Previous work has shown that young children exhibit more difficulty understanding speech in the presence of speech-like distractors than do adults, and are more susceptible to at least some form of informational masking (IM). Yet little is known about how/when the “susceptibility” to linguistically-based IM develops. The authors tested adults, school-age children (aged 8 yrs), and preschool-age children (aged 4 yrs) on sentence recognition in the presence of normal speech, “jumbled” speech, and reversed speech distractors. As has been found previously with adults [e.g., Summers and Molis (2004). J. Speech, Lang. Hear. Res. 47, 245–256], children in both age groups showed a release of masking when the distractor was uninterpretable (reversed speech). This suggests that children already demonstrate linguistically-based IM by the age of 4 yrs.
Earthquakes are natural disasters which may result in heavy losses. Accurate prediction of the time and intensity of future earthquakes can lead to minimizing losses due to earthquakes. A number of earthquake predictions have been proposed based on mathematical and statistical models. In this paper, we present an earthquake prediction technique using Bat Algorithm (BA) and Feed Forward Neural Network (FFNN). The BA is used to train the weights of the FFNN to predict future earthquakes on the basis of past input data. Experimental results show that our proposed approach is highly comparable and more stable than Back Propagation Neural Network (BPNN) with respect to accuracy.
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