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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101-110 订阅

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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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Stock trading by modelling price trend with dynamic bayesian networks

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

作者： Jangmin, O. Lee, Jae Won Park, Sung-Bae Zhang, Byoung-Tak School of Computer Science and Engineering Seoul National University San 56-1 Shillim-dong Kwanak-gu Seoul151-744 Korea Republic of School of Computer Science and Engineering Sungshin Women’s University Dongsun-dong Sungbuk-gu Seoul136-742 Korea Republic of

ISBN: (纸本)3540228810

We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHMM). there are five states describing the trend in first level. Second and third levels are abstract and concrete hidden Markov models to produce the observed patterns. To train the HHMM, we adapt a semi-supervised learning so that the trend states of first layer is manually labelled. the inferred probability distribution of first level are used as an indicator for the trading signal, which is more natural and reasonable than technical indicators. Experimental results on representative 20 companies of Korean stock market show that the proposed HHMM outperforms a technical indicator in trading performances. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Hidden Markov models

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Enhanced CNN Architecture for Accurate Waste Classification in Smart Cities 5

Enhanced CNN Architecture for Accurate Waste Classification ...

引用

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

作者： Kabilan, B. Sairam, R. Praveen, M. Saveetha Engineering College Dept. of ECE Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

Waste management is one of the biggest challenges of the present world as the generation of waste has risen tremendously. In this paper we introduce an automated waste classification system that uses Convolutional Neural Networks to effectively sort waste into four categories which are biodegradable, non-biodegradable, biomedical and electronic waste. the framework that has been suggested in this paper incorporates several new preprocessing techniques including data augmentation as well as noise removal for enhancing the robustness of the model. the CNN architecture has the convolutional, pooling and dense layers that are well tuned to identify essential features and enhance the accuracy of classification. the model was trained and validated using a large data set and the accuracy of the model was 91.87%, while the F1 score, recall and precision were also high for all the waste categories. In contrast to other methods for machine learning like SVM, RF, CNN was found to be better in dealing with high dimensional image data, owing to its flexibility and efficiency. the system can be implemented on edge devices which makes it possible to sort waste in real time and thus do not require much reliance on central systems. this approach has potential for application in cities, smart industries and healthcare units with a view of environmental conservation and optimal resource management. In the future, the work will be extended to improve the dataset, use transfer learning approaches and develop new applications including smart waste bins and hazardous material separation systems to enhance the field of automated waste management. © 2024 IEEE.

关键词： Convolutional neural networks

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A clustering model for mining evolving web user patterns in data stream environment

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

作者： Wu, Edmond H. Ng, Michael K. Yip, Andy M. Chan, Tony F. Department of Mathematics The University of Hong Kong Pokfulam Road Hong Kong Department of Mathematics University of California 405 Hilgard Avenue Los AngelesCA90095-1555 United States

ISBN: (纸本)3540228810

With the fast growing of the Internet and its Web users all over the world, how to manage and discover useful patterns from tremendous and evolvingWeb information sources become new challenges to our data engineering researchers. Also, there is a great demand on designing scalable and flexible data mining algorithms for various time-critical and data-intensive Web applications. In this paper, we purpose a new clustering model for generating and maintaining clusters efficiently which represent the changing Web user patterns in Websites. With effective pruning process, the clusters can be fast discovered and updated to reflect the current or changing user patterns to Website administrators. this model can also be employed in different Web applications such as personalization and recommendation systems. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Websites

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Leveraging Ensemble Deep learning Model for Fruit Image Recognition with Explainable AI 5

Leveraging Ensemble Deep Learning Model for Fruit Image Reco...

引用

5th international conference on Big data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Wang, Yaoqing Yu, Chang Lin, Xiyue Mgbejime, Goodness Temofe Nneji, Grace U. Monday, Happy N. University of Electronic Science and Technology School of Electronic Science and Engineering China Chengdu University of Technology Chengdu China Intelligent Computing Lab Hace Softtech Lagos102241 Nigeria

ISBN: (纸本)9798331506612

Precise classification of fruit images is crucial for a range of agricultural and retail purposes. Although there have been improvements, current methods have limits when it comes to dealing with complex feature extraction and ensuring model resilience. this paper introduces a new framework for categorizing fruits using a combination of advanced deep learning models, namely AlexNet, ResNet50, and EfficientNet. the collection of 43,912 photos across 44 fruit classifications underwent thorough pre-processing and data augmentation procedures. Grad-CAM, SHAP, and LIME were employed to achieve model interpretability, offering valuable insights into the decision-making process of the ensemble model. the experimental results indicate that the ensemble model surpasses the performance of individual models, obtaining 96.9% accuracy, 96.2% precision, 94.5% recall, and 95.4% F1-score. the proposed ensemble model provides a highly efficient solution for automated fruit classification, with substantial implications for agricultural technology and retail automation. © 2024 IEEE.

关键词： Convolutional neural networks

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learning users’ interests in a market-based recommender system

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

作者： Wei, Yan Zheng Moreau, Luc Jennings, Nicholas R. Intelligence Agents Multimedia Group School of Electronics and Computer Science University of Southampton United Kingdom

ISBN: (纸本)3540228810

Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. Our marketplace thus coordinates multiple recommender agents and ensures only the best recommendations are presented. To do this effectively, however, each agent needs to learn the users’ interests and adapt its recommending behaviour accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommender agents can use for these tasks. We then demonstrate that this strategy helps the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Recommender systems

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Modelling and clustering of gene expressions using RBFs and a shape similarity metric

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

作者： Möller-Levet, Carla S. Yin, Hujun Electrical Engineering and Electronics University of Manchester Institute of Science and Technology ManchesterM60 IQD United Kingdom

ISBN: (纸本)3540228810

this paper introduces a novel approach for gene expression time-series modelling and clustering using neural networks and a shape similarity metric. the modelling of gene expressions by the Radial Basis Function (RBF) neural networks is proposed to produce a more general and smooth characterisation of the series. Furthermore, we identified that the use of the correlation coefficient of the derivative of the modelled profiles allows the comparison of profiles based on their shapes and the distributions of time points. the series are grouped into similarly shaped profiles using a correlation based fuzzy clustering algorithm. A well known dataset is used to demonstrate the proposed approach and a set of known genes are used as a benchmark to evaluate its performance. the results show the biological relevance and indicate that the proposed method is a useful technique for gene expression time-series analysis. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Gene expression

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Clustering model selection for reduced support vector machines

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

作者： Jen, Lih-Ren Lee, Yuh-Jye Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

ISBN: (纸本)3540228810

the reduced support vector machine was proposed for the practical objective that overcomes the computational difficulties as well as reduces the model complexity by generating a nonlinear separating surface for a massive dataset. It has been successfully applied to other kernel-based learning algorithms. Also, there are experimental studies on RSVM that showed the efficiency of RSVM. In this paper we propose a robust method to build the model of RSVM via RBF (Gaussian) kernel construction. Applying clustering algorithm to each class, we can generate cluster centroids of each class and use them to form the reduced set which is used in RSVM. We also estimate the approximate density for each cluster to get the parameter used in Gaussian kernel. Under the compatible classification performance on the test set, our method selects a smaller reduced set than the one via random selection scheme. Moreover, it determines the kernel parameter automatically and individually for each point in the reduced set while the RSVM used a common kernel parameter which is determined by a tuning procedure. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Radial basis function networks

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A Novel Approach to Optimizing Spark: Detecting Misconfigurations for Enhanced Efficiency 5

A Novel Approach to Optimizing Spark: Detecting Misconfigura...

引用

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

作者： Sanjana, S. Rachana, V. Umesh, Uday Rao, Suraj Jayashree, R. PES University Department of Computer Science and Engineering Bengaluru India Bengaluru India

ISBN: (纸本)9798331518097

Configuration errors constitute 60% of contemporary system failures in distributed computing platforms. these errors lead to data breaches, system crashes, or silent failures requiring extensive technical support and knowledge of the application architecture. this research work introduces a dataset that can be used to detect misconfigurations in a Spark cluster. the dataset encompasses detailed system performance metrics such as CPU usage and memory allocation that were recorded by running CPU intensive and memory intensive Spark jobs under various configuration settings to capture performance variations. Analysis of this dataset provides insights into optimizing the values of Spark cluster configurations and mitigating the impact of configuration errors. By using machine learning techniques on this dataset, it is possible to construct automated methods for proactively detecting and mitigating misconfigurations, which improves the stability and efficiency of distributed computing environments. © 2024 IEEE.

关键词： Storage allocation (computer)

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An automated Face Mask Detection Using Ensemble learning Approach 5

An Automated Face Mask Detection Using Ensemble Learning App...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kumar, B. Kiran Mounika, Guggilam Harish, R.V.S. Nagarjuna, Valeti St. Ann's College of Engineering & Technology Embedded Systems Andhra Pradesh Chirala523187 India St. Ann's College of Engineering & Technology Andhra Pradesh Chirala523187 India Kallam Haranadhareddy Institute of Technology Dept of Cse NH-16 Andhra Pradesh Chowdavaram India

ISBN: (纸本)9798350379945

the COVID-19 pandemic and other infectious diseases are spreading quickly, which has brought attention to how essential face masks are in stopping the spread of respiratory disorders. To ensure compliance with mask-wearing rules, automatic face mask detection devices are becoming increasingly important. the application of automated Convolutional Neural Networks (ACNN) in conjunction with Internet of things (IoT) devices for an automated Face Mask Detection is the main emphasis of this research. the suggested method correctly detects people wearing or not wearing face masks in real-time video and picture data by leveraging ACNN, a deep-learning architecture created for image segmentation. A wide range of labeled photos of people wearing and not wearing masks are used to train the FCNN. the model is ideal for real-world applications in public areas, healthcare facilities, and educational institutions due to its high accuracy and efficiency. Real-time monitoring and notifications are made possible by the integration of IoT devices, which improves the operation of the system. In order to collect images and videos data, IoT sensors and cameras are positioned strategically in specific regions. A central processing unit (CPU) that executes the trained FCNN model is connected to these devices. Using email, SMS, or a mobile app, the system instantly notifies the appropriate authorities or persons when it finds someone not donning a face mask. © 2024 IEEE.

关键词： Convolutional neural networks

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