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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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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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Modification of glucocorticoid effects on body weight gain, plasma lipids by changes in diet composition
Published 2003“…The four groups [group I (HC, placebo), group II (HC, prednisolone), group III (HF, placebo), group IV (HF, prednisolone)] were pair fed and given the mean amount of food eaten by the group with the least amount of energy intake on the previous day. …”
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Revealing the Adoption Factors of Electric Vehicles in Dubai
Published 2024“…Electric vehicles (EVs) have gained interest in the last decades, as a significant amount of people in Dubai depend on private vehicles. …”
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A clustering metaheuristic for large orienteering problems
Published 2022“…The Orienteering Problem is a challenging NP-hard problem, such that obtaining optimal or near optimal solutions usually requires a significant amount of computation for large and even moderately large instances. …”
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A snapshot of current medical research: The notion of supply and demand
Published 2017“…Research has never been more in demand and pursued by various health disciplines worldwide as it is at present. Contemplating the amount, type and quality of current medical research over the last decade leads one to consider the driving forces behind such research. …”
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Alterations in blood Lipoprotein & Apolipoprotein B profile induced by moderate acute & chronic alcohol intake in the rat model. (c2002)
Published 2002“…On the other hand, a 3% alcohol consumption (as beer or whisky) maintained for a two months period did not have an effect on fasting plasma TAG concentration while total and LDL cholesterol concentrations showed a significant decrease and HDL cholesterol concentration was moderately increased. …”
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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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Improved properties and salt rejection of polysulfone membrane by incorporation of hydrophilic cobalt-doped ZnO nanoparticles
Published 2024“…Overall, the results of this study showed that embedding a small amount of Co<sup>2+</sup> ions doped ZnO NPs with PSf has significant potential to be applied in industrial-scale membrane applications.…”
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Circular economy application for a Green Stadium construction towards sustainable FIFA world cup Qatar 2022™
Published 2021“…<p dir="ltr">The construction industry is responsible for a significant amount of raw material consumption and environmental footprints. …”
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Effectiveness of Positron Emission Tomography for Predicting Chemotherapy Response in Colorectal Cancer Liver Metastases
Published 2010“…Therefore, we reviewed our patient population for the temporal relationship between PET results and tissue-proven hepatic metastases from colorectal carcinoma. …”
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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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Characterizing the dynamics of climate and native desert plants in Qatar
Published 2024“…Vegetation growth depended not only on rainfall amount but also on the number of rainfall events and accumulation. …”
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Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet
Published 2025“…<p dir="ltr">The exponential increase in the adoption of Electric Vehicles (EVs) presents significant problems to the stability of the power grid. …”