Seamless communication between authorities, people, and smart devices is crucial in today's globally interconnected world. Unprecedented demands on software design result from the advent of ubiquitous connectivity...
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The emotion extraction or opinion mining is one of the key tasks for any text processing frameworks. In recent times, the use of opinion mining has gained a lot of potential due to the application of the potential cus...
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This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term m...
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This paper proposes two polynomial-time approximation algorithms for allocating servers to design a consistency-aware multi-server network for delay-sensitive applications. Each algorithm selects servers and determine...
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Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for...
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Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.
Creating programming questions that are both meaningful and educationally relevant is a critical task in computerscience education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generat...
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In this study, we review the fundamentals of IoT architecture and we thoroughly present the communication protocols that have been invented especially for IoT technology. Moreover, we analyze security threats, and gen...
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Exploring the potential of technology in addressing Sustainable Development Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both...
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Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter ...
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Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.
A quantitative susceptibility mapping (QSM) approach using single-orientation imaging data is proposed in this study. The proposed method generates local field maps at five predefined orientations via deep learning fr...
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