Accurate traffic state estimations (tSEs) within road networks are crucial for enhancing intelligenttransportation systems and developing effective traffic management strategies. traditional tSE methods often assume ...
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Accurate traffic state estimations (tSEs) within road networks are crucial for enhancing intelligenttransportation systems and developing effective traffic management strategies. traditional tSE methods often assume homogeneous traffic, where all vehicles are considered identical, which does not accurately reflectthe complexities of real traffic conditions that often exhibit heterogeneous characteristics. In this study, we address the limitations of conventional models by introducing a novel tSE model designed for precise estimations of heterogeneous traffic flows. We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements: basic traffic parameters, heterogeneous vehicle speeds, heterogeneous vehicle flows, and mixed flow rates. this system aids in capturing the unique traffic characteristics of different vehicle types. Our proposed high-dimensional fuzzy tSE model, termed HiF-tSE, integrates three main processes: feature selection, which eliminates redundanttraffic features using Spearman correlation coefficients;dimension reduction, which utilizes the t-distributedstochasticneighborembedding machine learning algorithmto reduce high-dimensional traffic feature data;and FCM clustering, which applies the fuzzy C-means algorithmto classify the simplified data into distinct clusters. the HiF-tSE model significantly reduces computational demands and enhances efficiency in tSE processing. We validate our model through a real-world case study, demonstrating its ability to adaptto variations in vehicle type compositions within heterogeneous traffic and accurately representthe actual traffic state.
In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use K-Means algorithmto cluster the learner's ...
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ISBN:
(纸本)9781538684313
In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use K-Means algorithmto cluster the learner's help-seeking behavior data to getthe classification label of the learner's help-seeking behavior. Secondly, we use the t-distributedstochasticneighborembedding(t-sne) algorithmto reduce the dimension of the data to visualize the clustering result. Finally, the learner's help-seeking behavior data and the help-seeking behavior classification labels are used as training data to train the Naive Bayesian model so as to automatically obtain the help-seeking behavior classification for the data generated by the new learner. Via the analysis and processing of the help-seeking behavior data using the method proposed in this paper, it shows thatthis method can effectively find online learners' help-seeking behavior classifications.
Background and Aims: the aim of our study was to develop and evaluate a deep learning algorithm for the automated detection of small-bowel ulcers in Crohn's disease (CD) on capsule endoscopy (CE) images of individ...
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Background and Aims: the aim of our study was to develop and evaluate a deep learning algorithm for the automated detection of small-bowel ulcers in Crohn's disease (CD) on capsule endoscopy (CE) images of individual patients. Methods: We retrospectively collected CE images of known CD patients and control subjects. Each image was labeled by an expert gastroenterologist as either normal mucosa or containing mucosal ulcers. A convolutional neural network was trained to classify images into either normal mucosa or mucosal ulcers. First, we trained the network on 5-fold randomly split images (each fold with 80% training images and 20% images testing). We then conducted 10 experiments in which images from n - 1 patients were used to train a network and images from a different individual patient were used to testthe network. Results of the networks were compared for randomly split images and for individual patients. Area under the curves (AUCs) and accuracies were computed for each individual network. Results: Overall, our dataset included 17,640 CE images from 49 patients: 7391 images with mucosal ulcers and 10,249 images of normal mucosa. For randomly split images results were excellent, with AUCs of .99 and accuracies ranging from 95.4% to 96.7%. For individual patient-level experiments, the AUCs were also excellent (.94-.99). Conclusions: Deep learning technology provides accurate and fast automated detection of mucosal ulcers on CE images. Individual patient-level analysis provided high and consistent diagnostic accuracy with shortened reading time;in the future, deep learning algorithms may augment and facilitate CE reading.
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