There has been a recent trend in developing applications that use deep learning models on mobile devices. However, using deep learning models that require a large amount of computation solely on mobile devices is limi...
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The growth of the Internet of Things (IoT) in various industries has been unprecedented over the past few decades. However, IoT devices are prone to malicious network entities (i.e., attacks) such as data theft, phish...
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In this paper, we study distributed and centralized approaches of Q-learning for multi-objective optimization of binary problems and investigate their characteristics and performance on complex epistatic problems usin...
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The increasing penetration of renewable energy resources, such as photovoltaic (PV) systems, has caused significant concerns in power systems. As one of theses concerns, the escalating number of reported cyber-attacks...
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Lead-free BaZrS3 is considered a highly promising substitute for lead-based halide perovskites. This research involved designing and simulating a perovskite solar cell using the Glass/ITO/ZnO/BaZrS3/Cu2O/Ni structure....
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We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approa...
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The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic...
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Tire defect detection has significant industrial value and has been a research topic in both academia and industry. Despite its importance, prior works does not consider the practical manufacturing circumstances, wher...
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This study developed a machine learning model to forecast electrical demand for smart micro-grids with the existing of numerical weather forecasting (NWP). The model uses three techniques, linear regression (LR), Long...
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