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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 182 条记录，以下是111-120 订阅

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Cluster-based visualisation of marketing data

引用

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

作者： Lisboa, Paulo J.G. Patel, Shail School of Computing and Mathematical Sciences John Moores University LiverpoolL3 3BX United Kingdom Corporate Research Unilever R and D Colworth House Sharnbrook BedfordMK44 1LQ United Kingdom

ISBN: (纸本)3540228810

Marketing data analysis typically aims to gain insights for targeted promotions or, increasingly, to implement collaborative filtering. ideally, data would be visualised directly. there is a scarcity of methods to visualise the position of individual data points in clusters, mainly because dimensionality reduction is necessary for analysis of high-dimensional data and projective methods tend to merge clusters together. this paper proposes a cluster-based projective method to represent cluster membership, which shows good cluster separation and retains linear relationships in the data. this method is practical for the analysis of large, high-dimensional, databases, with generic applicability beyond marketing studies. theoretical properties of this non-orthogonal projection are derived and its practical value is demonstrated on real-world data from a web-based retailer, benchmarking with the visualisation of clusters using Sammon and Kohonen maps. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Marketing

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Deep learning on Natural Viewing Behaviors to Differentiate Children with Fetal Alcohol Spectrum Disorder

Deep Learning on Natural Viewing Behaviors to Differentiate ...

引用

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

作者： Tseng, Po-He Paolozza, Angelina Munoz, Douglas P. Reynolds, James N. Itti, Laurent Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Queens Univ Ctr Neurosci Studies Kingston ON K7L 3N6 Canada

ISBN: (纸本)9783642412783;9783642412776

Computational models of visual attention have attracted strong interest by accurately predicting how humans deploy attention. However, little research has utilized these models to detect clinical populations whose attention control has been affected by neurological disorders. We designed a framework to decypher disorders from the joint analysis of video and patients' natural eye movement behaviors (watch television for 5 minutes). We employ convolutional deep neural networks to extract visual features in real-time at the point of gaze, followed by SVM and Adaboost to classify typically developing children vs. children with fetal alcohol spectrum disorder (FASD), who exhibit impaired attentional control. the classifier achieved 74.1% accuracy (ROC: 0.82). Our results demonstrate that there is substantial information about attentional control in even very short recordings of natural viewing behavior. Our new method could lead to high-throughput, low-cost screening tools for identifying individuals with deficits in attentional control.

关键词： deep learning sparse representation eye movement natural scenes clinical populations

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Exploiting safety constraints in fuzzy self-organising maps for safety critical applications

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

作者： Kurd, Zeshan Kelly, Tim P. Austin, Jim High Integrity Systems Engineering Group Department of Computer Science University of York YorkYO10 5DD United Kingdom Advanced Computer Architectures Group Department of Computer Science University of York YorkYO10 5DD United Kingdom

ISBN: (纸本)3540228810

this paper defines a constrained Artificial Neural Network (ANN) that can be employed for highly-dependable roles in safety critical applications. the derived model is based upon the Fuzzy Self-Organising Map (FSOM) and enables behaviour to be described qualitatively and quantitatively. By harnessing these desirable features, behaviour is bounded through incorporation of safety constraints – derived from safety requirements and hazard analysis. the constrained FSOM has been termed a ‘Safety Critical Artificial Neural Network’ (SCANN) and preserves valuable performance characteristics for nonlinear function approximation problems. the SCANN enables construction of compelling (product-based) safety arguments for mitigation and control of identified failure modes. Illustrations of potential benefits for real-world applications are also presented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Safety factor

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Stacking-based ensemble learning on low dimensional features for fake news detection 21

Stacking-based ensemble learning on low dimensional features...

引用

21st IEEE international conference on High Performance Computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Li, Songqian Ma, Kun Niu, Xuewei Wang, Yufeng Ji, Ke Yu, Ziqiang Chen, Zhenxiang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9781728120584

With the age of incoming of self-media, everyone can be the author of the content in the media age of big data. this has caused a mass of fake news appearing in the network. Authors of these fake news will mislead the public by spreading and it will bring economic and social benefits. Existing work focuses on using the various types of features of the article in the hope that a way to accurately identify fake news can be found, but this undermines their universality. In this paper, we propose a pipeline that combines preprocessing, feature extraction and model fusion for a more accurate and automated prediction. Specially we fusion of latent semantic analysis (LSA) and ensemble learning model results using stacking. Experimental analysis of real-world data demonstrates that our pipeline achieves higher accuracy than existing approaches. © 2019 IEEE.

关键词： Pipelines

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Mining association rules using relative confidence

引用

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

作者： Do, Tien Dung Hui, Siu Cheung Fong, Alvis C.M. School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore

ISBN: (纸本)3540228810

Mining for association rules is one of the fundamental tasks of data mining. Association rule mining searches for interesting relationships amongst items for a given dataset based mainly on the support and confidence measures. Support is used for filtering out infrequent rules, while confidence measures the implication relationships from a set of items to one another. However, one of the main drawbacks of the confidence measure is that it presents the absolute value of implication that does not reflect truthfully the relationships amongst items. For example, if two items have a very high frequency, then they will probably form a rule with a high confidence even if there is no relationship between them at all. In this paper, we propose a new measure known as relative confidence for mining association rules, which is able to reflect truthfully the relationships of items. the effectiveness of the relative confidence measure is evaluated in comparison with the confidence measure in mining interesting relationships between terms from textual documents and in associative classification. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Association rules

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Business Process Redesign of Sales Monitoring Business Process Management to be a Smart Logistic Shipment Company 5

Business Process Redesign of Sales Monitoring Business Proce...

引用

5th international conference on Advancement in data Science, E-learning and Information Systems, ICADEIS 2023

作者： Firdaus, Taufiq Maulana Fajri, Hanif Fakhrurroja, Hanif Telkom University School of Industrial and System Engineering Bandung Indonesia Telkom University National Research and Innovation Agency School of Industrial Engineering Bandung Indonesia

ISBN: (纸本)9798350303414

the sales process in every company is important. therefore, every sales record must be stored in an integrated database so that tracking sales records is easy. However, there are still many sales recording practices that are still done manually, making the process of recording sales complicated and time-consuming. therefore, it needs a sales record automation process that can track all sales recording processes that provide standardization in the form of KPIs and progress at each stage of sales records. With the growth of smart logistics, it can drastically change the business process of dashboard monitoring in a logistics company. With the smart logistic concepts and the use of business process redesign methodology, it resulted in a more simplified yet integrated automated sales monitoring system. © 2023 IEEE.

关键词： Standardization

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intelligent data engineering and automated learning – ideal 2010 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Colin Fyfe Peter Tino Darryl Charles Cesar Garcia-Osorio Hujun Yin

ISBN: (数字)9783642153815

ISBN: (纸本)9783642153808

the ideal conference has become a unique, established and broad interdisciplinary forum for experts, researchers and practitioners in many fields to interact with each other and with leading academics and industries in the areas of machine learning, information processing, data mining, knowledge management, bio-informatics, neu- informatics, bio-inspired models, agents and distributed systems, and hybrid systems. this volume contains the papers presented at the 11th international conference on intelligent data engineering and automated learning (ideal 2010), which was held September 1–3, 2010 in the University of the West of Scotland, on its Paisley campus, 15 kilometres from the city of Glasgow, Scotland. All submissions were strictly pe- reviewed by the Programme Committee and only the papers judged with sufficient quality and novelty were accepted and included in the proceedings. the ideal conferences continue to evolve and this year’s conference was no exc- tion. the conference papers cover a wide variety of topics which can be classified by technique, aim or application. the techniques include evolutionary algorithms, artificial neural networks, association rules, probabilistic modelling, agent modelling, particle swarm optimization and kernel methods. the aims include regression, classification, clustering and generic data mining. the applications include biological information processing, text processing, physical systems control, video analysis and time series analysis.

关键词： Artificial Intelligence Programming Techniques database Management Information Systems Applications (incl. Internet) Information Storage and Retrieval Computation by Abstract Devices

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Detecting credit card fraud by using questionnaire-responded transaction model based on support vector machines

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

作者： Chen, Rong-Chang Chiu, Ming-Li Huang, Ya-Li Chen, Lin-Ti Department of Logistics Engineering and Management China Department of Information Management National Taichung Institute of Technology No. 129 Sec. 3 Sanmin Rd Taichung404 Taiwan

ISBN: (纸本)3540228810

this work proposes a new method to solve the credit card fraud problem. Traditionally, systems based on previous transaction data were set up to predict a new transaction. this approach provides a good solution in some situations. However, there are still many problems waiting to be solved, such as skewed data distribution, too many overlapped data, fickle-minded consumer behavior, and so on. To improve the above problems, we propose to develop a personalized system, which can prevent fraud from the initial use of credit cards. First, the questionnaireresponded transaction (QRT) data of users are collected by using an online questionnaire based on consumer behavior surveys. the data are then trained by using the support vector machines (SVMs) whereby the QRT models are developed. the QRT models are used to predict a new transaction. Results from this study show that the proposed method can effectively detect the credit card fraud. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Consumer behavior

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automated Detection of Encryption Algorithms Using AI Techniques 5

Automated Detection of Encryption Algorithms Using AI Techni...

引用

5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Chidambaranathan, Senthilnathan Santhanaraj, M. Ajitha, E. Syed Fiaz, A.S. Suchithra, B. Kathirvel, T. Virtusa Corporation NJ United States Kongunadu College of Engineering and Technology Department of IT Tamil Nadu Trichy India St. Joseph's Institute of Technology Computer Science and Engineering India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798331518097

In today's digital era, cryptographic algorithms play a vital role in safeguarding sensitive information, maintaining confidentiality, and defending against cyberattacks. With the growing variety of encryption methods and modes, identifying the specific algorithm used to generate a ciphertext has become increasingly challenging. To address this, we propose a machine learning solution that automatically identifies cryptographic algorithms using the Random Forest classifier. the system processes a vast dataset called 'encrypted-data,' which contains 39 different algorithms and modes, by extracting essential features like byte frequency and entropy from the ciphertexts. these features form a 257-dimensional Feature vector used for classification. Random Forest models are trained with these data. During testing, models are loaded is used to predict the encryption algorithm for a given ciphertext. Our method offers a scalable and efficient approach to cryptographic identification, surpassing traditional techniques. © 2024 IEEE.

关键词： Encryption algorithms

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Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems 2022

Evaluation Toolkit For Robustness Testing Of Automatic Essay...

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5th ACM India Joint international conference on data Science and Management of data (CoDS-COMAD) / 9th ACM IKDD CODS conference / 26th COMAD conference

作者： Kabra, Anubha Bhatia, Mehar Singla, Yaman Kumar Li, Junyi Jessy Shah, Rajiv Ratn Adobe San Jose CA 95110 USA IIIT Delhi Delhi India SUNY Buffalo Buffalo NY USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781450385824

Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. this number is increasing further due to COVID-19 and the associated automation of education and testing. Yet, the AI-based testing literature of these 'intelligent' models is highly lacking as most of the papers propose new models that rely only on quadratic weighted kappa (QWK) based agreement with human raters for showing model efficacy. However, this effectively ignores the highly multi-feature nature of essay scoring. Essay scoring depends on features like coherence, grammar, relevance, etc. and to date, there has been no study testing automated Essay Scoring (AES) systems holistically on all these features. With this motivation, we propose a model agnostic adversarial evaluation scheme and associated metrics for AES systems to test their natural language understanding capabilities and overall robustness. We evaluate the current state-of-the-art AES models using the proposed scheme and report the results on five recent models. these models range from feature-engineering-based approaches to the latest deep learning algorithms. We find that AES models are highly overstable such that even heavy modifications (as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models. On the other hand, unrelated content, on average, increases the scores, thus showing that the models' evaluation strategy and rubrics should be reconsidered. We conduct a human survey with 200 human raters and observe that they can easily detect differences between the original and perturbed responses and have a general disagreement with the scores assigned by auto scorers.

关键词： Deep learning

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