Cascade recurring deep networks for audible range prediction
نویسندگان
چکیده
BACKGROUND Hearing Aids amplify sounds at certain frequencies to help patients, who have hearing loss, to improve the quality of life. Variables affecting hearing improvement include the characteristics of the patients' hearing loss, the characteristics of the hearing aids, and the characteristics of the frequencies. Although the two former characteristics have been studied, there are only limited studies predicting hearing gain, after wearing Hearing Aids, with utilizing all three characteristics. Therefore, we propose a new machine learning algorithm that can present the degree of hearing improvement expected from the wearing of hearing aids. METHODS The proposed algorithm consists of cascade structure, recurrent structure and deep network structure. For cascade structure, it reflects correlations between frequency bands. For recurrent structure, output variables in one particular network of frequency bands are reused as input variables for other networks. Furthermore, it is of deep network structure with many hidden layers. We denote such networks as cascade recurring deep network where training consists of two phases; cascade phase and tuning phase. RESULTS When applied to medical records of 2,182 patients treated for hearing loss, the proposed algorithm reduced the error rate by 58% from the other neural networks. CONCLUSIONS The proposed algorithm is a novel algorithm that can be utilized for signal or sequential data. Clinically, the proposed algorithm can serve as a medical assistance tool that fulfill the patients' satisfaction.
منابع مشابه
Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
Achieved wireless networks since its beginning the prevalent wide due to the increasing wireless devices represented by smart phones and laptop, and the proliferation of networks coincides with the high speed and ease of use of the Internet and enjoy the delivery of various data such as video clips and games. Here's the show the congestion problem arises and represent aim of the research is t...
متن کاملAustralia's long-term electricity demand forecasting using deep neural networks
Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand underestimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep ne...
متن کاملNet-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Model reduction is a highly desirable process for deep neural networks. While large networks are theoretically capable of learning arbitrarily complex models, overfitting and model redundancy negatively affects the prediction accuracy and model variance. NetTrim is a layer-wise convex framework to prune (sparsify) deep neural networks. The method is applicable to neural networks operating with ...
متن کاملNet-Trim: A Layer-wise Convex Pruning of Deep Neural Networks
and quantum settings Model reduction is a highly desirable process for deep neural networks. While large networks are theoretically capable of learning arbitrarily complex models, overfitting and model redundancy negatively affects the prediction accuracy and model variance. Net-Trim is a layer-wise convex framework to prune (sparsify) deep neural networks. The method is applicable to neural ne...
متن کاملA Link Prediction Method Based on Learning Automata in Social Networks
Nowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probability of creation of a new relationship in future. A wide range of applications can be found for link prediction such as electro...
متن کامل