scholarly journals Speech Recognition using Deep Neural Network Neural (DNN) and Deep Belief Network (DBN)

2020 ◽  
Vol 8 (5) ◽  
pp. 1543-1548
Author(s):  
Prof. Kirti Rajadnya
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Deep Neural Network ◽  
Deep Belief Network ◽  
Belief Network

Deep neural network in QSAR studies using deep belief network

2018 ◽  
Vol 62 ◽  
pp. 251-258 ◽  
Author(s):  
Fahimeh Ghasemi ◽  
Alireza Mehridehnavi ◽  
Afshin Fassihi ◽  
Horacio Pérez-Sánchez
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Deep Belief Network ◽  
Belief Network ◽  
Qsar Studies

scholarly journals Deep Belief Network for Predicting the Predisposition to Lung Cancer in TP53 Gene

2020 ◽  
pp. 171-177 ◽  
Author(s):  
Zahraa Naser Shahweli
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Smoking Habit ◽  
Back Propagation ◽  
Tp53 Gene ◽  
Back Propagation Neural Network ◽  
Biological Data ◽  
Deep Belief Network ◽  
Restricted Boltzmann Machines ◽  
Belief Network

Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was trained by Back Propagation Neural Network (BPNN), where the data sets were divided into 6 folds, each is split into three partitions representing the training, validation, and testing. It is worthy to note that the proposed enhancer DBN predicted lung cancer in an acceptable manner, with an average F-measure value of  0. 96 and an average Matthews Correlation Coefficient (MCC) value of 0. 47 for 6 folds.

scholarly journals Speech Recognition of Oral English Teaching Based on Deep Belief Network

2020 ◽  
Vol 15 (10) ◽  
pp. 100
Author(s):  
Jianmei Wang
Keyword(s):  
Speech Recognition ◽  
Language Learning ◽  
Teaching And Learning ◽  
Teaching Method ◽  
Evaluation Model ◽  
Speech Rate ◽  
Pearson Correlation ◽  
Deep Belief Network ◽  
Belief Network ◽  
English Teaching

The oral English teaching faces several common problems: the teaching method is very inefficient, and the learners are poor in oral English. The development of computer-aided language learning offers a possible solution to these problems. Based on techniques of speech recognition, cloud computing and deep learning, this paper applies the deep belief network (DBN) to recognize the speeches in oral English teaching, and establishes a multi-parameter evaluation model for the pronunciation quality of oral English among college students. The model combines the merits of subjective and objective evaluations, and assesses the pronunciation from four aspects: pitch, speech rate, rhythm and intonation. Finally, the proposed model was verified through speech recognition and pronunciation evaluation experiments on 26 non-English majors from a college. The results show that the proposed evaluation model output credible results, which are consistent with those of experts, as evidenced by consistency, neighbourhood consistency and Pearson correlation coefficient. The research provides a feasible way to evaluate the oral English proficiency of learners, laying the basis for improving the teaching and learning efficiency of oral English.

Deep Learning-based Framework for Smart Sustainable Cities

2019 ◽  
Vol 15 (4) ◽  
pp. 76-107
Author(s):  
Nagarathna Ravi ◽  
Vimala Rani P ◽  
Rajesh Alias Harinarayan R ◽  
Mercy Shalinie S ◽  
Karthick Seshadri ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Deep Learning ◽  
Air Pollutants ◽  
Human Life ◽  
Deep Belief Network ◽  
Motor Vehicles ◽  
Human Beings ◽  
Long Term Effects ◽  
Belief Network

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

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