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Multi-sensor Fusion Based on Asymmetric Decision Weighting for Robust Activity Recognition.

, , , , , , and . Neural Process. Lett., 42 (1): 5-26 (2015)

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Towards an integrated swarm intelligence framework for urban mobility:A systematic review and proposed theoretical model., , , , and . J. King Saud Univ. Comput. Inf. Sci., 35 (10): 101836 (December 2023)High-Level Context Inference for Human Behavior Identification., , , , , , , and . IWAAL, volume 9455 of Lecture Notes in Computer Science, page 164-175. Springer, (2015)An Ontology for Dynamic Sensor Selection in Wearable Activity Recognition., , , and . IWBBIO (2), volume 9044 of Lecture Notes in Computer Science, page 141-152. Springer, (2015)mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications., , , , , , , and . IWAAL, volume 8868 of Lecture Notes in Computer Science, page 91-98. Springer, (2014)Deep Learning Approaches Applied to MRI and PET Image Classification of Kidney Tumours: A Systematic Review., , , , , and . IWANN (2), volume 14135 of Lecture Notes in Computer Science, page 254-265. Springer, (2023)Ontological modeling of motivational messages for physical activity coaching., , , , , , , and . PervasiveHealth, page 355-364. ACM, (2017)Opportunistic Activity Recognition in IoT Sensor Ecosystems via Multimodal Transfer Learning., , , , , , and . Neural Process. Lett., 53 (5): 3169-3197 (2021)Dynamic Formation of Business Networks: A Framework for 'Quality of Information'-Based Discovery of Resources., , and . Int. J. Appl. Logist., 2 (4): 44-60 (2011)MIMU-Wear: Ontology-based sensor selection for real-world wearable activity recognition., , , and . Neurocomputing, (2017)A Deep Transfer Learning Approach to Support Opportunistic Wearable Activity Recognition., , , , and . IWANN (1), volume 14134 of Lecture Notes in Computer Science, page 473-482. Springer, (2023)