-time processing and analyzing, which leads to increased interest in streaming data classification field of machine learning. It is not rare, that many of those applications deal with somehow skewed or imbalanced data. In this paper, we analyze usage of smote oversampling algorithm variations in learning patterns from imbalanced data streams using different incremental learning ensemble algorithms.

关键词： data streams Imbalanced learning Synthetic oversampling Classifier ensembles

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Cost Sensitive Matrix Factorization for Face Recognition 1

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Wan, Jianwu Yang, Ming Wang, Hongyuan Changzhou Univ Sch Informat Sci & Engn Changzhou Peoples R China Nanjing Normal Univ Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

In this paper, we propose a cost sensitive matrix factorization (CSMF) for face recognition. To make the face representation cost sensitive, CSMF adopts a more flexible feature embedding strategy. It contains two main steps: (1) matrix factorization for the learning of latent semantic representation and (2) cost sensitive latent semantic regression. In this way, the face images are embedded into their label space with the misclassification loss minimized. the experimental results on Extended Yale B and ORL demonstrate its effectiveness.

关键词： Cost sensitive learning Matrix factorization Face recognition Latent semantic regression

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An Approximation to Deep learning Touristic-Related Time Series Forecasting 19th

An Approximation to Deep Learning Touristic-Related Time Ser...

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

作者： Trujillo Viedma, Daniel Rivera Rivas, Antonio Jesus Charte Ojeda, Francisco del Jesus Diaz, Maria Jose Univ Jaen Dept Comp Sci Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain

ISBN: (纸本)9783030034931;9783030034924

Tourism is one of the biggest economic activities around the world. this means that an adequate planning of existing resources becomes crucial. Precise demand-related forecasting greatly improves this planning. Deep learning models are showing an greatly improvement on time-series forecasting, particularly the LSTM, which is designed for this kind of tasks. this article introduces the touristic time-series forecasting using LSTM, and compares its accuracy against well known models RandomForest and ARIMA. Our results shows that new LSTM models achieve the best accuracy.

关键词： LSTM ARIMA Time series forecasting

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Railway Incident Ranking with Machine learning 16

Railway Incident Ranking with Machine Learning

引用

16th IEEE international conference on Machine learning and Applications (ICMLA)

作者： Bikov, Evgeni Boyko, Pavel Sokolov, Evgeny Yarotsky, Dmitry Skolkovo Inst Sci & Technol Moscow Russia JSC Russian Railways Moscow Russia

ISBN: (纸本)9781538614174

Modern railway networks include thousands of failure registration devices, and prompt response to detected failures is critical to normal network operation. However, a large share of produced alerts may be formed by false alarms associated with maintenance or faulty diagnostics, thus hindering the processing of actual failures. It is therefore very desirable to perform fast automated intelligent ranking of incidents before they are analyzed by human operators. In this paper we describe a machine-learning-based incident ranking model that we have developed and deployed at the Moscow Railway network (a large network with 500+ stations). the model estimates the probability of failure using multiple features of the incident at hand. the model was constructed using the XGBoost library and a database of 5 million historical incidents. the model shows high accuracy (AUC 0.901) in the deployment environment.

关键词： Predictive models Rail transportation databases Maintenance engineering Feature extraction data models Libraries

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Imbalanced data Classification Based on Feature Selection Techniques 19th

Imbalanced Data Classification Based on Feature Selection Te...

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

作者： Ksieniewicz, Pawel Wozniak, Michal Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wroclaw Poland

ISBN: (纸本)9783030034962;9783030034955

the difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canonical classifiers display strongly biased performance on imbalanced datasets. In this work a novel classifier ensemble forming technique for imbalanced datasets is presented. On the one hand it takes into consideration selected features used for training individual classifiers, on the other hand it ensures an appropriate diversity of a classifier ensemble. the proposed method was tested on the basis of the computer experiments carried out on the several benchmark datasets. their results seem to confirm the usefulness of the proposed concept.

关键词： Machine learning Classification Imbalanced data Feature selection Random search

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Design and Evaluation of automated Scoring Java Programming Assignments 16

Design and Evaluation of Automated Scoring Java Programming ...

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16th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Akahane, Yuki Kitaya, Hiroki Inoue, Ushio Tokyo Denki Univ Grad Sch Engn Tokyo Japan

ISBN: (纸本)9781479986767

this paper presents a web-based automatic scoring system for Java programming assignments, and reports evaluation results in an actual programming course. the system receives Java application programs submitted by students and returns the test results immediately. the test consists of compiler check, JUnit test, and result test. the result test is very useful for assignments in elementary programming courses, because a typical program is composed of only a main method that reads/writes data from/to the standard input/output devices. the system was used and evaluated in an actual course of our university. We confirmed that the system is very helpful for students to improve their programming skills. Especially, many students noticed and corrected their mistakes by repeating submission of their programs again several times.

关键词： automated scoring Java programming assignments web systems e-learning systems

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intelligent Wristbands for the Automatic Detection of Emotional States for the Elderly 19th

Intelligent Wristbands for the Automatic Detection of Emotio...

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

作者： Rincon, Jaime A. Costa, Angelo Novais, Paulo Julian, Vicente Carrascosa, Carlos Univ Politecn Valencia D Sistemas Informat & Comp Valencia Spain Univ Minho Algoritmi Ctr Dept Informat Braga Portugal

ISBN: (纸本)9783030034931;9783030034924

Over the last few years, research on computational intelligence is being conducted to detect emotional states of people. this paper proposes the use of intelligent wristbands for the automatic detection of emotional states to develop an application which allows to monitor older people in order to improve their quality of life. the paper describes the hardware design and the cognitive module that allows the recognition of the emotional states. the proposed wristband also integrates a camera that improves the emotion detection.

关键词： Wearable devices Emotional models Ambient assisted living

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