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Abstract Machine learning is a growing field of research with many applications. It provides a series of techniques able to solve complex nonlinear problems, and that has promoted their application for statistical downscaling. Intercomparison exercises with other classical methods have so far shown promising results. Nevertheless, many evaluation studies of statistical downscaling methods neglect the analysis of their extrapolation capability. In this study, we aim to make a...
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The aim of this book is to provide a holistic view to ensure that AI empowers educators and learners, not over-empowers them, and that future developments and practices are truly for the common good. Artificial intelligence (Al) is increasingly having an impact on education, bringing opportunities as well as numerous challenges. These observations were noted by the Council of Europe’s Committee of Ministers in 2019 and led to the commissioning of this report, which sets out to examine the...
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Abstract Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state‐of‐the‐art AI in education, and exceedingly narrow views on the functions of education in society. In this article, we provide a review of existing AI systems in education and their pedagogic and...
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Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state-of-the-art AI in education, and exceedingly narrow views on the functions of education in society. In this article, we provide a review of existing AI systems in education and their pedagogic and educational assumptions. We...
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Climate change is a global issue that must be considered and addressed immediately. Many articles have been published on climate change mitigation and adaptation. However, new methods are required to explore the complexities of climate change and provide more efficient and effective adaptation and mitigation policies. With the advancement of technology, machine learning (ML) and deep learning (DL) methods have gained considerable popularity in many fields, including climate change. This...
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Indoor thermal comfort in smart buildings has a significant impact on the health and performance of occupants. Consequently, machine learning (ML) is increasingly used to solve challenges related to indoor thermal comfort. Temporal variability of thermal comfort perception is an important problem that regulates occupant well-being and energy consumption. However, in most ML-based thermal comfort studies, temporal aspects such as the time of day, circadian rhythm, and outdoor temperature are...
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This study managed to create thermal comfort conditions at three temperatures (24°C-T24, 26°C-T26, and 28°C-T28) by adjusting clothing and air velocity. Thirty- six subjects (18 males and 18 females) were exposed to each of the three conditions for 4.5 h in a design balanced for order of presentation of conditions. During each exposure, they rated the physical environment, their comfort, the intensity of acute subclinical health symptoms, and their mental load, and they performed a...
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The relation between electroencephalogram signals, thermal comfort, and cognitive performance in neutral to hot indoor environment was investigated. The experiments were carried out at four temperatures: 26ºC, 30ºC, 33ºC, and 37ºC, and two relative humidity levels: 50% and 70%. Thirty-two subjects were exposed for 175 min. The electroencephalogram signals were measured for 30 min 25 min after the onset of exposure while the recruited subjects performed neurobehavioral tests and rated their...
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