Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
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Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs. [PDF]
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A wireless sensor network for coal mine safety powered by modified localization algorithm. [PDF]
Ul Hassan HZ, Wang A, Mohi-Ud-Din G.
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Intraoperative Techniques for Language Mapping in Brain Surgery: A Comparison Between Direct Electrical Stimulation (DES) and Electrocorticography (ECoG). [PDF]
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A hierarchical overlay network optimisation model for enhancing data transmission performance in blockchain systems. [PDF]
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Multi-objective optimization for dynamic logistics scheduling based on hierarchical deep reinforcement learning. [PDF]
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Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network. [PDF]
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Design and development of an intelligent zone based master electronic control unit for power optimization in electric vehicles. [PDF]
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