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Traffic Transformer: Transformer-based framework for temporal traffic accident prediction
Published 2024“…Our proposed Traffic Transformer employs the sophisticated multi-head attention mechanism in lieu of the widely used recurrent architecture. This significant shift enhances the model's ability to capture long-range dependencies within time series data. …”
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W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting
Published 2022“…Among several merits of transformers, the ability to capture long-range temporal dependencies and interactions is desirable for time series forecasting, leading to its progress in various time series applications. …”
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Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)
Published 2024“…This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. …”
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Internet Searches for Medical Symptoms Before Seeking Information on 12-Step Addiction Treatment Programs: A Web-Search Log Analysis
Published 2019“…Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried.…”
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Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA and Explainability
Published 2025“…<p dir="ltr">Objective: Despite advancements in artificial intelligence (AI) for predicting cardiac arrest (CA) with multivariate time-series vital signs data, existing models continue to face significant problems, particularly concerning balance, efficiency, accuracy, and explainability. …”
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Visitors off the trail: Impacts on the dominant plant, bryophyte and lichen species in alpine heath vegetation in sub-arctic Sweden
Published 2021“…With a greater decrease in taller forbs and shrubs than in graminoids and prostrate plants, a greater decrease in lichen than in bryophyte species, and a change in vegetation composition. …”
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Effectiveness of Positron Emission Tomography for Predicting Chemotherapy Response in Colorectal Cancer Liver Metastases
Published 2010“…There appears to be a correlation between decreasing PET uptake and reduction in tumor burden; however, hypometabolic lesions may still harbor viable malignant cells.10,11 In addition, the authors reviewed the temporal relationship between chemotherapy and false-negative and false-positive results on PET. …”
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Effect of consanguinity on birth weight for gestational age in a developing country
Published 2007“…No significant difference was observed in the decrease in birth weight between the first- and second-cousin marriages. …”
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A novel hybrid methodology for fault diagnosis of wind energy conversion systems
Published 2023“…Feature selection pre-processing is an important step to increase the accuracy of the classification algorithm and decrease the dimensionality of a dataset. …”
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Optimizing the number of players and training bout durations in soccer small‐sided games: Effects on mood balance and technical performance
Published 2025“…Therefore, coaches should consider longer continuous bouts when planning SSGs‐based training to significantly decrease TMD and enhance technical‐tactical performance in soccer SSGs.…”
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Correlation drives a strong attractive force on plasmonic photoelectrons
Published 2020“…This attraction can significantly influence the emerging photoelectron’s temporal delay behavior.…”
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A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems
Published 2021“…The results of the conducted computational experiments indicate that an increase in infrastructure investment through high speed chargers can significantly decrease the size of the necessary fleets. …”
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MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model
Published 2025“…MCDFN utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) to extract spatial and temporal features from time series data. Comparative benchmarking against seven other deep-learning models validates MCDFN’s efficacy, showing it outperforms its counterparts across key metrics with a mean squared error (MSE) of 23.5738, root mean squared error (RMSE) of 4.8553, mean absolute error (MAE) of 3.9991, and mean absolute percentage error (MAPE) of 20.1575%. …”
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