Application of soft computing techniques to adaptive user buffer overflow control on the Internet

Artificial neural network for herbal ingredient discoveries

“…Our literature review of soft computing techniques [3,4,5,6,7] indicates that the artificial neural network (ANN) based on backpropagation is suitable for fast, trusted herbal ingredient discoveries. The reasons are: i) reusability -the same ANN construct can be trained to become named ANN modules that assume different roles; ii) simplicity -it is easy to program and less error-prone than the traditional algorithmic programming approach; iii) data-orientation -the logical points inside an ANN construct will converge to the required logical operation with respect to the given training dataset; iv) versatility -an ANN construct can be combined with its clones or other constructs to form larger, more complex ANN configurations; v) adaptability -the neuron's activation function can be replaced any time, and the input parameters to a neuron can be weighted and normalized according to the needs; vi) optimizationan ANN can be effectively optimized or pruned for a particular operation [5]; vii) commodity -many ANN constructs in the form of freeware are available in the public domain with rich user experience, viii) accuracy -as long as the number of the hidden neurons is twice that of the input neurons the ANN output is accurate [8,9]; and ix) parallelism -many named ANN constructs can be invoked to work in parallel for speedup.…”

An Ontology Supported Meta-interface for the Development and Installation of Customized Web Based Telemedicine Systems

“…Channel reliability methods: These shorten the service roundtrip time in client/server interaction. Usually dynamic or adaptive methods are more effective than static methods [8]. C. User participation: This is a necessity for effective fast prototyping so that immediate user feedback improves the prototype.…”

Adaptive fuzzy sliding mode active queue management algorithms

“…Thus, AFSM with scalable implementation can be deployed at the router. The computation load of the fuzzy decision with the least fuzzy rules is also much smaller compared with some peer fuzzy AQMs [24,34].…”