DEES: a real-time system for event extraction from disaster-related web text

Algiriyage, Nilani; Prasanna, Raj; Stock, Kristin; Doyle, Emma E.H.; Johnston, David

doi:10.1007/s13278-022-01007-2

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…In a separate yet technologically innovative exploration, Algiriyage et al discuss the utilization of advanced algorithms for analyzing traffic data from surveillance video footage [19]. Their study offers students insights into real-time urban dynamics, highlighting the potential of modern technologies in traffic flow estimation.…”

Section: Emerging Methodologies In Urban Analysismentioning

confidence: 99%

“…Their study offers students insights into real-time urban dynamics, highlighting the potential of modern technologies in traffic flow estimation. This aspect is crucial for urban planning and development, emphasizing the potential of leveraging cutting-edge technology to address urban challenges and enrich the academic curriculum with practical, real-world applications [19].…”

Section: Emerging Methodologies In Urban Analysismentioning

confidence: 99%

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Integrating Machine Learning in Urban Pedagogy: Addressing Homelessness in Skid Row

Meshkani

2024

Architecture

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This paper investigates the application of machine learning in urban and architectural education, with a focus on addressing homelessness in Skid Row, Los Angeles. It presents a case study of an urban design studio utilizing data-driven methods to propose transitional housing solutions, emphasizing the importance of design in the context of social justice. The study explores the use of machine learning and digital cartography for a detailed analysis of Skid Row’s dense homeless population, offering students a thorough insight into urban challenges. The research also identifies the complexities involved in integrating these technologies into educational frameworks, including issues with data accuracy, technical hurdles, and ethical considerations. The paper concludes by advocating for an interdisciplinary, data-informed, and socially conscious approach in architectural and urban design education, highlighting its necessity in preparing students to effectively tackle contemporary urban problems.

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Section: Emerging Methodologies In Urban Analysismentioning

confidence: 99%

Section: Emerging Methodologies In Urban Analysismentioning

confidence: 99%

Integrating Machine Learning in Urban Pedagogy: Addressing Homelessness in Skid Row

Meshkani

2024

Architecture

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“…Following the general trends in crisis management (Kersten et al, 2019;Algiriyage et al, 2021;Bourgon et al, 2022), we designed an out-of-type evaluation protocol by training on a pool of events related to different types of crises (e.g., Hurricane, Storm) and testing on a particular different type (e.g., Earthquake). The aim is to evaluate if a model can deal with new types of crisis, which is crucial to ensure the portability of the models to unseen events.…”

Section: Experimental Settingsmentioning

confidence: 99%

Image and Text: Fighting the same Battle? Super Resolution Learning for Imbalanced Text Classification

Meunier,

Farah,

Moriceau

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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In this paper, we propose SRL4NLP, a new approach for data augmentation by drawing an analogy between image and text processing: Super-resolution learning. This method is based on using high-resolution images to overcome the problem of low resolution images. While this technique is a common usage in image processing when images have a low resolution or are too noisy, it has never been used in NLP. We therefore propose the first adaptation of this method for text classification and evaluate its effectiveness on urgency detection from tweets posted in crisis situations, a very challenging task where messages are scarce and highly imbalanced. We show that this strategy is efficient when compared to competitive state-of-the-art data augmentation techniques on several benchmarks datasets in two languages.

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“…Initially, the YOLOv5 real-time video analytics system [105] was used to analyze the video stream, detecting and tracking the movements of visible vehicles. The YOLO algorithm divides the original image into a grid of N × N cells.…”

mentioning

confidence: 99%

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Lopukhova,

Abdulnagimov,

Voronkov

et al. 2024

Applied Sciences

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Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

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DEES: a real-time system for event extraction from disaster-related web text

Cited by 5 publications

References 41 publications

Integrating Machine Learning in Urban Pedagogy: Addressing Homelessness in Skid Row

Integrating Machine Learning in Urban Pedagogy: Addressing Homelessness in Skid Row

Image and Text: Fighting the same Battle? Super Resolution Learning for Imbalanced Text Classification

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

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