Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is mor...
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The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-anima...
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In modern process industries, maintaining precise weight measurements is critical for ensuring product quality and operational efficiency. Accurate weight measurement systems not only aid in meeting regulatory standar...
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Crime prevention in smart city infrastructure significantly impacts improving quality of human life. However, the substantial increase in the urban population in recent years affects accuracy, safety, and security. Th...
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The purpose of this paper is to investigate the impact of Significant Q-Assignment (SQN) on the convenience of multiclass portrayal estimations. To create a framework that blends DQN with existing supervised multiclas...
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Knowledge graphs, which indicate knowledge as a semantic graph, have stirred up notable worries in the professional as well as academic communities. Many researchers believe that their ability to provide semantically ...
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Gallium nitride-based high-electron-mobility-transistors(HEMTs) have gained widespread interest and become primary candidates for next-generation high-frequency and high-power RF electronics, due to their wide bandgap...
Gallium nitride-based high-electron-mobility-transistors(HEMTs) have gained widespread interest and become primary candidates for next-generation high-frequency and high-power RF electronics, due to their wide bandgap,high breakdown field, and strong polarization-induced high-density 2-dimensional electron gas(2DEG) at the heterojunction interface.
The technical advancements in modernizing the world in every way possible have increased the usage of power and energy. Due to this, an increase in tariff, decrease in power quality, and frequent blackouts have increa...
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Opportunistic internet of things (OppIoT) is a class of opportunistic networks, where the data are transmitted in a broadcast manner and shared among the nodes (i.e., IoT devices and human communities' devices) th...
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Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** ...
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Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.
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