Deep Learning Neural Network (DLNN), is a new branch of machine learning with the ability for complex feature representatio Although it was mainly suited for image feature (since it was inspired by object recognition method of mammalian visual system), if any type of feature can be translate into image, other type of data could be fit for using DLNN. In this paper, we prove that Mel Frequency Cepstrum Coefficient (MFCC) feature generates from audio signal of infant cry could be used as input feature for the Convolution Neural Network (CNN J Fundam Appl Sci. 2017, 9(3S), 768-778 769 The result shows CNN can be used to classify between normal and pathological (asphyxiated) cry with 94.3% accuracy in training set and 92.8% accuracy in testing set.
Stool specimens from 334 infants and young children hospitalized with diarrhea in the General Hospital, Kuala Lumpur, Malaysia between August and November, 1987 were analyzed for the presence of rotavirus double-stranded (ds) RNA by polyacrylamide gel electrophoresis. Of the 334 specimens analyzed, 32 (9.6%) were positive for rotavirus RNA. One specimen (designated G147) exhibited a ds RNA electropherotype profile characteristic of Group C rotavirus and was selected for further characterization. In Northern blot hybridization studies, the gene 5 segment of strain G147 hybridized with a cDNA probe generated from the cloned gene 5 (which encodes the VP6 inner capsid protein that is group specific) of porcine Group C rotavirus strain Cowden, confirming the classification of strain G147 in Group C. The association of Group C rotavirus with diarrheal illness in Malaysia is consistent with earlier studies that suggest a global distribution of this virus and supports the need for additional epidemiologic studies.
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