EEG-based emotion classification using deep belief networks

Hyperspectral data is not linearly separable, and it has a high characteristic dimension. This paper proposes a new algorithm that combines a deep belief network based on the Boltzmann machine with a self-organizing neural network. The primary features of the hyperspectral image are extracted with a deep belief network. The weights of the network are fine-tuned using the labeled sample. Feature vectors extracted by the deep belief network are classified by a self-organizing neural network. The method reduces the spectral dimension of the data while preserving the large amount of original information in the data. The method overcomes the long training time required when using self-organizing neural networks for clustering, as well as the training difficulties of Deep Belief Networks (DBN) when the labeled sample size is small, thereby improving the accuracy and robustness of the semi-supervised classification. Simulation results show that the structure of the network can achieve higher classification accuracy when the labeled sample is deficient.

Surface roughness is a crucial factor affecting the surface quality of workpieces in manufacturing industries. Thus, it is important to provide an accurate performance of surface roughness prediction and optimal parameters to reduce the burden of time and costs during the process. In this study, two predict models, namely multiple linear regression and deep belief network (DBN) models, were performed to accurately predict change in surface roughness in the rotational electromagnetic finishing (REMF). Compared to the statistical-based model, the data-driven model based on the DBN architecture was a significantly considerable effect on surface roughness prediction in the REMF process. Among the considered DBN models, DBN5 architecture as [7, 14, 14, 1] showed effective features of the nonlinear relationship between process parameters and response with the highest determination coefficient (R2) of 0.9340 and the lowest mean squared error (MSE) of 1.3037 \(\times\) 10−3 in the testing datasets. In addition, a genetic algorithm (GA) as a heuristic optimization technique was adopted to optimize the input parameters of the best derived DBN model. It showed that the maximum change in surface roughness was 0.530 at particle length of 3 mm, particle diameter of 0.7 mm, particle weight of 1.3 kg, liquid water quantity of 1.0 l, a rotational speed of 1323 rpm, working time of 35 min, and initial surface roughness of 2.5478 m\(\upmu\).

To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.

In this study, a hybrid deep belief network (DBN) cyber intrusion detection system was proposed to provide a secure network by controlling network traffic in Industrial control systems (ICS). The disadvantages of DBN have been analyzed and improved to create attack detectors in network traffic. The output is combined with Softmax Regression for effective intrusion detection and classification detection. Training and testing of the hybrid DBN model were carried out with the actual and original data set generated by ICS. DBNs are a much-preferred approach for detecting malicious attacks in network traffic. In instances where there is a lot of data, it is important to select the most appropriate structure for the DBN model. Therefore, in the model the hidden layers are updated by contrastive divergence (CD), and the output layer is combined with the Softmax classifier. The proposed model architecture has proved successful in many limitations, such as the complexity and size of training data. The proposed hybrid DBN model provided 99.72% accuracy in intrusion detection and classification. These results showed that the model achieved better performance than the existing intrusion detection system (IDS). It also provided approximately 5% more accuracy improvements with the hybrid model than with older DBN-based systems.

With the rapid development of artificial intelligence technology of deep learning method, it has been applied to many fields, especially to life science. In this paper, a novel approach for the task of voiceprint recognition was proposed. The combination of deep belief network (DBN) and support vector machine (SVM) was used to identify the voiceprint of 10 different individuals. Based on a 24-dimension Mel Frequency Cepstrum Coefficient (MFCC), the authors extracted 256-dimension deep voiceprint features via DBN model that is developed by stacking three layers of Restricted Boltzmann Machine (RBM), and conducted voiceprint recognition by approach of SVM. According to the recognition results, the new approach can significantly improve both of accuracy and efficiency when it was compared with traditional voiceprint features and recognition models. The low dimensional features of voiceprint was extracted into higher dimensions by DBN model, while SVM can avoid the elevation of computation complexity caused by the increases of feature dimension. The combined strengths have been fully expressed by the experimental results.

Emotion is closely related to healthy and abnormal mood is the alarm of our body. This paper is concentrated on the objective and accurate emotion classification using EEG signal. We propose emotional patches and combine it with the deep belief network(DBN) to achieve high-precision emotion classification. DBN is able to fit the distribution of the EEG signal and mapping the extracted feature to the higher-level characteristics space where we can easily perform high-precision classification. Compared with the other method, our method uses the emotional patches which have considered the temporal information of emotion and reduce the influence of noise. In addition, our model doesn't need to be trained twice to complete higher classification accuracy. We divide the EEG signal and choose the vital β frequency band where we perform feature extraction. Based on the SJTU Emotion EEG Dataset(SEED), we perform the emotion classification experiment and compare our method with the commonly used classifiers such as SVM, LR and CCA ect. The experimental result demonstrates that our method achieves the highest classification accuracy and outperform the state-of-theart emotion classification approaches based on EEG.

Image fusion plays a significant role in various computer vision applications. However, designing an efficient image fusion technique is still a challenging task. In this paper, a novel deep belief networks based image fusion framework is proposed to improve the performance of the image fusion process further. We have initially, evaluated the fusion dataset by applying various feature extraction techniques. Thereafter, features selection techniques are applied to select potential features. Finally, the image fusion machine learning model is built by using a deep belief network model. Extensive experiments reveal that the proposed technique outperforms existing image fusion techniques.

The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data

Research in emotion recognition seeks to develop insights into the temporal properties of emotion. However, automatic emotion recognition from spontaneous speech is challenging due to non-ideal recording conditions and highly ambiguous ground truth labels. Further, emotion recognition systems typically work with noisy high-dimensional data, rendering it difficult to find representative features and train an effective classifier. We tackle this problem by using Deep Belief Networks, which can model complex and non-linear high-level relationships between low-level features. We propose and evaluate a suite of hybrid classifiers based on Hidden Markov Models and Deep Belief Networks. We achieve state-of-the-art results on FAU Aibo, a benchmark dataset in emotion recognition [1]. Our work provides insights into important similarities and differences between speech and emotion.

Deep belief network based electricity load forecasting: An analysis of Macedonian case

Experimental and DBN-Based neural network extraction of radiation attenuation coefficient of dry mixture shotcrete produced using different additives

Supply chain flexibility is the processing ability of the enterprize to deal with the uncertain environment of supply and demand. In this paper, we consider the supply side (node interrupt) and demand side (demand fluctuations) under uncertain environment. By using the deep belief network (DBN), which is composed of multilayer Restricted Boltzmann Machine (RBM), it establishes the supply chain flexibility network with optimization of the transfer node and flow. The deep belief network is trained by the data of large manufacturing enterprize, compared with the traditional neural network (MLR, BP and GA). The results show that the deep belief network model overcomes the shortcomings of the traditional neural networks, such as easy to fall into local optimum, long training time and low function fitting degree, and it has higher prediction accuracy. This network model based on the deep belief network can promote the supply chain flexibility more, when supply and demand fluctuations occur.

This paper presents an approach to implement vibration signals for fault diagnosis of the rolling bearing. Due to the noise and transient impacts, it is difficulty to accurately diagnosis the faults with traditional methods. So a new type of learning architecture for deep generative model called deep belief networks (DBN) is applied. Since the unsupervised learning ability in DBN, it can extract the features from the raw data layer by layer. This article mainly research how to construct the encoder using DBN which can minimize the energy between the output and input vibration signals. Compared with existing diagnosis techniques, the proposed method can learn a good representation of features with higher accuracy. The results show that DBN can more comprehensively retain the data features in pattern recognition.

Although IoT sectors seem more popular and pervasively, they struggle with hazards. The botnet is one of the largest security dangers associated with IoT. It enables malicious software to administer and attack private network equipment collectively without the owners’ knowledge. Although many studies have used ML to detect botnets, these are either not very effective or only work with specific types of botnets or devices. As a result, the detection model for deep learning ideas is the focus of this research. It entails three key processes: (a) preprocessing, (b) feature extraction, and (c) classification. The input data are initially preprocessed using an improved data normalization approach. The preprocessed data are used to extract a number of features, including Tanimoto coefficient features, improved differential holoentropy-based features, Pearson r correlation-based features, and others. The detection process will be completed by an ensemble classification model that randomly shuffles models like the Deep Belief Network (DBN) model, Bidirectional Gated Recurrent Unit (Bi-GRU), and Long Short-Term Memory (LSTM). Bi-GRU, DBN, and LSTM will be averaged to provide the ensemble results. Bi-GRU is trained using the Self Improved Blue Monkey Optimization (SIBMO) Algorithm by selecting the optimal weights, which increases the detection accuracy. The overall performance of the suggested work is then evaluated in relation to other existing models using various methodologies. In comparison to existing methods, the created ensemble classifier [Formula: see text] SIBMO scheme obtains the highest accuracy (93%) at a learning percentage of 90%.

EMG Signals based Human Action Recognition via Deep Belief Networks