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检索条件"任意字段=6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005"

共 209 条记录，以下是1-10 订阅

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intelligent data engineering and automated learning - ideal 2005: 6th international conference. Proceedings

Intelligent Data Engineering and Automated Learning - IDEAL ...

引用

6th international conference on intelligent data engineering and automated learning - ideal 2005

the proceedings contain 76 papers from the intelligent data engineering and automated learning - ideal 2005: 6th international conference. Proceedings. the topics discussed include: synthetic environment representational semantics using the web ontology language;model trees for classification of hybrid data types;unsupervised image segmentation using penalized fuzzy clustering algorithm;mining job logos using incremental attribute-oriented approach;design of simple structure neural voltage regulator for power systems;designing an optimal network using the cross-entropy method;efficient spatial clustring algorithm using binary tree;matching peptide sequences with mass spectra;and toward transitive dependence in MAS.

关键词： data processing

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intelligent data engineering and automated learning--ideal 2004 : 5th international conference, Exeter, UK, August 25-27, 2004 : Proceedings 1

引用

丛书名： Lecture Notes in Computer Science 3177

2004年

作者： Yang Zheng Rong. Everson Richard Yin Hujun

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Quantitative Estimation of Reputation Risk 25th

Quantitative Estimation of Reputation Risk

引用

25th international conference on intelligent data engineering and automated learning

作者： Mitic, Peter UCL Dept Comp Sci Gower St London WC1E 6BT England

ISBN: (纸本)9783031777301;9783031777318

We present a quantitative definition of reputation risk, formulated in terms of a reputation time series comprising daily sentiment measurements. Self Supported learning is used to quantify reputation risk by progressively refining an initial proposal for a Minimum Acceptable Sentiment, calculated from descriptive statistics of the reputation data. the derived values are validated using a "sense test" based on a Loess quantile. the results show that the Minimum Acceptable Sentiment value is given approximately by a two standard deviation lower tail of the observed data.

关键词： Reputation Sentiment Time Series Descriptive Statistics Value-at-Risk Normal Mixture Distribution CNN Loess Self Supervised learning SSL NLP

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Automatic PDF Document Classification with Machine learning 25th

Automatic PDF Document Classification with Machine Learning

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25th international conference on intelligent data engineering and automated learning

作者： Llacer Luna, Socrates Garigliotti, Dario Martinez Plumed, Fernando Ferri Ramirez, Cesar Univ Politecn Valencia Valencia Spain Univ Bergen Bergen Norway

ISBN: (纸本)9783031777301;9783031777318

Universitat Polit`ecnica de Val`encia (UPV) faces challenges in managing its Alfresco document repository, which contains 600,000 PDF files, of which only 100,000 are correctly categorised. Manual classification is laborious and error-prone, hindering information retrieval and advanced search capabilities. this project presents an automated pipeline that integrates optical character recognition (OCR) and machine learning to efficiently classify documents. Our approach distinguishes between scanned and digital documents, accurately extracts text and categorises it into 51 predefined categories using models such as BERT and RF. By improving document organisation and accessibility, this work optimises UPV's document management and paves the way for advanced search technologies and real-time classification systems.

关键词： Document Classification OCR Machine learning Alfresco Repository

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Pipeline for Semantic Segmentation of Large Railway Point Clouds 25th

Pipeline for Semantic Segmentation of Large Railway Point Cl...

引用

25th international conference on intelligent data engineering and automated learning

作者： Gabrielidis, Hugo Gatti, Filippo Vialle, Stephane Univ Paris Saclay LMPS Lab Mecan Paris Saclay UMR 9026 CNRS Cent SupelecENS Paris Saclay F-91190 Gif Sur Yvette France Univ Paris Saclay LISN Lab Interdisciplinaire Sci Numer CNRS F-91405 Orsay France Cent Supelec F-91192 Gif Sur Yvette France

ISBN: (纸本)9783031777301;9783031777318

this paper presents a novel deep-learning pipeline to segment large railway datasets with minimal manual annotation, notoriously time consuming. the pipeline adapts DINOv2 [11] for labeling point clouds, with tailored self-distillation pre-training and fine-tuning. the adopted transformer architecture successfully generalizes to multiple railway datasets, with a lightweight pipeline that outperforms manual labeling speed by a factor of 6, despite requiring a final segmentation check and correction. this groundbreaking achievement bridges the gap between the need for annotated point clouds in railway industry and the lack of publicly available annotated datasets.

关键词： 3D Point Cloud Deep learning Semantic Segmentation Large Scale data

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Noise Tolerance and Robustness Ranking in Machine learning Models 25th

Noise Tolerance and Robustness Ranking in Machine Learning M...

引用

25th international conference on intelligent data engineering and automated learning

作者： Padro-Ferragut, Cristina Ramirez-Quintana, Maria Jose Martinez-Plumed, Fernando Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9783031777370;9783031777387

Machine learning models often excel in controlled environments but may struggle with noisy, incomplete, or shifted real-world data. Ensuring that these models maintain high performance despite these imperfections is crucial for practical applications, such as medical diagnosis or autonomous driving. this paper introduces a novel framework to systematically analyse the robustness of Machine learning models against noisy data. We propose two empirical methods: (1) Noise Tolerance Estimation, which calculates the noise level a model can withstand without significant degradation in performance, and (2) Robustness Ranking, which ranks Machine learning models by their robustness at specific noise levels. Utilizing Cohen's kappa statistic, we measure the consistency between a model's predictions on original and perturbed datasets. Our methods are demonstrated using various datasets and Machine learning techniques, identifying models that maintain reliability under noisy conditions.

关键词： Model robustness Noise in data Machine learning Cohen's kappa Performanceevaluation

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Using Contrastive learning to Map Stylistic Similarities in Narrative Writers 25th

Using Contrastive Learning to Map Stylistic Similarities in ...

引用

25th international conference on intelligent data engineering and automated learning

作者： Valero-Redondo, Maria Huertas-Tato, Javier D'Antonio Maceiras, Sergio Martin, Alejandro Camacho, David Univ Cordoba Cordoba Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Exploring how literary authors influence each other and how intertextual connections can be found among them is a complex problem that is handled manually by experts. this research field, intertextuality, seeks to understand how writers relate to each other and how the works of one writer have reflected on the work of another. this analysis usually provides insight into the historical, cultural, biographical, and literary contexts that shape literary works. From a computational point of view, the approaches related to this problem have mainly dealt with problems of style classification, known as authorship attribution, but none have so far dealt with the problem of intertextuality. this paper proposes a novel approach based on contrastive learning to generate authorship embeddings that encapsulate the stylistic signatures of narrative writers and that can be projected into graphical representations to discern similarities between a dataset of literary writers. the embeddings generated with this approach aim to represent authorship styles are created and evaluated using a dataset of books from Project Gutemberg. During the evaluation, we perform a book and chunk-level evaluation, showing good performance in both cases. Finally, we present different graphical representations and provide a deep analysis of the relations that arise between the writers.

关键词： Contrastive learning Intertextual analysis Language Models Transformer

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Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

the integration of the Internet of things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. this paves the way for a wide variety of intelligent IoT applications to be developed. this article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. these include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015-2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT. © 2025 IEEE.

关键词： Federated learning

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Handwritten Character Recognition with Convolution Neural Network 6th

Handwritten Character Recognition with Convolution Neural Ne...

引用

6th international conference on intelligent Communication, Control and Devices, ICICCD 2024

作者： Soni, Vishnu Kant Chouksey, Praveen Tandan, S.R. Miri, Rohit Nema, Neetesh Kumar Naik, Muskan Dr. C. V. Raman University C.G. Bilaspur India Department of Computer Science and Engineering Dr. C. V. Raman University C.G. Bilaspur India Department of Computer Science Government R. V. R. S. Kanya Mahavidyalaya C.G. Kawardha India Department of Computer Science and Engineering UTD CSVTU Bhilai C.G. Bhilai India Department of Computer Science and Engineering Lakhmi Chand Institute of Technology C.G. Bilaspur India

ISBN: (纸本)9789819783281

In the realm of pattern recognition, the automated detection of handwritten text or symbols poses intricate challenges in the field of handwriting recognition. the paper introduces a novel approach that considers the batch_size to calculate the accuracy of the machine learning method to attain precise and effective recognition of handwritten characters. the suggested technique employs artificial neural networks, particularly convolutional neural networks (CNNs), to train a model skilled of precisely identifying and categorizing handwritten characters. Experimental result indicates that the proposed model achieves a notable accuracy percentage of 99.10% on a widely used data set (NIST). the attained high level of accuracy accentuates the effectiveness and viability of machine learning algorithms in research works related to handwriting recognition. the achievement also paves the way for numerous potential applications in fields like document analysis, optical character recognition, and interfaces based on handwriting. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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