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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121-130 订阅

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T-RAPPI: A Machine learning Model for the Corredor Metropolitano 11

T-RAPPI: A Machine Learning Model for the Corredor Metropoli...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Traverso, Deneb Pacheco, Gonzalo Castañeda, Pedro San Isidro Lima Peru

ISBN: (纸本)9789897587450

the public transportation system in Lima, Peru, faces significant challenges, including bus shortages, long queues, and severe traffic congestion, which diminish service quality. these issues arise from a lack of modern management tools capable of efficiently handling the Metropolitano bus system. this paper introduces T-RAPPI, a predictive model based on Random Forest, developed to estimate bus arrival times at Metropolitano stations. Using historical data on bus arrivals and operational parameters, the model achieved exceptional accuracy, with an R2 score of 0.9998 and a MAPE of 0.0554%, demonstrating its robustness and ability to minimize prediction errors. the implementation of T-RAPPI represents a substantial improvement over existing systems, providing operators with data-driven insights to optimize route planning and bus allocation. Additionally, the model's integration into the mobile application Metropolitano + enhances the commuting experience by offering users real-time bus arrival predictions, reducing uncertainty and wait times. Future extensions of this work could include incorporating real-time traffic and weather data to further enhance prediction accuracy and expanding the model to other transit systems in Lima and beyond. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Mass transportation

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A Comparative Study of Log-Based Anomaly Detection Methods in Real-World System Logs 10

A Comparative Study of Log-Based Anomaly Detection Methods i...

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10th international conference on Internet of things, Big data and Security, IoTBDS 2025

作者： Nipa, Nadira Anjum Bouguila, Nizar Patterson, Zachary Concordia Institute for Information and Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9789897587504

the reliability and security of today’s smart and autonomous systems increasingly rely on effective anomaly detection capabilities. Logs generated by intelligent devices during runtime offer valuable insights for monitoring and troubleshooting. Nonetheless, the enormous quantity and complexity of logs produced by contemporary systems render manual anomaly inspection impractical, error-prone, and laborious. In response to this, a variety of automated methods for log-based anomaly detection have been developed. However, many current methods are evaluated in controlled environments with set assumptions and frequently depend on publicly available datasets. In contrast, real-world system logs present greater complexity, lack of labels, and noise, creating substantial challenges when applying these methods directly in industrial settings. this work explores and adapts existing machine learning and deep learning techniques for anomaly detection to function on real-world system logs produced by an intelligent autonomous display device. We conduct a comparative analysis of these methods, evaluating their effectiveness in detecting anomalies through various metrics and efficiency measures. Our findings emphasize the most efficient approach for detecting anomalies within this specific system, enabling proactive maintenance and enhancing overall system reliability. Our work provides valuable insights and directions for adopting log-based anomaly detection models in future research, particularly in industrial applications. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Industrial research

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Hybrid Framework for Real-Time Traffic Flow Estimation Using Breadth-First Search 11

Hybrid Framework for Real-Time Traffic Flow Estimation Using...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Mahdaviabbasabad, Sajjad Vanderhoydonc, Ynte Vandenberghe, Roeland Mercelis, Siegfried University of Antwerp imec IDLab Faculty of Applied Engineering Sint-Pietersvliet 7 Antwerp2000 Belgium Transport & Mobility Leuven Diestsesteenweg 71 Leuven3000 Belgium

ISBN: (纸本)9789897587450

Traffic flow data is essential for urban planning, logistics, transport management, and similar applications. However, achieving full sensor coverage across a road network is often infeasible due to high installation and maintenance costs. Simulation data from traffic models can help in filling this gap. However, calibrating and validating these traffic models is time-consuming. this paper presents a framework that combines real-time traffic flow predictions from sensor-equipped road segments with 24-hour static simulation data across an entire network. By applying a method based on the Breadth-First Search algorithm, this framework updates network-wide traffic flow by utilizing the data-driven predictions at sensor-equipped road segments and simulation data. Evaluation on a network with over 27000 road segments shows that this approach improves prediction accuracy over static simulation and is viable for real-time deployment. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Urban planning

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Confluence of Machine learning and Internet of things for E-Healthcare Security 8th

Confluence of Machine Learning and Internet of Things for E-...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Pattnayak, Parthasarathi Das, Tulip Mohanty, Arpeeta Patnaik, Sanghamitra School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India Faculty of Emerging Technologies SRI SRI University Odisha Cuttack India School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India School of Law KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819784219

In the current era, the e-communication technology is developing swiftly and has made a remarkable transformation in e-healthcare sector by facilitating efficient and secure transmission of patient’s data. Internet of things (IoT) is developing drastically in the e-healthcare sector and the intelligent devices are also improving with many new innovative enhancements. the confluence of IoT and ML is manifested in many sectors but its implementation in e-healthcare sector provides real-time surveillance and monitoring of patient’s data and health status through prediction, anticipation and the capability of taking decisions intelligently. Nowadays, cryptographic and biometric systems, anomaly detection and machine learning (ML) approaches for authentication are being widely used for ensuring the security and functioning of healthcare systems. the present study aims to know how the convergence of IoT e-healthcare management system (EHMS) and ML techniques helps to design automated systems for proper monitoring and intelligent decision making. It also focuses to present an innovative architecture to safeguard the real-time healthcare from outside threats with the consumption of less possible resources of low-powered devices. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Investigating the Safety Effects of Degraded Wireless Performance on Connected Longitudinal Driver Assistance Functions 11

Investigating the Safety Effects of Degraded Wireless Perfor...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Nagy, Roland Petho, Zsombor Kazár, Tamás Márton Turóczi, Tibor Török, Árpád Department of Automotive Technologies Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Műegyetem rkp. 3 BudapestH-1111 Hungary Jaguar Land Rover Hungary Ltd. Hungary

ISBN: (纸本)9789897587450

In the near future, Vehicle-to-Everything (V2X) based technologies will enable vehicles and other road users to exchange information with each other, even in cases where the applicability of other sensors is limited. this technology will be able to ensure the operation of advanced driver assistance systems, in cases where the other sensors are malfunctioning. In such situations, where only wireless communication can be relied upon, it is essential to be able to react to possible changes in network performance metrics. the objective of this paper is to address the aforementioned problem by characterizing the safety-risk associated with vehicle dynamic parameters and the factors influencing the network performance metrics in different scenarios. the network can be divided into seven distinct layers that are responsible for data transmission, and the research primarily focuses on the physical layer, with the objective of studying its impact on the packets sent. In the research, the Signal-to-Noise Ratio (SNR) is considered to be the primary network influencing parameter. this will facilitate the enhancement of not only the safety of transportation but also its reliability. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Accident prevention

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A Modular Detection System for Smart Cities: Integrating Monocular and LiDAR Solutions for Scalable Traffic Monitoring 11

A Modular Detection System for Smart Cities: Integrating Mon...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Borau-Bernad, Javier Ramajo-Ballester, Álvaro María Armingol Moreno, José Sanchis de Miguel, Araceli Intelligent Systems Lab University Carlos III of Madrid Av. de la Universidad 30 Leganés Madrid28911 Spain Control Learning and Systems Optimization Group University Carlos III of Madrid Av. de la Universidad 30 Leganés Madrid28911 Spain

ISBN: (纸本)9789897587450

As smart cities continue to develop, they require scalable and efficient traffic monitoring systems. this paper presents a modular detection system that switches between monocular and multimodal modes, depending on the available sensors. the monocular mode, based on the MonoLSS algorithm, offers a cost-effective vehicle detection solution using a single camera, ideal for simpler or low-budget setups. In contrast, the multimodal mode integrates camera and LiDAR data via the MVX-Net model, enhancing 3D accuracy in complex traffic scenarios. this dual-mode flexibility allows smart cities to adapt the system to their infrastructure and budgetary needs, ensuring scalability as urban demands evolve. Inference results demonstrate the superior accuracy of the multimodal approach in challenging environments while validating the efficiency of the monocular mode for simpler settings. therefore, the modular detection system offers a flexible solution that optimizes both cost and performance, effectively addressing the varied requirements of smart city traffic management. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Budget control

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A Surrogate Urban Building Energy Model for Predicting Cooling Energy Consumption in a Hot and Arid Climate 9th

A Surrogate Urban Building Energy Model for Predicting Cooli...

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9th international Building Physics conference

作者： Ahmed, Omar Wang, Liangzhu (Leon) Hassan, Ibrahim Galal Concordia Univ Dept Bldg Civil & Environm Engn Ctr Zero Energy Bldg Studies Montreal PQ Canada Texas A&M Univ Qatar Mech Engn Program Doha Qatar

ISBN: (纸本)9789819783113;9789819783090;9789819783083

Building Performance Simulation (BPS) is a potent and commonly utilized method to assess and improve building design, operation, and overall energy performance. Commonly used physics-based BPS tools often entail a high computational cost and require a significant amount of technical data and multiple assumptions for energy predictions. While data-driven approaches are contingent upon the availability of large enough volumes of high-quality data for training the prediction models. Surrogate models, which combine physics-based simulations with components of statistical or machine learning techniques have demonstrated their ability to harness the advantages of both physics-based and data-driven models to achieve superior prediction results. In this study, a surrogate model is utilized to predict the cooling energy consumption for a mixed-use district. A hybrid approach is employed, which combines machine learning techniques with representative building physics-based archetypes. High-rise building archetypes, representative of the building stock in the district, are modeled using EnergyPlus software. A parametric simulation is then automated using RStudio by modifying crucial parameters related to the building envelope characteristics, building geometry, and building operational parameters, including occupant behavior. the proposed methodology offers a solution for a more accurate representation of occupant behavior in machine learning surrogate building energy models. the artificial dataset generated is then used to train three neural network models, one for each of the predominant building typologies available in the district. the predictions made by the machine learning models are then benchmarked against unseen EnergyPlus results, and the prediction performance of the models is validated using findings in the literature.

关键词： Surrogate Modeling Machine learning Building Performance Analysis

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Technology Acceptance Modelling for Investigating the Uptake of Electric, Connected, Autonomous, and Shared Mobility Technologies 11

Technology Acceptance Modelling for Investigating the Uptake...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Karathansopoulou, Konstantina Mitsiogianni, Despoina Tsaousi, Eleni Dimitrakopoulos, George Georgiadis, Dimitris Department of Informatics and Telematics Harokopio University of Athen Greece

ISBN: (纸本)9789897587450

this paper introduces QETAM (Quantitative Effect and Technology Acceptance Modelling), the first quantitative user acceptance model for evaluating the impact and adoption of Electric, Connected, Autonomous, and Shared (ECAS) mobility technologies. Developed in Python, QETAM leverages data collected through specifically designed questionnaires to assess key adoption factors, including technological reliability, user attitudes, infrastructure, and environmental considerations. the model accounts for the interconnected nature of ECAS technologies, emphasizing synergies between electric propulsion, connectivity, autonomy, and shared mobility services. Utilizing advanced statistical techniques, it analyzes large-scale datasets to provide a data-driven understanding of user behavior. Beyond academic contributions, QETAM offers practical insights for policymakers and industry stakeholders, supporting the transition toward sustainable and user-centric mobility solutions. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： engineering education

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data-Driven Shielding of Online Reinforcement learning: A Stormwater Pond Case Study 11th

Data-Driven Shielding of Online Reinforcement Learning: A ...

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11th IFIP WG 2.2 international conference on Fundamentals of Software engineering, FSEN 2025

作者： Kim, Esther Hahyeon Goorden, Martijn Angelo Larsen, Kim Guldstrand Nielsen, thomas Dyhre Department of Computer Science Aalborg University Aalborg East9220 Denmark Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands

ISBN: (纸本)9783031870538

To synthesize a safe and optimal controller for switched hybrid systems, one can first synthesize a shield that ensures safety, and then apply reinforcement learning within the constraints of the shield to obtain the desired controller. However, developing such a shield for switched hybrid systems typically requires a full model of the environment, which is not always available. Instead, historical data of the environment might be available. In this paper, we introduce a method for the construction of safety shields based on different scenarios captured in historical data. We show how individual shields for different scenarios can be combined to obtain a single shield that is provably safe within the bounds of the observed scenarios. We demonstrate the method using an industrial case study of a stormwater detention pond, which includes ten years of historical data of different rain events/scenarios. Our experimental results show that the shielded optimal controller ensures safety across all individual historical rain scenarios compared to the unshielded optimal controller. Additionally, we empirically show that the shield may also generalize for scenarios not covered by the historical data. © IFIP international Federation for Information Processing 2025.

关键词： Storm sewers

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RUTGe: Realistic Urban Traffic Generator for Urban Environments Using Deep Reinforcement learning and SUMO Simulator 11

RUTGe: Realistic Urban Traffic Generator for Urban Environme...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Guillén, Alberto Bazán Barbecho Bautista, Pablo A. Igartua, Mónica Aguilar Barcelona Spain Cuenca Ecuador

ISBN: (纸本)9789897587450

We are witnessing a profound shift in societal and political attitudes, driven by the visible consequences of climate change in urban environments. Urban planners, public transport providers, and traffic managers are urgently reimagining cities to promote sustainable mobility and expand green spaces for pedestrians, bicycles, and scooters. To design more sustainable cities, urban planners require realistic simulation tools to optimize mobility, identify location for car chargers, convert streets to pedestrian zones, and evaluate the impact of alternative configurations. However, realistic traffic profiles are essential to produce meaningful simulation results. Addressing this need, we propose a traffic generator based on deep reinforcement learning integrated with the SUMO simulator. this tool learns to generate an instantaneous number of vehicles throughout the day, aligning closely with the target profiles observed at the traffic monitoring stations. Our approach generates accurate 24-hour traffic patterns for any city using minimal statistical data, achieving higher accuracy compared to existing alternatives. In particular, our proposal demonstrates a highly accurate 24-hour traffic adjustment, with the generated traffic deviating only by about 5% from the real target traffic. this performance significantly exceeds that of current SUMO tools like RouteSampler, which struggle to accurately follow the total daily traffic curve, especially during peak hours when severe traffic congestion occurs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Traffic congestion

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