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

共 7157 条记录，以下是4291-4300 订阅

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A Hybrid intelligent Agent for Notification of Users Distracted by Mobile Phones in an Urban Environment 11

A Hybrid Intelligent Agent for Notification of Users Distrac...

引用

11th international conference on Agents and Artificial Intelligence (ICAART)

作者： Gelaim, thiago A. Langer, Gabriel A. Santos, Elder R. Silveira, Ricardo A. O'Hare, John Kendrick, Paul Fazenda, Bruno M. Univ Fed Santa Catarina Dept Informat & Stat Grad Program Comp Sci PPGCC Santa Catarina Brazil Univ Salford coust Res Ctr Salford M5 4WT Lancs England

ISBN: (纸本)9789897583506

Mobile devices are now ubiquitous in daily life and the number of activities that can be performed using them is continually growing. this implies increased attention being placed on the device and diverted away from events taking place in the surrounding environment. the impact of using a smartphone on pedestrians in the vicinity of urban traffic has been investigated in a multimodal, fully immersive, virtual reality environment. Based on experimental data collected, an agent to improve the attention of users in such situations has been developed. the proposed agent uses explicit, contextual data from experimental conditions to feed a statistical learning model. the agent's decision process is aimed at notifying users when they become unaware of critical events in their surroundings.

关键词： Situational Awareness Bayesian Networks Predictive Model

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Do You Really Follow them? Automatic Detection of Credulous Twitter Users 20th

Do You Really Follow Them? Automatic Detection of Credulous ...

引用

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

作者： Balestrucci, Alessandro De Nicola, Rocco Petrocchi, Marinella Trubiani, Catia Gran Sasso Sci Inst Laquila Italy IMT Sch Adv Studies Lucca Italy CNR Inst Informat & Telemat IIT Pisa Italy

ISBN: (纸本)9783030336073;9783030336066

Online Social Media represent a pervasive source of information able to reach a huge audience. Sadly, recent studies show how online social bots (automated, often malicious accounts, populating social networks and mimicking genuine users) are able to amplify the dissemination of (fake) information by orders of magnitude. Using Twitter as a benchmark, in this work we focus on what we define credulous users, i.e., human-operated accounts with a high percentage of bots among their followings. Being more exposed to the harmful activities of social bots, credulous users may run the risk of being more influenced than other users;even worse, although unknowingly, they could become spreaders of misleading information (e.g., by retweeting bots). We design and develop a supervised classifier to automatically recognize credulous users. the best tested configuration achieves an accuracy of 93.27% and AUC-ROC of 0.93, thus leading to positive and encouraging results.

关键词： Twitter Humans-bots interactions Gullibility Disinformation Social networks data Mining Supervised learning

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Class Representation Networks for Few-Shot learning

Class Representation Networks for Few-Shot Learning

引用

IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Yongping Zhai Junhua Wang College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha Hunan Province China Institute of Intelligent Information Guangdong University of Foreign Studies South China Business College Guangzhou Guangdong Province China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

In this paper, we proposed a novel network referred as Class Representation Networks (CRNs) to solve the few-shot learning problems. In the proposed CRNs, a high-quality class representation is learned by training a set-based neural network. In addition, a network with fully connected layers was constructed for learning distance metric instead of using a predefined distance metric. Compared with recent methods for few-shot learning, our network achieves state-of-the-art performance for few-shot learning. Extensive experiments on three benchmark datasets validate the effectiveness of our proposed model.

关键词： Training Measurement conferences Neural networks Benchmark testing Software engineering

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Human Gait Patterns Classification based on MEMS data using Unsupervised and Supervised learning Algorithms 11

Human Gait Patterns Classification based on MEMS Data using ...

引用

11th international conference on Knowledge and Systems engineering (KSE)

作者： Nguyen, My N. Zao, John Kar-Kin thanh Hai Nguyen Can Tho Univ Coll ICT Can Tho Vietnam Natl Chiao Tung Univ Pervas Embedded Syst Lab Hsinchu Taiwan

ISBN: (纸本)9781728130033

With the proliferation of smartphones and wearable devices having Micro-Electro-Mechanical Systems (MEMS) sensors built in, data samples of linear acceleration and angular velocity can be collected almost anytime anywhere. these motion data can be used to identify various types of human motions and to detect the anomaly of individuals movements. this work presents attempts to use the unsupervised Affinity Propagation (AP) clustering algorithm and the supervised Support Vector Machine (SVM) classification algorithm to identify four types of human gait motions: walking, jogging, climbing upstairs and downstairs. Features of three-dimensional linear acceleration that can enable the algorithms to identify these motion types correctly were selected by analyzing the variation of the feature values among different motion types. Efficacy of Affinity Propagation (AP), Linear and Non-linear Support Vector Machine (SVM) algorithms were also studied by comparing their ratios of correct, false positive, false negative and F1 score classification. this preliminary study demonstrated Linear SVM achieved the best performance, followed by Affinity Propagation. Quite surprisingly, Non-linear SVM appeared to be inferior to the other two algorithms.

关键词： human gait motions Affinity Propagation motion sensors Support Vector Machine

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Searching Optimal Values of Identification and Controller Design Horizon Lengths, and Regularization Parameters in NARMA Based Online learning Controller Design 11

Searching Optimal Values of Identification and Controller De...

引用

11th international conference on Electrical and Electronics engineering (ELECO)

作者： Toprak, Tugce Sahin, Savas Soydemir, M. Ugur Bulucu, Parvin Kocaoglu, Aykut Guzelis, Cuneyt Dokuz Eylul Univ Grad Sch Nat & Appl Sci Izmir Turkey Izmir Katip Celebi Univ Elect & Elect Engn Dept Izmir Turkey Dokuz Eylul Univ Vocat Sch Elect Program Izmir Turkey Yasar Univ Elect & Elect Engn Dept Izmir Turkey

ISBN: (纸本)9786050112757

this paper presents an analysis on searching the optimal values of the system identification and tracking window lengths, and regularization parameter for the online learning NARMA controller algorithm. Both window lengths and regularization parameter are generally determined with exhaustive searches by researchers. Although the estimation of plant and controller parameters plays the essential role in online learning control algorithms, using non-optimal values of the window lengths and regularization parameter may deteriorate badly the estimation and so the performance of the controller. In the paper, the effects of the window lengths and the regularization parameter on the tracking performance of the NARMA based online learning controller are analyzed with a search method. the considered NARMA based online learning control method is performed on a rotary inverted pendulum model. While the effect of the regularization parameter is examined in the batch mode, the effects of identification and tracking error window lengths are studied for the online mode of the controller learning algorithm. the developed search method can provide the optimum values of the plant identification and tracking horizon lengths, and regularization parameter when a sufficiently large class of possible input, output and reference signals are taken into account in the search. the presented study may be extended, as future research in the direction of developing intelligent control systems, by determining the horizon window lengths and regularization parameter, in an automatic way, with efficient learning algorithms.

关键词： learning algorithms

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A Metric Framework for Quantifying data Concentration 20th

A Metric Framework for Quantifying Data Concentration

引用

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

作者： Mitic, Peter Santander UK Risk London NW1 3AN England UCL Dept Comp Sci Gower St London WC1E 6BT England Lab Excellence Regulat Financiere LabEx ReFi Paris France

ISBN: (纸本)9783030336172;9783030336165

Poor performance of artificial neural nets when applied to credit-related classification problems is investigated and contrasted with logistic regression classification. We propose that artificial neural nets are less successful because of the inherent structure of credit data rather than any particular aspect of the neural net structure. three metrics are developed to rationalise the result with such data. the metrics exploit the distributional properties of the data to rationalise neural net results. they are used in conjunction with a variant of an established concentration measure that differentiates between class characteristics. the results are contrasted with those obtained using random data, and are compared with results obtained using logistic regression. We find, in general agreement with previous studies, that logistic regressions out-perform neural nets in the majority of cases. An approximate decision criterion is developed in order to explain adverse results.

关键词： Copula Hypersphere Cluster Herfindahl-Hirschman HHI Credit Concentration Decision criterion Tensorflow Neural Net

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Automatic Damage Detection of Fasteners in Overhaul Processes 15

Automatic Damage Detection of Fasteners in Overhaul Processe...

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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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Deep learning and Sensor Fusion Methods for Studying Gait Changes Under Cognitive Load in Males and Females 20th

Deep Learning and Sensor Fusion Methods for Studying Gait Ch...

引用

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

作者： Alharthi, Abdullah S. Ozanyan, Krikor B. Univ Manchester Sch Elect & Elect Engn Sackville St Manchester M1 3BU Lancs England

ISBN: (纸本)9783030336073;9783030336066

Human gait is the manner of walking in people. It is influenced by weight, age, health condition or the interaction with the surrounding environment. In this work, we study gait changes under cognitive load in healthy males and females, using machine learning methods. A deep learning model with multi-processing pipelining and back propagation techniques, is proposed for cognitive load gait analysis. the IMAGiMAT floor system enabling sensor fusion from plastic optical fiber (POF) elements, is utilized to record gait raw data on spatiotemporal ground reaction force (GRF). A deep parallel Convolutional Neural Network (CNN) is engineered for POF sensors fusion, and gait GRF classification. the Layer-Wise Relevance Propagation (LRP), is applied to reveal which gait events are relevant towards informing the parallel CNN prediction. the CNN differentiates between males and females with 95% weighted average precision, and cognitive load gait classification with 93% weighted average precision. these findings present a new hypothesis, whereas larger dataset holds promise for human activity analysis.

关键词： Convolutional Neural Networks (CNN) Cognitive load gait Ground Reaction Force (GRF) Layer-Wise Relevance Propagation (LRP)

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A Multiple-Replica Provable data Possession Algorithm Based on Branch Authentication Tree

A Multiple-Replica Provable Data Possession Algorithm Based ...

引用

IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Sichen Dong Jian Jiao Shuyu Li School of Computer Science Shaanxi Normal University Xi'an China

the following topics are dealt with: learning (artificial intelligence); neural nets; feature extraction; pattern classification; convolutional neural nets; computer network security; security of data; recurrent neura... 详细信息

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

关键词： Heuristic algorithms data integrity conferences Software algorithms Authentication Security Software engineering

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threat Identification in Humanitarian Demining Using Machine learning and Spectroscopic Metal Detection 20th

Threat Identification in Humanitarian Demining Using Machine...

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

作者： van Verre, Wouter Ozdeger, Toykan Gupta, Ananya Podd, Frank J. W. Peyton, Anthony J. Univ Manchester Oxford Rd Manchester M13 9PL Lancs England

ISBN: (纸本)9783030336073;9783030336066

the detection of buried minimum-metal anti-personnel landmines is a time-consuming problem, due to the high false alarm rate (FAR) arising from metallic clutter typically found in minefields. Magnetic induction spectroscopy (MIS) offers a potential way to reduce the FAR by classifying the metallic objects into threat and non-threat categories, based on their spectroscopic signatures. A new algorithm for threat identification for MIS sensors, based on a fully-connected artificial neural network (ANN), is proposed in this paper, and compared against a classifier based on Support Vector Machines (SVM). the results demonstrate that MIS is a potentially viable option for the reduction of false alarms in humanitarian demining. It is also shown that the ANN outperforms the SVM-based approach for threat objects containing minimal amounts of metal.

关键词： Magnetic induction spectroscopy Machine learning Landmine detection

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