An algorithm for characterizing pre-fuzzified linguistic nuance using artificial neural network

The attractiveness of artificial neural networks (ANNs) in solving many complex real world and computational demanding problems was used in characterizing linguistic nuance for harnessing malicious intent or decoding a communication trend. A set of adjectival watch lists was created a priori to serve as target convergence outputs to the ANN’s graphical user interface designed by the researchers. A set of pre-fuzzified or pre-processed speech conversation or written text was used as inputs to the neural network and represents a sub set of actual words used in the investigated two-way communication. The watch lists represents an editable set of words that represents malicious intent or key elements of conversation intent in bidirectional conversation or communication. The watch list database was generated a priori by identifying adjectives and specific nouns as used in the communication under investigation and then normalized. The pre-processed speech and text have been obtained from Recognizers utilizing the hidden Markov models and its hybrids for its processing. The algorithm showed robustness in sorting out pre-normalized and fuzzified speech that ordinarily contained certain elements of interest as conveyed by the investigated conversations. Analysis of a patient-to-healthcare provider’s bidirectional communication during malaria diagnosis and used for testing the developed algorithm showed significant accuracy when compared with the results of clinical analysis or consultation for the corresponding diagnosis.