Abstract-In order to develop a new hearing loss detection method, this paper proposed to combine wavelet entropy with feedforward neural network trained by genetic algorithm. The dataset contains 72 subjects-24 healthy controls, 24 left-sided hearing loss patients, and 24 right-sided hearing loss patients. The 10 runs of 8-fold cross validation showed that optimal decomposition level was 4, better than the results using decomposition level of 2, 3, and 5. Our method using 4-level decomposition yielded a sensitivity for healthy controls of 81.25±4.91%, a sensitivity for left-sided hearing loss of 80.42±5.57%, a sensitivity for right-sided hearing loss of 81.67±6.86%, and an overall accuracy of 81.11±1.34%.

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Teeth is a structure in which many vertebrates exist. For some animals, such as lions, tigers and so on, teeth are chewing tools and weapons to protect themselves. But for human, it also carries the beauty of the face. When the teeth are sick, accurate classification of the teeth seems particularly important. The main purpose of this paper is to classify the teeth accurately using biogeography-based optimization algorithm (BBO) and Multilayer perceptron (MLP). The results showed our method achieved 83.75± 2.95%, 83.50± 5.16%, 84.00± 5.16%, and 84.75± 3.43% accuracy for identifying incisor, canine, premolar, and molar.