ConaMSN: a contextaware messenger using dynamic Bayesian networks with wearable sensors,” Expert Systems with Applications - Hong, Yang, et al. - 2010 () Citation Context ...vity such as walking, running, resting, and sleeping by using pattern recognition techniques with the mobile built-in ...
Game-theoretic insights to the operation of autonomic and opportunistic networks Participatory and urban sensing in autonomic and opportunistic networks Trust, security, and reputation in context-aware systems Autonomic and opportunistic communication testbeds and prototypes, measurement data from real ...
Antoniou J, Pinto F, Simoes J, Pitsillides A (2010) Supporting context-aware multiparty sessions in heterogeneous mobile networks. Mobile Netw Appl 15:831–844 Article Google Scholar Bao L, Intille S (2004) Activity recognition from user-annotated acceleration data. In: Ferscha A, Mattern ...
Graph neural networks: foundations, frontiers, and applications. Singapore: Springer Nature Singapore; 2022. p. 483–497. https://doi.org/10.1007/978-981-16-6054-2_22. Carbonell M, Riba P, Villegas M, Fornes A, Llados J. Named entity recognition and relation extraction with graph neural ...
- Mobile Networks and Applications – Springer (SCI-E, IF=2.602) - Concurrency and Computation: Practice and Experience – Wiley (SCI-E, IF=1.167) – Special issue is opening soon! All accepted authors are eligible to submit an extended version in a fast track of: - EAI Endorsed ...
2017. Optimal hyperparameters for deep lstm-networks for sequence labeling tasks. arXiv preprint arXiv:1707.06799 (2017). Sang and De Meulder (2003) Erik F Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. arXiv ...
Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions 2024, Archives of Computational Methods in Engineering Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges 2023, ACM Computing Surveys Multimodal Emotion Recognition wit...
Deep learning models had been developed for action recognition in video. These models could fall into three categories. 2.2.1. Spatiotemporal Networks The convolutional design adequately uses the picture structure in diminishing the search space of the network by pooling and weight-sharing. Pooling ...
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emoLearnAdapt: A new approach for an emotion-based adaptation in e-learning environments 2024, Education and Information Technologies Contextual Recommendations: Dynamic Graph Attention Networks With Edge Adaptation 2024, IEEE Access Dropout prediction and decision feedback supported by multi temporal sequenc...