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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91-100 订阅

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Requirements engineering in the Development of Multi-Agent Systems: A Systematic Review

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Blanes, David Insfran, Emilio Abrahao, Silvia Univ Politecn Valencia ISSI Res Grp Dept Informat Syst & Computat Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783642043932

the goal of this paper is to investigate which requirements engineering techniques have been applied in the development of Multi-Agent Systems (MAS) and how they were applied. We performed a systematic review of 58 of a total of 835 papers found in scientific digital libraries. the results show that most of the proposals for dealing with requirements (79%) use already defined methods or techniques from other software development paradigms and that 69% of these techniques are based on the goal-oriented paradigm. A total of 95% of the reviewed papers focus on techniques for analyzing requirements, and only 45% of them explicitly consider some type of elicitation technique. Finally, only 5% of the papers give some empirical evidence about the effectiveness of their approaches by conducting empirical studies. the results of our study are particularly important in the determination of current research activities in Requirements engineering for MAS and in the identification of research gaps for further investigation.

关键词： requirements engineering multi-agent systems systematic review

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A novel hybrid GA/SVM system for protein sequences classification

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

作者： Zhao, Xing-Ming Huang, De-Shuang Cheung, Yiu-Ming Wang, Hong-Qiang Huang, Xin Department of Automation University of Science and Technology of China P.O.Box.1130 HefeiAnhui230027 China Intelligent Computing Lab Hefei Institute of Intelligent Machines CAS P.O.Box.1130 HefeiAnhui230031 China Department of Computer Science Hong Kong Baptist University Kowloon Hong Kong

ISBN: (纸本)3540228810

A novel hybrid genetic algorithm(GA)/Support Vector Machine (SVM) system, which selects features from the protein sequences and trains the SVM classifier simultaneously using a multi-objective genetic algorithm, is proposed in this paper. the system is then applied to classify protein sequences obtained from the Protein Information Resource (PIR) protein database. Finally, experimental results over six protein superfamilies are reported, where it is shown that the proposed hybrid GA/SVM system outperforms BLAST and HMMer. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Proteins

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Automating co-evolutionary data mining

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

作者： Smith, James F. Naval Research Laboratory Code 5741 WashingtonDC20375-5000 United States

ISBN: (纸本)3540228810

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. this approach automates the data mining problem. the game automatically creates a cleansed database reflecting the domain expert’s knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. Co-evolutionary fitness functions are discussed. the strategy tree concept and its relationship to coevolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. Co-evolutionary data mining alters the geometric properties of the overlap region known as the combined admissible region of phase space significantly enhancing the performance of the resource manager. Significant experimental results are provided. © Springer-Verlag Berlin Heidelberg 2004.

关键词： data mining

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Integration of projected clusters and principal axis trees for high-dimensional data indexing and query

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

作者： Wang, Ben Gan, John Q. Department of Computer Science University of Essex ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)3540228810

High-dimensional data indexing and query is a challenging problem due to the inherent sparsity of the data. Fast algorithms are in an urgent need in this field. In this paper, an automatic subspace dimension selection (ASDS) based clustering algorithm is derived from the well-known projection-based clustering algorithm, ORCLUS, and a two-level architecture for highdimensional data indexing and query is also proposed, which integrates projected clusters and principal axis trees (PAT) to generate efficient highdimensional data indexes. the query performances of similarity search by ASDS+PAT, ORCLUS+PAT, PAT alone, and Clindex are compared on two high-dimensional data sets. the results show that the integration of ASDS and PAT is an efficient indexing architecture and considerably reduces the query time. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Clustering algorithms

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An algorithm for artificial intelligence-based model adaptation to dynamic data distribution

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

作者： Lee, Vincent C. S. Sim, Alex T. H. School of Business Systems Faculty of Information Technology Monash University 63 Wellington Road VIC3800 Australia

ISBN: (纸本)3540228810

Changes in data distribution for in-sample training and out-sample validation can be unavoidable due to presence of random dynamic noises created by external uncontrollable environmental factors. To compensate for the variation in data distribution, one approach is to recursively use immediate past prediction error to augment the current data. this paper proposes a simple algorithm that ensures the parameter settings in an ANFIS model are adaptive to its unique data distribution. Such an ‘open ended’ strategy allows the ANFIS to be more accurate in predicting chaotic time series problems. An application of the proposed a procedure to predict Dow Jones Industrial Average index has yielded better prediction accuracy than using the conventional prediction model. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Forecasting

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Generative AI Empowered Skin Cancer Diagnosis: Advancing Classification through Deep learning 5

Generative AI Empowered Skin Cancer Diagnosis: Advancing Cla...

引用

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

作者： thoviti, Sindhu Harshitha Varma, Bhupathiraju Karthik Sai, Seelam Nikhil Prasanna, B. Lakshmi Institute of Aeronautical Engineering Department of Computer Science and Information Tecnology Hyderabad India Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331518097

Effective diagnostic techniques are still needed for the early diagnosis and treatment of skin cancer, which is still a serious health concern. this paper presents a Generative AI-powered approach for the diagnosis of skin cancer that uses deep learning techniques to enhance classification accuracy and reliability. Current diagnostic methods face challenges like class imbalance, limited dataset diversity, and dependency on manual labeling. Recent advancements in AI, including CNNs and GANs, offer promising solutions but are hindered by issues such as overfitting and lack of generalizability. By using a Convolutional Neural Network (CNN) for classification and training over 200 epochs, the model achieved an accuracy of 92%. Precision, recall, and F1-score are measures of evaluation metrics to ensure the performance of the model is coherent, for which data augmentation was used to handle class imbalance. A Generative Adversarial Network was used with this model to create synthetic images, thus augmenting 1000 samples over 600 epochs. this type of data augmentation made a difference in the generalization capability of the model and greatly contributed to more robust classification outcomes and advancements in the automated detection of skin cancer. © 2024 IEEE.

关键词： Generative adversarial networks

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Char: An automatic way to describe characteristics of data

引用

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

作者： Liu, Yu-Chin Hsu, Ping-Yu Department of Business Administration National Central University Chung-Li320 Taiwan Department of Information Management TungNan Institute of Technology Taipei222 Taiwan Department of Business Administration National Central University Chung-Li320 Taiwan

ISBN: (纸本)3540228810

As e-business software prevails worldwide, large amount of data are accumulated automatically in databases of most sizable companies. Managers in organizations now face the problems of making sense out of the data. In this paper, an algorithm to automatically produce characteristic rules to describe the major characteristics of data in a table is proposed. In contrast to traditional Attribute Oriented Induction methods, the algorithm, named as Char Algorithm, does not need a concept tree and only requires setting a desired coverage threshold to generate a minimal set of characteristic rules to describe the given dataset. Our simulation results show that the characteristic rules found by Char are fairly consistent even when the number of records and attributes increase. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Trees (mathematics)

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A Novel Recommendation System for Next Feature in Software 20th

A Novel Recommendation System for Next Feature in Software

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

作者： Prata, Victor R. Moreira, Ronaldo S. Cordeiro, Luan S. Maia, Atilla N. Martins, Alan R. Leao, Davi A. Cavalcante, C. H. L. Souza Junior, Amauri H. Rocha Neto, Ajalmar R. Fed Inst Ceara Fortaleza Ceara Brazil Fortes Tecnol Rua Antonio Fortes 330 BR-60813460 Fortaleza Ceara Brazil

ISBN: (纸本)9783030336073;9783030336066

Software that needs to fulfill many tasks requires a large number of components. Users of these software need a lot of time to find the desired functionality or follow a particular workflow. Recommendation systems can optimize a user's working time by recommending the next features he/she needs. Given that, we evaluate the use of three algorithms (Markov Chain, IndRNN, and LSTM) commonly applied in sequence recommendation/classification in a dataset that reflects the use of the accounting software from Fortes Tecnologia. We analyze the results under two aspects: accuracy for top-5 recommendations and training time. the results show that the IndRNN achieved the highest accuracy, while the Markov Chain reached the lowest training time.

关键词： Recommendation system Independently Recurrent Neural Network Sequential recommendation Deep learning Commercial applications

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the categorisation of similar non-rigid biological objects by clustering local appearance patches

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

作者： Wang, Hongbin Culverhouse, Phil F. Centre for Interactive Intelligent Systems School of Computing Communications and Electronics University of Plymouth PlymouthPL4 8AA United Kingdom

ISBN: (纸本)3540228810

A novel approach is presented to the categorisation of non-rigid biological objects from unsegmented scenes in an unsupervised manner. the biological objects investigated are five phytoplankton species from the coastal waters of the European Union. the high morphological variability within each species and the high similarity between species make the categorisation task a challenge for both marine ecologists and machine vision systems. the framework developed takes a local appearance approach to learn the object model, which is done using a novel-clustering algorithm with minimal supervised information. Test objects are classified based on matches with local patches of high occurrence. Experiments show that the method achieves good results, given the difficulty of the task. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Clustering algorithms

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Clustering individuals in non-vector data and predicting: A novel model-based approach

Clustering individuals in non-vector data and predicting: A ...

引用

4th international conference on intelligent data engineering and automated learning (ideal 2003)

作者： Luo, KD Wang, JM Li, DY Sun, JG Tsing Hua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (纸本)354040550X

In many applications, data is non-vector in nature. For example, one might have transaction data from a dialup access system, where each customer has an observed time-series of dialups which are different on start time and dialup duration from customer to customer. It's difficult to convert this type of data to a vector form, so that the existing algorithms oriented on vector data [5] are hard to cluster the customers with their dialup, events. this paper presents an efficient model-based algorithm to cluster individuals whose data is non-vector in nature. then we evaluate on a large data set of dialup transaction, in order to show that this algorithm is fast and scalable for clustering, and accurate for prediction. At the same time, we compare this algorithm with vector clustering algorithm by predicting accuracy, to show that the former is fitter for non-vector data than the latter.

关键词： Vectors

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