TY  - CHAP
AU  - Kalsum U. Hassan
AU  - Razib M. Othman
AU  - Rohayanti Hassan
AU  - Hishammuddin Asmuni
AU  - Jumail Taliba
AU  - Shahreen Kasim
ED  - Mahmoud ElHefnawi
ED  - Mohamed Mysara
Y1  - 2012-03-30
PY  - 2012
T1  - BRNN-SVM: Increasing the Strength of Domain Signal to Improve Protein Domain Prediction Accuracy
N2  - New applications in recurrent neural networks are covered by this book, which will be required reading in the field. Methodological tools covered include ranking indices for fuzzy numbers, a neuro-fuzzy digital filter and mapping graphs of parallel programmes. The scope of the techniques profiled in real-world applications is evident from chapters on the recognition of severe weather patterns, adult and foetal ECGs in healthcare and the prediction of temperature time-series signals. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, bioprocess identification and I-term control and the use of BRNN-SVM to improve protein-domain prediction accuracy. Recurrent neural networks can also be used in virtual reality and nonlinear dynamical systems, as shown by two chapters.
BT  - Recurrent Neural Networks and Soft Computing
SP  - Ch. 7
UR  - https://doi.org/10.5772/36188
DO  - 10.5772/36188
SN  - 
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-26
ER  -