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检索条件"任意字段=19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018"

共 134 条记录，以下是21-30 订阅

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On Application of learning to Rank for Assets Management: Warehouses Ranking 19th

On Application of Learning to Rank for Assets Management: Wa...

引用

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

作者： Pongpech, Worapol Alex NIDA Fac Appl Stat Bangkok 10240 Thailand

ISBN: (纸本)9783030034931;9783030034924

the number of connected Internet of things devices is forecast to grow to more than 31 billion globally in 2018. Many of these devices are/will be connecting to physical assets, and data collected from these devices can be used to model, manage, or describe these assets. We are motivated by utilizing these abundant data for assets' resources requirements ranking. Multiple Criteria Decision Analysis, MCDA, has often been utilized in finding ranking by computing on collected data and predefined criteria. However, changes in asset's environment have a direct impact on its resource requirements ranking and decisions on ranking must be constantly revised. this is a repetitive process where managers require to repetitively adjusting the decisions on the ranking. With machine learning, such repetitiveness can be heavily minimized by teaching machines how to rank not by instruction but rather by examples of the task being done. In this paper, we present Learn to Rank, LTR, machine learning framework in conjunction with MCDA for warehouse resources requirements ranking application. A framework for smart contract renegotiate resources allocation ranking for each asset on the blockchains is also discussed.

关键词： IoT Machine learning Learn to Rank Multiple criteria decision analysis Resources allocation Smart contract

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CCTV Image Sequence Generation and Modeling Method for Video Anomaly Detection Using Generative Adversarial Network 19th

CCTV Image Sequence Generation and Modeling Method for Video...

引用

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

作者： Shin, Wonsup Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030034931;9783030034924

Video anomaly detection is one of the most attractive problem in various fields likes computer vision. In this paper, we propose a VAD classifier modeling method that learns in a supervised learning manner. the basic idea is to solve the problem of labeled data shortage through transfer learning. the key idea is to create an underlying model of transfer learning through the GAN of discriminator. We solved this problem by proposing a GAN model consisting of a generator that generates video sequences and a discriminator that follows LRCN structure. As a result of the experiment, the VAD classifier learned through GAN-based transfer learning obtained higher accuracy and recall than the pure LRCN classifier and other machine learning methods. Additionally, we demonstrated that the generator be able to stably generate the image similar to the actual data as the learning progressed. To the best of our knowledge, this paper is the first case to solve the VAD problem using the GAN model and the supervised learning manner.

关键词： Autonomous CCTV surveillance Video anomaly detection Transfer learning Supervised learning Generative adversarial network

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Anomaly Detection in Spatial Layer Models of Autonomous Agents 1

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

作者： Kiermeier, Marie Feld, Sebastian Phan, thomy Linnhoff-Popien, Claudia Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

For describing the complete state of complex environments with multiple mobile and autonomous agents, spatial layer models (SLM) are popular data structures. these models consist of several planes describing the spatial structure of selected features. those SLM can directly be used for deep reinforcement learning tasks. However, detecting anomalies in such SLM poses two major challenges: the state space explosion in such settings and the spatial relations between the features. In this paper, we present a method for anomaly detection in SLM which solves both challenges by first extracting significant sub-patterns from training data and storing them in a dictionary. Afterwards, the entries of this dictionary are used for reconstructing SLM, which have to be validated. the resulting covering rate is an indicator for the (ab)normality of the given SLM. We show the applicability of our approach for a simple multi-agent scenario, and more complex smart factory scenarios with autonomous agents.

关键词： Anomaly detection Autonomous agents Spatial data

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Exploring the Perceived Usefulness and Attitude Towards Using Tesys e-learning Platform 19th

Exploring the Perceived Usefulness and Attitude Towards Usin...

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

作者： Popescu, Paul-Stefan Ionascu, Costel Mihaescu, Marian Cristian Univ Craiova Fac Automat Comp & Elect Str Decebal 107 Craiova Romania Univ Craiova Fac Econ & Business Adm Str AI Cuza 13 Craiova Romania

ISBN: (纸本)9783030034931;9783030034924

In this paper we present a study that aims at exploring two components of the Technology Acceptance Model. As an evaluation environment, we used Tesys e-learning platform, R programming language for data processing, and Google Forms for data collection. the study intended to explore if there are significant differences between genders regarding the perceived usefulness and attitude towards evaluation using online educational environment. the questionnaire exploring the abovementioned components was applied to a group of full-time students that have never used e-learning platforms. the results showed that the questionnaire was reliable and there is no cross-gender difference regarding students' perceived usefulness of e-learning platforms and their attitude towards using them. the overall feedback received from each of the explored items is positive with a grade above four on a five Likert scale.

关键词： Interface evaluation e-learning data analysis HCI

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Reusable Big data System for Industrial data Mining - A Case Study on Anomaly Detection in Chemical Plants 19th

Reusable Big Data System for Industrial Data Mining - A Case...

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

作者： Borrison, Reuben Kloepper, Benjamin Chioua, Moncef Dix, Marcel Sprick, Barbara ABB Corp Res Ctr Germany Ladenburg Germany SRH Hsch Heidelberg Heidelberg Germany

ISBN: (纸本)9783030034931;9783030034924

Industrial data mining projects in general and big data mining projects in particular suffer from long project execution. the resulting high costs render many interesting use cases otherwise economically unattractive. this contribution shows on the example of anomaly detection for process plants, how the major obstacles - namely the inefficient development tools for Big data Frameworks like Apache Hadoop and Spark and the lack of reuse of software artifacts across different projects can be overcome. this is achieved by selecting an application case that shares considerable commonalities across different projects and providing a supported project workflow implemented in a scalable and extensible big data architecture.

关键词： Big data data mining Software architecture

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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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Single-Class Bankruptcy Prediction Based on the data from Annual Reports 19th

Single-Class Bankruptcy Prediction Based on the Data from An...

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

作者： Drotar, Peter Gnip, Peter Zoricak, Martin Gazda, Vladimir Tech Univ Kosice Fac Elect Engn & Informat Letna 9 Kosice Slovakia Tech Univ Kosice Fac Econ Nemcovej 32 Kosice Slovakia

ISBN: (纸本)9783030034931;9783030034924

the companies involved in all areas of the business and industry can due to the unfavourable financial situation or inappropriate investments face financial problems resulting in bankruptcy of the company. the ability to foresee imminent bankruptcy helps managers and stock holders to take the corrective actions. In this paper, we analyze annual reports of thousands of limited liability companies and propose the bankruptcy prediction model. the available dataset is strongly imbalanced that corresponds to the real-world situation where bankrupt companies constitute only a small fraction of all companies. the proposed model is based on single-class least-squares anomaly detection classifier achieving as high as 91% prediction accuracy.

关键词： Anomaly detection Novelty detection Imbalanced learning Bankruptcy Least-squares anomaly detection

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Semi-supervised learning to Reduce data Needs of Indoor Positioning Models 19th

Semi-supervised Learning to Reduce Data Needs of Indoor Posi...

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

作者： Grzenda, Maciej Warsaw Univ Technol Fac Math & Informat Sci Ul Koszykowa 75 PL-00662 Warsaw Poland

ISBN: (纸本)9783030034962;9783030034955

Indoor positioning systems answer the need for ubiquitous localisation systems. Frequently, indoor positioning relies on machine learning models developed based on the training data composed of WiFi received signal strength (RSS) vectors observed in different indoor locations. However, this requires expensive collection of RSS vectors in precisely measured locations. In this study, we propose a semi-supervised method, which can reduce the volume of expensive labelled training data and exploit the availability of unlabelled signal strength measurements. the method relies, inter alia, on the measures of similarity among nearest neighbours of unlabelled vectors. Tests performed with a number of testbed areas confirm that the method improves the accuracy of random forest models used to estimate indoor location of mobile terminals.

关键词： Semi-supervised learning Fingerprinting Indoor positioning

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Instance-Based Stacked Generalization for Transfer learning 19th

Instance-Based Stacked Generalization for Transfer Learning

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

作者： Baghoussi, Yassine Mendes-Moreira, Joao LIAAD INESC TEC Porto Portugal Univ Porto Fac Sci Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783030034931;9783030034924

We present a method for improving the prediction accuracy using multiple predictive algorithms. Several techniques have been developed to tackle this issue such as bagging, boosting and stacking. In contrary to the first two that, usually, generate homogeneous ensembles of classifiers, stacking techniques have demonstrated success using heterogeneous ensembles. In our method, we adopt the stacking mechanism. Several models are generated using different learning algorithms. Forward stepwise selection is implemented to link each instance to its appropriate learning model. Experiments with three datasets benchmarked with four learning schemes show that this novel method improves prediction accuracy and can serve as a bridge to transfer knowledge between tasks given the same feature space but different data distributions.

关键词： Ensemble learning Stacking Transfer learning Operational planning

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Machine learning Methods Based Preprocessing to Improve Categorical data Classification 19th

Machine Learning Methods Based Preprocessing to Improve Cate...

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

作者： Ruiz-Chavez, Zoila Salvador-Meneses, Jaime Garcia-Rodriguez, Jose Univ Cent Ecuador Ciudadela Univ Quito Ecuador Univ Alicante Ap 99 Alicante 03080 Spain

ISBN: (纸本)9783030034931;9783030034924

the following study is aimed at dealing with large volumes of data whose main characteristic is to contain a high number of variables, most of which are categorical in nature. In the knowledge extraction process, Knowledge Discovery in databases (KDD), it is very common to deal with a stage of data pre-processing and dimensionality reduction. A key part of extracting information is having high quality data. this paper proposes the use of the Pairwise and Listwise methods as part of the dimensionality reduction process, when there is a high level of missing values present in one or more variables. As part of the preprocessing, we generate n-clusters using Kohonen Self-Organizing Maps (SOM) algorithm with H2O on R. A comparison of the performance and accuracy of classification algorithms is made with the complete subdata set and the algorithms are applied to each cluster. As a case study, we analyzed the characteristics that influence the level of schooling of women of childbearing age.

关键词： Machine learning Classification Clustering Missing data

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