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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81-90 订阅

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Medical Image-to-Image Translation with Spatial Self-Attention for Radiotherapy in Federated learning 5

Medical Image-to-Image Translation with Spatial Self-Attenti...

引用

5th international conference on intelligent data Science Technologies and Applications, IDSTA 2024

作者： Bdair, Tariq Saadeh, Hiba Qaqish, Ban Sulaq, Aya Rawashdeh, Majdi Princess Sumaya University for Technology Data Science Department Amman Jordan Al Yamamah University Software Engineering Department Riyadh Saudi Arabia Princess Sumaya University for Technology Business Information Technology Department Amman Jordan

ISBN: (纸本)9798350354751

Globally, cancer remains a leading cause of death, affecting millions of people each year. Accurate medical imaging is crucial for the effective planning of radiotherapy. However, repeated exposure to radiation from Computed Tomography (CT) scans during treatment planning can put patients at more risk. Fortunately, the recent improvement in automated image-to-image translation using deep learning methods has reached a superior performance. However, this might be challenged by data limitation of requiring a large amount of annotated data assembled in one location, privacy, and motion artifacts in medical imaging. Yet, finding such conditions usually is not feasible. To address this, we propose RadiaSync. RadiaSync is a federated learning framework proposed to train decentralized models in a privacy-preserved fashion. In our method, we use CycleGAN architecture for image translation within an FL environment, ensuring patient privacy and collaborative learning across different clients. Further, we propose a spatial attention mechanism that enhances the translated image quality with more than 55% improvement over the baseline. © 2024 IEEE.

关键词： Federated learning

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

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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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Advances and Challenges in automated Agriculture: Integrating 5G Wireless Technology with IoT, Cloud Computing and Robotics 5

Advances and Challenges in Automated Agriculture: Integratin...

引用

5th international conference on Electrical, Communication and Computer engineering, ICECCE 2024

作者： Li, Xinwei Kang, Chia Chao Ng, Zi Neng Minhad, Khairun Nisa' School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798331529437

the agricultural sector is undergoing a transition as automation and robotics converge and evolve. the current labor shortage and the growing need to increase yields are driving innovations in automated agriculture. automated systems are gradually replacing traditional farming that relies on manpower. the Internet of things (IoT) with 5G technology has become the key to these traditional automated irrigation systems. the system collects data from soil moisture, rainfall and temperature sensors thus uploads it to the cloud to store data and analyze it to make intelligent irrigation decisions. the proposed innovative system uses iterative learning algorithms and training models to identify pests, diseases and flower types. this article discusses in depth the model evolution of automation technology in the agricultural sector, IoT and cloud computing applications in automated agriculture and the growing use of robotics in agricultural operations. Besides that, it also discusses future directions and challenges. In addition, this article explores the potential for cross-platform collaboration and data sharing to advance the progress of this automation model. However, the field still faces challenges due to the decentralized nature of technology development. © 2024 IEEE.

关键词： Smart agriculture

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Experimental comparison of classification uncertainty for randomised and bayesian decision tree ensembles

引用

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

作者： Schetinin, Vitaly Partridge, Derek Krzanowski, Wojtek J. Everson, Richard M. Fieldsend, Jonathan E. Bailey, Trevor C. Hernandez, Adolfo School of Engineering Computer Science and Mathematics University of Exeter United Kingdom

ISBN: (纸本)3540228810

In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Decision trees

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On a detection of Korean prosody phrases boundaries

引用

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

作者： Kim, Jong Kuk Lee, Ki Young Bae, Myung Jin Department of Information Telecommunication Engineering SoongSil University Sangdo 5-dong Dongjak-gu Seoul156-743 Korea Republic of Department of Information Communication Engineering KwanDong University 7 San Imcheon-ri Yangyang-eup Yangyang-gunGangwon-do Korea Republic of

ISBN: (纸本)3540228810

this paper describes an automatic detection technique of Korean accentual phrase boundaries by using one-stage DP, and the normalized pitch pattern. For making the normalized pitch pattern, we propose a method of modified normalization for Korean spoken language. the results shows that 76.4% of the accentual phrase boundaries are correctly detected while 14.7% are the false detection rate. Also we can know that accentual phrase detection method by pattern matching shows the more superior detection rate than detection method by LH tone from this result. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Pattern matching

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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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Machine learning or discrete choice models for car ownership demand estimation and prediction? 5

Machine learning or discrete choice models for car ownership...

引用

5th IEEE international conference on Models and Technologies for intelligent Transportation Systems, MT-ITS 2017

作者： Paredes, Miguel Hemberg, Erik O'Reilly, Una-May Zegras, Chris CSAIL and DUSP Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781509064847

Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. they show how some distinct choice of human behavior or preference influences a decision. they are also used to project future demand estimates to support policy exploration. this latter use for prediction is indirectly aligned with and conditional to the model's estimation which aims to fit the observed data. In contrast, machine learning models are derived to maximize prediction accuracy through mechanisms such as out-of-sample validation, non-linear structure, and automated covariate selection, albeit at the expense of interpretability and sound behavioral theory. We investigate how machine learning models can outperform discrete choice models for prediction of car ownership using transportation household survey data from Singapore. We compare our household car ownership model (multinomial logit model) against various machine learning models (e.g. Random Forest, Support Vector Machines) by using 2008 data to derive, i.e. estimate models that we then use to predict 2012 ownership. the machine learning models are inferior to the discrete choice model when using discrete choice features. However, after engineering features more appropriate for machine learning they are superior. these results highlight both the cost of applying machine learning models in econometric contexts and an opportunity for improved prediction and better urban policy making through machine learning models with appropriate features. © 2017 IEEE.

关键词： Forecasting

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Predictive Analytics in Hotel Recommendations Using Machine learning 5

Predictive Analytics in Hotel Recommendations Using Machine ...

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

作者： Singh, Siddharth Dey, Shayan Sharma, Shreyanshi Prathaban, Banu Priya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

In a world where travellers want the perfect stay, a new system emerges as the one that goes beyond simple hotel recommendations. By analysing details like interaction words and prices, it predicts hotel ratings using a powerful Random Forest Regressor which performs better than methods like Linear Regressor. With careful data processing and encoding, it achieves high accuracy, measured through precise metrics like RMSE, MSE and Max Error. It achieves a great RMSE score of 0.424. this system offers a scalable solution, turning every hotel suggestion into a personalized experience, redefining the way people find their ideal stay. © 2024 IEEE.

关键词： Hotels

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