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检索条件"任意字段=15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014"

共 118 条记录，以下是41-50 订阅

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DIVACE: Diverse and accurate ensemble learning algorithm

引用

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

作者： Chandra, A Yao, X Univ Birmingham CERCIA Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)3540228810

In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. there exists a tradeoff as to what should be the optimal measures of diversity and accuracy. the aim of this paper is to address this issue. We propose the DIVACE algorithm which tries to produce an ensemble as it searches for the optimum point on the diversity-accuracy curve. the DIVACE algorithm formulates the ensemble learning problem as a multi-objective problem explicitly.

关键词： learning algorithms

来源：评论

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Reduction of Signal Strength data for Fingerprinting-Based Indoor Positioning 16th

Reduction of Signal Strength Data for Fingerprinting-Based I...

引用

16th 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)9783319248349;9783319248332

Indoor Positioning Services (IPS) estimate the location of devices, frequently being mobile terminals, in a building. Many IPS algorithms rely on the Received Signal Strength (RSS) of radio signals observed at a terminal. However, these signals are noisy due to both the impact of the surrounding environment such as presence of other persons, and limited accuracy of strength measurements. Hence, the question arises whether raw RSS data can undergo transformation preserving its information content, but reducing the data and increasing the resilience of the algorithms to inherent noisiness of raw RSS data. this work evaluates the use of binning applied to RSS data and proposes the way the reduction of RSS data can be attained. Results show that the proposed way of using RSS binning causes little or no positioning accuracy degradation. Still, it yields significant RSS data reduction. Interestingly, in some cases data reduction results even in accuracy gains.

关键词： Received signal strength Binning Fingerprinting

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Automatic Damage Detection of Fasteners in Overhaul Processes 15

Automatic Damage Detection of Fasteners in Overhaul Processe...

引用

15th IEEE international conference on Automation Science and engineering (IEEE CASE)

作者： Taheritanjani, Sajjad Schoenfeld, Ralf Bruegge, Bernd Tech Univ Munich Dept Informat Garching Germany SAP Innovat Serv Dev Consultant IoT & Data Sci Walldorf Baden Germany

ISBN: (纸本)9781728103563

Commercial aircraft engines have a maintenance process that includes overhauling approximately every six years. Hundreds of different components must be disassembled, checked, repaired (if necessary), and then reassembled. this includes undoing fasteners, cleaning, checking, refitting, and tightening them. Prior to refitting the fasteners, they must be checked for damages. In this paper, we propose an automatic damage inspection of the fasteners, using computer vision and machine learning. We built a setup to automatically record and preprocess the data and compared multiple supervised and unsupervised machine learning models for detecting damages of 12 different fasteners. Using our automatic approach, we can determine the type of fastener, its status (damaged or intact) and visualize the anomalies to aid the understanding of the decisions of the automatic detection. this can be the first step towards a fully automated fastener damage detection in overhaul processes.

关键词： Computer Vision for Automation automated Damage Detection and Inspection Fasteners Damage Detection Overhaul Processes

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Triaxial Accelerometer Located on the Wrist for Elderly People's Fall Detection 17th

Triaxial Accelerometer Located on the Wrist for Elderly Peop...

引用

17th international conference on intelligent data engineering and automated learning (ideal)

作者： Collado Villaverde, Armando R-Moreno, Maria D. Barrero, David F. Rodriguez, Daniel Univ Alcala Dept Automat Crta Madrid Barcelona Madrid Spain Univ Alcala Dept Ciencias Computac Crta Madrid Barcelona Alcala De Henares Spain

ISBN: (纸本)9783319462578;9783319462561

the loss of motor function in the elderly makes this population group prone to accidental falls. Actually, falls are one of the most notable concerns in elder care. Not surprisingly, there are several technical solutions to detect falls, however, none of them has achieved great acceptance. the popularization of smartwatches provides a promising tool to address this problem. In this work, we present a solution that applies machine learning techniques to process the output of a smartwatch accelerometer, being able to detect a fall event with high accuracy. To this end, we simulated the two most common types of falls in elders, gathering acceleration data from the wrist, then applied that data to train two classifiers. the results show high accuracy and robust classifiers able to detect falls.

关键词： Fall detection Accelerometer Machine learning Classification Supervised learning Care for the elderly

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Predicting learning Effect by Learner's Behavior in MOOCs 18th

Predicting Learning Effect by Learner's Behavior in MOOCs

引用

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

作者： Tian, Ye Wen, Yimin Yi, Xinhe Yang, Xi Miao, Yuqing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur 1 Jinji Rd Guilin 541004 Peoples R China Guangxi Dist Educ Dept 69 Zhuxi Ave Nanning 5530021 Peoples R China

ISBN: (纸本)9783319689357;9783319689340

With the fast development of MOOCs in recent years, more and more people start to take MOOCs to perfect themselves. However, there exist high dropout rate and low passing rate of examination in many courses. So it is very important to predict learners' learning effect exactly. For learners who predicted good learning effect, teachers can impose intervention to help these learners to stick to the end of courses, while for predicted bad learning effect, teachers can take measures to help these learners to study harder to improve their learning. In this paper, we first analyze learners' learning behavior data to explore the differences among learners with different categories, then a cascade prediction model is proposed to predict whether a learner can earn certificate in a course. Experiments conducted on a real-world dataset illustrated the effectiveness of the proposed model.

关键词： MOOCs learning effect learning behavior Prediction model Education data mining

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Research on Overload Monitoring Technology for Bridges Based on Fast DIC and Deep learning 15

Research on Overload Monitoring Technology for Bridges Based...

引用

15th international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Li, Binghai Niu, Ziyang Tian, Hua Li, Daping Lang, Hongbo Zhao, Qingxiang Yan, Banfu Xiao, Zimu China Construction Fifth Engineering Bureau Co. Ltd. Changsha China School of Civil Engineering Hunan University Hunan China

ISBN: (纸本)9798350326178

Combined with a deep learning-based overloaded vehicle information capture and recognition method, the captured information from bridge surface images is used to establish a mapping relationship between real-time monitoring data of mid-span deflection and spatiotemporal data of passing vehicles, realizing the collection and analysis of overloaded vehicle data during bridge operation. Experimental results show that the bridge overloading monitoring method based on fast DIC and deep learning has achieved ideal results in the bridge overloading monitoring scene, and can provide data basis for road and bridge maintenance management work, with certain engineering application value. © 2023 IEEE.

关键词： Bridges

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automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

引用

15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. these convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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Continuous Motion Recognition Using Multiple Time Constant Recurrent Neural Network with a Deep Network Model

Continuous Motion Recognition Using Multiple Time Constant R...

引用

14th international conference on intelligent data engineering and automated learning (ideal)

作者： Yu, Zhibin Lee, Minho Kyungpook Natl Univ Sch Elect Engn Taegu 702701 South Korea

ISBN: (纸本)9783642412783;9783642412776

Multiple timescale recurrent neural network (MTRNN) model is a useful tool to model a continuous signal for a dynamic task such as human action recognition. Different setting of initial states in the MTRNN brings us convenience to predict multiple signals using the same network model. On the contrary, optimal switching for suitable initial states in the slow context unit of the MTRNN becomes critical condition to achieve desired multiple dynamic tasks. In this paper, we propose a hybrid neural network model combining the MTRNN with a deep learning neural network (DN), which is to overcome the problem related to the initial state setting in the MTRNN. the DN together with MTRNN generates a suitable initial state for the slow context units in the MTRNN according to automatically detected situation change. We apply our approach to 20 motion skeleton units, which is obtained by KINECT, to construct three kinds of human motion sequences. the results show that the proposed method is able to recognize various motions using proper initial state information in a real-time procedure.

关键词： Motion recognition MTRNN Classification Deep learning neural network hybrid neural network

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the effect of missing wind speed data on wind power estimation

引用

8th international conference on intelligent data engineering and automated learning

作者： Hocaoglu, Fatih Onur Kurban, Mehmet Anadolu Univ Dept Elect & Elect Eng Eskisehir Turkey

ISBN: (纸本)9783540772255

In this paper, the effect of possible missing data on wind power estimation is examined. One-month wind speed data obtained from wind and solar observation station which is constructed at Iki Ey- Campus of Anadolu University is used. A closed correlation is found between consecutive wind speed data that are collected for a period of 15 second. A very short time wind speed forecasting model is built by using two-input and one-output Adaptive Neuro Fuzzy Inference System (ANFIS). First, some randomly selected data from whole data are discarded. Second, 10%, 20% and 30% of all data which are randomly selected from a predefined interval (3-6 m/sec) are discarded and discarded data, are forecasted. Finally, the data are fitted to Weibull distribution, Weibull distribution parameters are obtained and wind powers are estimated for all cases. the results show that the missing data has a significant effect on wind power estimation and must be taken into account in wind studies. Furthermore, it is concluded that ANFIS is a convenient tool for this kind of prediction.

关键词： Wind power

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Personalized news reading via hybrid learning

引用

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

作者： Chen, K Yeung, S Univ Manchester Sch Informat Manchester M60 1QD Lancs England Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)3540228810

In this paper, we present a personalized news reading prototype where latest news articles published by various on-line news providers are automatically collected, categorized and ranked in light of a user's habits or interests. Moreover, our system can adapt itself towards a better performance. In order to develop such an adaptive system, we proposed a hybrid learning strategy;supervised learning is used to create an initial system configuration based on user's feedbacks during registration, while an unsupervised learning scheme gradually updates the configuration by tracing the user's behaviors as the system is being used. Simulation results demonstrate satisfactory performance.

关键词： Artificial intelligence

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