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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3588 条记录，以下是71-80 订阅

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

Using Contrastive Learning to Map Stylistic Similarities in ...

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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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Exploring data Symbion EI Deep learning and Model Sharing Modules 25th

Exploring Data Symbion EI Deep Learning and Model Sharing Mo...

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25th international conference on intelligent data engineering and automated learning

作者： Huszcza, Rafael Mendes, Amanda Lopes, Jeferson Borges, Eduardo N. Lucca, Giancarlo B. Guilherme, Pablo D. B. Pereira, Leandro A. Univ Fed Rio Grande FURG Ctr Ciencias Computac C3 Rio Grande Brazil Univ Catolica Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron & Computacao Pelotas RS Brazil Univ Estadual Parana UNESPAR Ctr Educ Humanidades Ciencias Biolog Paranagua Brazil Inst Fed Parana IFPR Eixo Tecnolog Meio Ambiente Paranagua Brazil

ISBN: (纸本)9783031777370;9783031777387

Considering the growing volume of available information, storing and managing data is an increasing challenge. Leveraging these raw datasets to extract meaningful knowledge and make informed decisions requires data mining methods, especially when combined with Machine learning algorithms to uncover hidden patterns in data. Considering this context, this work describes a project to contribute to datadriven initiatives by seamlessly integrating a data repository system with Machine learning (ML) tools. this integration allows users to implement pipelines within a single system-from storing and managing datasets to training and deploying ML models, making easier, more efficient, and intuitive data science workflow accessible to all, not just computer scientists. Furthermore, the system aims to enhance knowledge sharing among researchers and practitioners by enabling users to share datasets and trained machine learning models within the platform. this work highlights the system's key features, particularly the Machine learning module, which includes classic ML algorithms for tabular data and the newly introduced Deep learning algorithms for image classification. the new Model Sharing module also emphasizes promoting collaboration by allowing users to share and manage trained models effectively.

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

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New Approach to Support the Breast Cancer Diagnosis Process Using Frequent Pattern Growth and Stacking Based on Machine learning Techniques 25th

New Approach to Support the Breast Cancer Diagnosis Process ...

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25th international conference on intelligent data engineering and automated learning

作者： Sanmartin, John Azuero, Paulina Hurtado, Remigio Univ Politecn Salesiana Cuenca Ecuador

ISBN: (纸本)9783031777370;9783031777387

Breast cancer is one of the most common types of cancer in women, and its early detection significantly improves the survival rate. Although mammography is one of the least invasive and most widely used methods in the diagnostic process, its complexity and subjectivity in medical interpretation present significant challenges. In this article, we propose a new approach that supports the breast cancer diagnosis process by assisting in the classification of mammography images as malignant or benign, or through the BIRADS system. Our proposal consists of two phases. Initially, we implemented the FP-Growth algorithm on patients' clinical data, analyzing variables such as age and sex to identify frequent patterns. this allows us to explore, group, and visually characterize shared findings and trends among clinical data, which is useful for doctors when creating risk groups or establishing a pre-diagnosis based on the patient's profile. In this phase, we also prepared the images for training the different models. Subsequently, we combined the strengths of two models through stacking: the Random Forest (RF) model and Convolutional Neural Networks (CNN) with knowledge transfer, to improve image classification and diagnosis. We also explored other methods such as CNN and Support Vector Machine (SVM) to compare the accuracy of the proposed methodology against conventional techniques. the developed models were trained using public datasets: "the Chinese Mammography database" [2] and "the INbreast database" [3]. the accuracy of the method is evaluated using various classification-related metrics, such as Accuracy, Precision, F1 Score, and Recall. the results show that combining base models using a stacking strategy achieves significantly superior performance compared to individual models, with ideal scores in accuracy, recall, and F1 score using k-fold cross-validation in the meta-model. these excellent results suggest that combining multiple base models more effectively capture

关键词： Machine learning data Science FP-Growth Random Forest Convolutional Neural Networks Transfer learning Support Vector Machine Medical Image Analysis Breast Cancer Diagnosis

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Optimizing Pedestrian Paths to Minimize Exposure to Urban Pollution through Traffic data Analysis 25th

Optimizing Pedestrian Paths to Minimize Exposure to Urban Po...

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25th international conference on intelligent data engineering and automated learning

作者： Nadal, Silvia Jordan, Jaume Sanchez-Anguix, Victor Alberola, Juan M. Julian, Vicente Botti, Vicent Univ Politecn Valencia Valencian Res Inst Artificial Intelligence Cami Vera S-N Valencia 46022 Spain Univ Politecn Valencia Grp Sistemas Optimizac Aplicada Cami Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

the increase in urban population translates into denser urban traffic and, in turn, more environmental pollution. By taking advantage of the high level of sensorisation in smart cities, pollution levels can be monitored in real time, thus helping citizens to make informed decisions. In this research, we propose a healthy routing system. For this purpose, we estimate the pollution dispersion in the urban area from traffic intensity data. then, the fastest walking route for a given trip is calculated. this route is evaluated with respect to the pollution that a pedestrian would be exposed to by completing it at a certain time. Finally, the system proposes alternative routes to avoid streets with a higher accumulation of harmful particles. the results demonstrate the feasibility of our proposal for the creation of healthy routes and, furthermore, the usefulness of the system as a real-time pollution estimator.

关键词： Smart cities Optimization Traffic data Air pollution

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Blueprint of Tomorrow: Contrasting Off-Line and On-Line Drawing Tasks for Alzheimer's Disease Screening 25th

Blueprint of Tomorrow: Contrasting Off-Line and On-Line Draw...

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25th international conference on intelligent data engineering and automated learning

作者： Hosseini-Kivanani, Nina Salobrar-Garcia, Elena Elvira-Hurtado, Lorena Salas, Mario Schommer, Christoph Leiva, Luis A. Univ Luxembourg Esch Sur Alzette Luxembourg Ramon Castroviejo Inst Ophthalmol Res Madrid Spain Univ Complutense Madrid Madrid Spain Hosp Clin San Carlos Geriatr Serv Memory Unit Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Alzheimer's disease (AD) is the leading cause of dementia. Although there is currently no cure for AD, early detection of cognitive decline can help clinicians mitigate its impact. Recently, Machine learning (ML) approaches have been developed to automatically analyze handwriting and hand-drawing tasks to support the early diagnosis of AD. In this paper, we study pentagon and clock drawing tests using both off-line (scanned image pixels) and on-line (discrete point sequences) data as input to several ML models (i.e., DensNet, ResNet, EfficientNet, RNN, LSTM, and GRU). Our study is the first to determine the most effective modality (on-line vs. off-line) and drawing tasks to distinguish healthy controls from AD patients (binary classification) as well as two stages of AD severity (multi-class classification). Our results suggest that, contrary to other domains, the off-line modality outperforms the on-line one, sometimes by a large margin: 90% vs. 60% accuracy in binary classification and 53% vs. 82% accuracy in multi-class classification. this suggests that, for drawing tasks and small-scale datasets, image-based representations may be more effective in predicting AD than those relying on more complex data representations.

关键词： Alzheimer disease Off-line handwriting On-line handwriting Deep learning data augmentation Classification

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Optimizing Vehicle Coordination at Multi-lane Intersections Using Traffic Control Algorithms 25th

Optimizing Vehicle Coordination at Multi-lane Intersections ...

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25th international conference on intelligent data engineering and automated learning

作者： Gonzalez, Cesar L. Delgado, Santiago L. Alberola, Juan Miguel Nino, Luis F. Julian, Vicente Univ Politecn Valencia Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Nacl Colombia Lab Invest Sistemas Inteligentes LISI Ingn Sistemas Ind Carrera 45 26-85 Bogota 111321 Colombia Univ ECCI Grp Invest SIGCIENTY Carrera 10 19-64 Bogota 110311 Colombia

ISBN: (纸本)9783031777370;9783031777387

intelligent Transportation Systems (ITS) encompass several strategies aimed at ensuring safe and efficient vehicle-to-vehicle interactions on roads. these strategies involve the implementation of advanced technologies, including intelligent traffic lights, wireless (V2X) communication systems, visual recognition systems, and algorithms. In this research, we introduce the implementation of a distributed algorithm tailored for managing highly complex road infrastructure, particularly intersections in urban areas with multiple lanes. Our evaluation of the system's performance involves applying a type of density distribution called "Biased" and comparing it against a traditional traffic control system (traffic lights - "Green Wave"). the inclusion of this type of density distribution aims to align our approach with real-world traffic conditions, specifically the variation in densities across different lanes. this comprehensive assessment allows for a more thorough evaluation of system performance.

关键词： Autonomous driving Distributed intersection management Multiple lanes intelligent transportation system Density distribution

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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...

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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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Enhancing Customer Service Efficiency in the Holy Makkah Municipality Using Machine learning 25th

Enhancing Customer Service Efficiency in the Holy Makkah M...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Alharbi, Amal Alozayri, Samaher King Abdulaziz University Jeddah Saudi Arabia Holy Makkah Municipality Makkah Saudi Arabia

ISBN: (纸本)9789819605729

this study investigates the application of Machine learning to enhance customer service efficiency within the Holy Makkah Municipality, focusing on the Tasahalat service. By analyzing historical data, we developed a model designed to automatically classify customer inquiries and deliver prompt, automated responses. Several Machine learning algorithms, including Support Vector Machines, Naive Bayes, and Random Forest, were employed in the process. the research emphasizes the superior performance of the Random Forest algorithm, which achieved an impressive accuracy of 93% and a robust F1 score. By utilizing Natural Language Processing techniques to handle Arabic-language data, the model substantially improves response times and service quality, leading to enhanced customer satisfaction and more efficient municipal operations. this research underscores the potential of integrating advanced Machine learning technologies into public services, setting the stage for future innovations in the sector. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

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8th international conference on intelligent Transport Systems, INTSYS 2024

8th International Conference on Intelligent Transport System...

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8th international conference on intelligent Transport Systems, INTSYS 2024

ISBN: (纸本)9783031863691

the proceedings contain 25 papers. the special focus in this conference is on intelligent Transport Systems. the topics include: Interoperable Traceability in Supply Chains: A Use Case in Agritech;treemob: Expressive Mobility data Representation through Tree-Based Structures;From GPS Traces to Individual Emission Exposure: A data-Driven Four-Step Process;capacity Vehicle Routing Problem with Time Windows: Simulation Tool for Footprint Network Design;chain of Portable Health Folders: A Systematic Literature Review;integrating Metro Infrastructure in Circular Food Supply Chains: A Model for Decentralized Quito’s Food Bank Network Redesign;An AutoML Approach for Bike Demand Forecasting and Redistribution;adaptive Stop-Skipping Scheduling Approach Using Reinforcement learning;machine learning Approach for Labeling Undetected Planned Trips in Public Transport Operators;reinforcement learning Algorithms with Graph Convolution Networks for Traffic Signal Control;optimizing intelligent Transportation Systems with Multi-agent Reinforcement learning: A Socio-economic Impact Assessment;a Mobile Application to Secure Pedestrians Interacting with automated Vehicles;A Simulation-Based Security Benchmarking Approach for Assessing Cooperative Driving Automation (CDA) Applications;wireless Interference and Regulatory Frameworks for Frequency Allocation in V2X Communication Systems;runtime Norms Regulation Framework for Drones’ Smart Cities Applications;a data-Driven Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Traffic Scenarios;federated learning for Lane-Change Prediction;Evaluating Traffic Control Strategies for Autonomous Shuttle in Different AV Penetration, Using SUMO Traffic Simulation;Adaptive Video Bitrate Allocation for Remotely Operated Vehicles (ROV);experimental Evaluation of Road-Crossing Decisions by Autonomous Wheelchairs Against Environmental Factors;impact of Network Delays on Edge-Assisted Platooning Systems in 5G Networks: Addressing Lat

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AutoTable: Effective and Efficient automated Feature Transformation for Tabular data 25th

AutoTable: Effective and Efficient Automated Feature Transf...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Huang, Shanshan Zhu, Junpeng Zhang, Fengyan Cai, Peng Dong, Qiwen School of Data Science and Engineering East China Normal University No. 3663 Zhongshan North Road Shanghai200062 China

ISBN: (纸本)9789819605781

Mission-critical data are commonly organized as tables within relational databases, and feature transformation from these tabular data is a pivotal component of the machine learning pipeline for business intelligence. However, automating this process poses a significant challenge, primarily due to the exponential growth in the size of the search space with an increase in the number of features (i.e., columns or dimensions of the table) and transform functions. this paper presents AutoTable, an effective and efficient feature transformation framework for tabular data using reinforcement learning. Specifically, AutoTable formulates the feature transformation problem as a search process carried out on a transformation tree, which offers a more well-structured search space and facilitates fine-grained exploration, empowering domain experts to perform AFT tasks with minimal statistical and machine learning expertise. To further improve search performance, we propose merging and lazy loading mechanisms. Experimental results demonstrate that AutoTable outperforms state-of-the-art approaches in terms of both efficiency and effectiveness. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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