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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7123 条记录，以下是41-50 订阅

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Preliminary Investigation on Machine learning and Deep learning Models for Change of Direction Classification in Running 25th

Preliminary Investigation on Machine Learning and Deep Learn...

引用

25th international conference on intelligent data engineering and automated learning

作者： Jaiswal, Pranay Kaushik, Abhishek Lawless, Fiona Malaquias, Tiago McCaffery, Fergal Dundalk Inst Technol Dundalk Ireland STATSports Newry North Ireland

ISBN: (纸本)9783031777301;9783031777318

the ability to detect, define, and classify Change of Direction (COD) movements during running plays a crucial role in sports science, as it has been widely used to assess athlete performance. Automating the process of COD classification during live games or training can provide real-time feedback. In this study, we evaluated Machine learning (ML) and Deep learning (DL) models for the classification of COD using accelerometers and gyroscope sensor data, and speed data were calculated from the Global Positioning System (GPS) sensor data. We hypothesized that DL algorithms classify COD better than ML classification algorithms. Comparative analysis showed that the best-performing DL and ML models showed similar behavior. Similarly, the statistical analysis observed no significant difference. this emphasized the importance of accurate model selection.

关键词： Change of Direction Machine learning Deep learning Sports Running Classification

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Padel Two-Dimensional Tracking Extraction from Monocular Video Recordings 25th

Padel Two-Dimensional Tracking Extraction from Monocular Vid...

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25th international conference on intelligent data engineering and automated learning

作者： Novillo, Alvaro Acena, Victor Lancho, Carmen Cuesta, Marina De Diego, Isaac Martin Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain

ISBN: (纸本)9783031777301;9783031777318

this study introduces a novel framework for the automatic two-dimensional tracking of padel games using monocular recordings. By integrating advanced Computer Vision and Deep learning techniques, our algorithm detects and tracks players, the court, and the ball. through homography, we accurately project detected player positions onto a two-dimensional court, enabling comprehensive tracking throughout the game. We tested the proposed algorithm using amateur video recordings of padel games found in literature. this approach remains user-friendly, cost-effective, and adaptable to various camera angles and lighting conditions. this makes it accessible to both amateur and professional players and coaches, providing a valuable tool for performance analysis. Additionally, the proposed framework holds potential for adaptation to other sports with minimal modifications, further broadening its applicability.

关键词： Computer Vision Padel Deep learning Court Detection Homography Applied Intelligence Tracking data

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An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis Processes 25th

An Ontology-Lexicon-Driven Approach for Refining Sentiment A...

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25th international conference on intelligent data engineering and automated learning

作者： Toldy, Francisco Belo, Orlando Univ Minho ALGORITMI Res Ctr LASI P-4710059 Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

the high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns of customer behavior. this is quite important for companies intending to change their way of being, promoting work actions aimed at specific customer segments, to obtain business advantages and improve their image and performance in the market in which they work. In this paper, we present and describe a sentiment analysis system that combine techniques based on ontologies and domain lexicons, to provide relevant indicators to support the evaluation of the degree of user satisfaction and know the influence of each ontological element incorporated in opinion texts in sentiment classification.

关键词： Sentiment Analysis Opinion Analysis Ontologies Lexical Classification data Preparation Tagging Texts Marketing Applications

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A Deep-learning Approach for the Identification of New Subtypes of Lung Cancer 25th

A Deep-Learning Approach for the Identification of New Subty...

引用

25th international conference on intelligent data engineering and automated learning

作者： Banerjee, Tuhin Corradini, Andrea Canon EMEA Amstelveen Netherlands MCI Innsbruck Austria

ISBN: (纸本)9783031777301;9783031777318

Lung cancer is the deadliest cancer in the world. It is caused by unchecked cell division of damaged cells in the lungs forming tumors that eventually prevent the lung from functioning properly. Identification of novel unsupervised subtypes of lung cancer is critical to reveal new insights into the underlying biology of cancer and ensure that patients receive specialized precision treatment based on the subtype of cancer they are suffering from. the ability of modern sequencing tools to produce patient-specific RNA sequencing (RNA-seq) gene expression data has transformed cancer research by offering in-depth understanding of the molecular landscape of cancer. this paper reports on a pipeline that comprise of a Deep Autoencoder (DAE) model coupled with hierarchical agglomerative clustering (H-Clust). It aims to identify new unsupervised lung cancer subtypes from RNA-seq expression samples collected from a publicly available dataset. Further, a deep learning (DL) model, Artificial Neural Network (ANN) is used to classify a patient's data into one of the newly identified subtypes.

关键词： lung cancer subtype TCGA RNA-seq deep learning DAE encoder-decoder agglomerative clustering ANN

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Comparative Study of Federated learning Algorithms Based on SPADE Agents 25th

Comparative Study of Federated Learning Algorithms Based on ...

引用

25th international conference on intelligent data engineering and automated learning

作者： Enguix, Francisco Peris, Saul Cerda Rincon, J. A. Carrascosa, C. Univ Politec Valencia UPV Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Burgos Dept Digitalizac Escuela Politec Super Ctra Oron 28 Burgos 09200 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning (FL) is a prominent method in machine learning, that ensures privacy by enabling distributed devices to collaboratively learn a shared model without exchanging local data. this paper provides a comparative analysis of various FL algorithms implemented on the Smart Python Agent Development Environment (SPADE) framework. We focus on evaluating the performance, scalability, and resilience of these algorithms across different network setups and data distribution scenarios. Our results highlight the differential impacts of decentralized versus centralized approaches, particularly under non-IID data conditions, common in real-world applications. By leveraging SPADE agents and consensus algorithms, this study not only tests algorithmic efficiency and system robustness but also explores advanced strategies like asynchronous updates and coalition-based learning, which show promise in enhancing model accuracy and reducing communication overhead.

关键词： Federated learning Multi-agent systems Consensus

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Smart Sign Language Decoder 25th

Smart Sign Language Decoder

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25th international conference on intelligent data engineering and automated learning

作者： Costescu, Constantin Marius Popescu, Paul Stefan Mihaescu, Marian Cristian Univ Craiova Fac Automat Comp Sci & Elect Craiova Romania

ISBN: (纸本)9783031777370;9783031777387

Sign language recognition and understanding are challenging tasks for many people who are not familiar with it, which limits communication between deaf-mute people and others. the system presented in this paper lowers the communication barrier, introducing an automatic translation layer that facilitates sign language understanding. the system uses a deep-learning model for sign language detection and a separate library for hand joint mapping. the application's architecture was designed to allow users to access the system from desktop and mobile devices. the model's results revealed an 82% accuracy, and after several tweaks on the activation function in our tests, we achieved perfect classification in our real word tests. the results of the system offered excellent accuracy, and its usability lowers the communication barrier between people, providing flexibility as the application is available for any device with a browser.

关键词： Sign Language Recognition deep learning dataset

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Model-Based Meta-reinforcement learning for Hyperparameter Optimization 25th

Model-Based Meta-reinforcement Learning for Hyperparameter O...

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25th international conference on intelligent data engineering and automated learning

作者： Albrechts, Jeroen Martin, Hugo M. Tavakol, Maryam Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783031777301;9783031777318

Hyperparameter Optimization (HPO) plays a significant role in enhancing the performance of machine learning models. However, as the size and complexity of (deep) neural architectures continue to increase, conducting HPO has become very expensive in terms of time and computational resources. Existing methods that automate this process still demand numerous evaluations to find the optimal hyperparameter configurations. In this paper, we present a novel approach based on model-based reinforcement learning to effectively improve sample efficiency while minimizing resource consumption. We formulate the HPO task as a Markov decision process and develop a predictive dynamics model for efficient policy optimization. Additionally, we employ the Deep Sets framework to encode the state space, which is then leveraged in meta-learning for transfer of knowledge across multiple datasets, enabling the model to quickly adapt to new datasets. Empirical studies demonstrate that our approach outperforms alternative techniques on publicly available datasets in terms of sample efficiency and accuracy.

关键词： Deep reinforcement learning

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A New dataset for Analyzing Battery Failures in Wheelchairs 25th

A New Dataset for Analyzing Battery Failures in Wheelchairs

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25th international conference on intelligent data engineering and automated learning

作者： Manzolli, William M. Rickes, Tiago B. Lucca, Giancarlo Oliveira, Lizandro de Souza Yamin, Adenauer Correa Univ Catolica Pelotas Ctr Ci encias Sociais Tecnol Mestrado Engenharia Eletron & Comp Pelotas RS Brazil Univ Fed Pelotas Pelotas RS Brazil

ISBN: (纸本)9783031777370;9783031777387

this study addresses battery failure in motorized wheel chairs, which are essential for the mobility of individuals with disabilities. the main objective was to concept a comprehensive dataset comprising six attributes that directly impact battery life, consisting of 498 instances. Using the Random Forest algorithm, we demonstrate the ability to accurately predict battery failures. the results highlight the necessity for proactive measures to prevent battery degradation and extend its lifespan.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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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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Federated learning for Vietnamese SMS Spam Detection Using Pre-trained PhoBERT 25th

Federated Learning for Vietnamese SMS Spam Detection Using P...

引用

25th international conference on intelligent data engineering and automated learning

作者： Hoang Quang Anh Pham Tuan Anh Pham Son Nguyen Phan Duy Hung FPT Univ Hanoi Vietnam

ISBN: (纸本)9783031777301;9783031777318

SMS Spam Detection has increasingly garnered attention due to the widespread use of mobile devices. Currently, most SMS spam detection model training methods rely on centralized data collection, which poses numerous privacy threats and creates security vulnerabilities that expose sensitive information. this study aims to propose a training method that does not require data sharing between parties, based on a federated learning system. In this paper, we experiment with FedAvg, FedAvgM, and FedAdam algorithms using a fine-tuned PhoBERT model tailored for the SMS spam classification task. the results show that the FedAvg algorithm achieves high performance with an accuracy of 99.38% in the IID setting, while the FedAdam algorithm proves more effective in the Non-IID setting, yielding a model with an accuracy of up to 98.5%. this study demonstrates that models like PhoBERT trained with FL algorithms can achieve classification capabilities comparable to centralized data training methods, highlighting the significant potential of FL for natural language processing models without the need for centralized data collection.

关键词： Vietnamese SMS spam detection PhoBERT Federated learning

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