Road traffic can be predicted by machine learning equally effectively as by complex microscopic model

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Сен. 4, 2023

Abstract Since high-quality real data acquired from selected road sections are not always available, a traffic control solution can use software simulators working offline. The results show that in contrast to microscopic simulation, the algorithms employing neural networks work real-time, so they be used, among others, determine speed displayed on variable message signs. This paper describes an experiment develop and test machine learning models, i.e., long short-term memory, gated recurrent unit networks, stacked autoencoder networks. It compares their effectiveness with prediction generated using widely recognized simulator analyzes at level of individual vehicles.

Язык: Английский

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A digital twin-based motion forecasting framework for preemptive risk monitoring

Advanced Engineering Informatics, Год журнала: 2023, Номер 59, С. 102250 - 102250

Опубликована: Ноя. 14, 2023

Язык: Английский

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An effective IoT interface considering an eye-tracking method for autonomous vehicle

Internet of Things, Год журнала: 2025, Номер unknown, С. 101583 - 101583

Опубликована: Март 1, 2025

Язык: Английский

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Boost-Fmh: A Multi-Objective Algorithm Based on Boost Weighted Fusion Strategy

Опубликована: Янв. 1, 2025

Язык: Английский

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Multi-Camera Association Tracking Algorithm for Pedestrian Target Based on Difference Image

Systems and Soft Computing, Год журнала: 2025, Номер unknown, С. 200282 - 200282

Опубликована: Май 1, 2025

Язык: Английский

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Using the Deep Learning Algorithm to Determine the Presence of Sacroiliitis from Pelvic Radiographs

Life, Год журнала: 2025, Номер 15(6), С. 876 - 876

Опубликована: Май 29, 2025

Deep learning (DL) techniques have demonstrated remarkable capabilities in recognizing complex patterns medical imaging data. In recent years, DL has been increasingly applied clinical medicine for disease diagnosis and progression prediction. This study aimed to develop validate a model detecting sacroiliitis using pelvic anteroposterior (AP) radiographs. We retrospectively analyzed 1853 patients with AP radiographs, including 3706 sacroiliac joints (SIJs). Pelvic radiographs served as input data the development, while presence or absence of confirmed by computed tomography (CT) was used reference standard output Based on CT findings, 1463 right SIJs showed evidence sacroiliitis, 390 had no sacroiliitis. Similar findings were observed left SIJs. The dataset split 70% (1297 images) training 30% (556 validation. areas under curve (AUC) our validation 0.871 (95% confidence interval (CI): 0.834–0.907) 0.869 CI: SIJs, respectively. Diagnostic accuracies sides 85.4% 86.3%, These results demonstrate that trained CT-confirmed diagnoses can effectively aid

Язык: Английский

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Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning

Transportation Research Part C Emerging Technologies, Год журнала: 2024, Номер 167, С. 104804 - 104804

Опубликована: Авг. 14, 2024

Язык: Английский

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Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead

IoT, Год журнала: 2023, Номер 4(4), С. 486 - 513

Опубликована: Окт. 24, 2023

The falling cost of IoT cameras, the advancement AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled widespread deployment surveillance cameras with ability to automatically analyze streaming video feeds detect events interest. While analytics is currently largely performed in cloud, edge computing has emerged as a pivotal component due its advantages low latency, reduced bandwidth, enhanced privacy. However, distinct gap persists between state-of-the-art algorithms successful practical implementation edge-based systems. This paper presents comprehensive review more than 30 research papers published over last 6 years on (IE-SVA) are analyzed across 17 dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths weaknesses diverse implementations. Our findings suggest that certain critical topics necessary realization IE-SVA systems not sufficiently addressed current research. Based these observations, propose trajectories short-, medium-, long-term horizons. Additionally, explore trending other areas can significantly impact evolution

Язык: Английский

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Edge Computing Systems for Streaming Video Analytics : Trail Behind and the Paths Ahead

Опубликована: Авг. 4, 2023

The falling cost of cameras, the advancement AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment surveillance cameras with ability to automatically analyze streaming video feeds detect events interest. While analytics is currently largely done in cloud, edge computing has emerged as a pivotal component due its advantages low latency, reduced bandwidth, enhanced privacy. However, distinct gap persists between state-of-the-art successful practical implementation edge-based systems. This paper presents comprehensive review more than 30 research papers published over last 6 years on are analyzed across 17 dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths weaknesses diverse implementations. Our findings suggest that certain critical topics necessary realization systems not sufficiently addressed current research. Based these observations, propose trajectories short, medium, long term horizons. Additionally, explore trending other areas can significantly impact field analytics.

Язык: Английский

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Visual Sentinel: Data Analytics for Missing Subject Identification

Опубликована: Дек. 14, 2023

The goal of the "Visual Sentinel: Video Analytics for Missing Subject Identification" project is to automate process identifying missing subjects from CCTV video by utilizing cutting-edge machine learning and computer vision techniques. One project's goals create a real-time surveillance system that integrates face recognition technology increased precision accuracy. has ability save lives bring families back together speeding up search recovery procedure. To effectively locate subjects, uses de-blurring methods algorithms. matches people in against specified data collection, giving authorities access timely information, according key results. sum up, Visual Sentinel provides strong practical solution enables security law enforcement professionals.

Язык: Английский

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