Now showing items 1-4 of 4

    • Demonstrating a linked data visualiser for finite element biosimulations 

      Mehdi, Muntazir; Khan, Yasar; Freitas, Andre; Jares, Joao; Raza, Saleem; Hasapis, Panagiotis; Sahay, Ratnesh (IEEE, 2016-02-04)
      Healthcare experts have recently turned towards the use of Biosimulation models to understand the multiple or different causative factors that cause impairment in human organs. The applications of biosimulations have been ...
    • A linked data visualiser for finite element biosimulations 

      Mehdi, Muntazir; Khan, Yasar; Freitas, Andre; Jares, Joao; Raza, Saleem; Hasapis, Panagiotis; Sahay, Ratnesh (IEEE, 2016-02-04)
      Biosimulation models are used to understand the multiple or different causative factors that cause impairment in human organs. Finite Element Method (FEM) provide a mathematical framework to simulate dynamic biological ...
    • Multimodal multimodel emotion analysis as linked data 

      Sánchez-Rada, J. Fernando; Iglesias, Carlos A.; Sagha, Hesam; Schuller, Björn; Wood, Ian; Buitelaar, Paul (IEEE, 2018-02-01)
      The lack of a standard emotion representation model hinders emotion analysis due to the incompatibility of annotation formats and models from different sources, tools and annotation services. This is also a limiting factor ...
    • Toward distributed, global, deep learning using IoT devices 

      Sudharsan, Bharath; Patel, Pankesh; Breslin, John; Ali, Muhammad Intizar; Mitra, Karan; Dustdar, Schahram; Rana, Omer; Jayaraman, Prem Prakash; Ranjan, Rajiv (Institute of Electrical and Electronics Engineers (IEEE), 2021-07-20)
      Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed ...