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On-demand energy monitoring and response architecture in a ubiquitous world.

, , , and . Personal and Ubiquitous Computing, 21 (3): 537-551 (2017)

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On-demand energy monitoring and response architecture in a ubiquitous world., , , and . Personal and Ubiquitous Computing, 21 (3): 537-551 (2017)Artificial neural network modelling of the bioethanol-to-olefins process on a HZSM-5 catalyst treated with alkali., , , , , and . Appl. Soft Comput., (2017)MASSHA: An agent-based approach for human activity simulation in intelligent environments., , , , , and . Pervasive Mob. Comput., (2017)A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective., , , and . Sensors, 21 (20): 6910 (2021)Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks., , , , , and . GECCO (Companion), page 133-134. ACM, (2016)Agent Based Simulations for the Estimation of Sustainability Indicators., , , , and . ICCS, volume 51 of Procedia Computer Science, page 2943-2947. Elsevier, (2015)New methodology for facilitating the food wastage quantification. Identifying gaps and data inconsistencies., , , , , and . CoRR, (2019)An Optimisation Strategy for the Catalytic Transformation of Bioethanol into Olefins Using Computational Intelligence., , , , and . ITAT, volume 1422 of CEUR Workshop Proceedings, page 115-120. CEUR-WS.org, (2015)Bridging the Gap between Real and Simulated Environments: A Hybrid Agent-Based Smart Home Simulator Architecture for Complex Systems., , , , , , , , and . UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld, page 220-227. IEEE Computer Society, (2016)