A novel speech emotion recognition model using mean update of particle swarm and whale optimization-based deep belief network

2020 ◽  
Vol 54 (3) ◽  
pp. 297-322
Author(s):  
Rajasekhar B ◽  
Kamaraju M ◽  
Sumalatha V
Keyword(s):  
Human Computer Interaction ◽  
Emotion Recognition ◽  
Particle Swarm ◽  
Deep Belief Network ◽  
Speech Emotion Recognition ◽  
Content Type ◽  
Belief Network ◽  
Whale Optimization ◽  
The Mean ◽  
Gender And Emotion

PurposeNowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.Design/methodology/approachThis paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.FindingsThrough the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.Originality/valueThis paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

scholarly journals A Prediction Model Based on Deep Belief Network and Least Squares SVR Applied to Cross-Section Water Quality

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1929
Author(s):  
Jianzhuo Yan ◽  
Ya Gao ◽  
Yongchuan Yu ◽  
Hongxia Xu ◽  
Zongbao Xu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Least Squares ◽  
Quality Parameters ◽  
Deep Belief Network ◽  
Water Quality Parameters ◽  
Series Data ◽  
Belief Network ◽  
The Mean

Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it is possible to improve the accuracy of prediction. This paper proposes a method for predicting water quality based on the deep belief network (DBN) model. First, the particle swarm optimization (PSO) algorithm is used to optimize the network parameters of the deep belief network, which is to extract feature vectors of water quality time series data at multiple scales. Then, combined with the least squares support vector regression (LSSVR) machine which is taken as the top prediction layer of the model, a new water quality prediction model referred to as PSO-DBN-LSSVR is put forward. The developed model is valued in terms of the mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results illustrate that the model proposed in this paper can accurately predict water quality parameters and better robustness of water quality parameters compared with the traditional back propagation (BP) neural network, LSSVR, the DBN neural network, and the DBN-LSSVR combined model.

Optimal weighted feature vector and deep belief network for medical data classification

2019 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Vinod Jagannath Kadam ◽  
Shivajirao Manikrao Jadhav
Keyword(s):  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Deep Belief Network ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Belief Network ◽  
Medical Diagnoses ◽  
Whale Optimization ◽  
Medical Data Classification

Medical data classification is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures are usually taken from a variety of sources within the healthcare record, such as the transcription of the physician’s notes, laboratory results, radiologic results and other sources. However, there exist many frequency distribution problems in these domains. Hence, this paper intends to develop an advanced and precise medical data classification approach for diabetes and breast cancer dataset. With the knowledge of the features and challenges persisting with the state-of-the-art classification methods, deep learning-based medical data classification methodology is proposed here. It is well known that deep learning networks learn directly from the data. In this paper, the medical data is dimensionally reduced using Principle Component Analysis (PCA). The dimensionally reduced data are transformed by multiplying by a weighting factor, which is optimized using Whale Optimization Algorithm (WOA), to obtain the maximum distance between the features. As a result, the data are transformed into a label-distinguishable plane under which the Deep Belief Network (DBN) is adopted to perform the deep learning process, and the data classification is performed. Further, the proposed WOA-based DBN (WOADBN) method is compared with the Neural Network (NN), DBN, Generic Algorithm-based NN (GANN), GADBN, Particle Swarm Optimization (PSONN), PSO-based DBN (PSODBN), WOA-based NN (WOANN) techniques and the results are obtained, which shows the superiority of proposed algorithm over conventional methods.

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