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1
Peng, Fan, Suping Peng, Wenfeng Du, and Hongshuan Liu. "Coalbed methane content prediction using deep belief network." Interpretation 8, no. 2 (May 1, 2020): T309—T321. http://dx.doi.org/10.1190/int-2019-0126.1.
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Accurate measurement of coalbed methane (CBM) content is the foundation for CBM resource exploration and development. Machine-learning techniques can help address CBM content prediction tasks. Due to the small amount of actual measurement data and the shallow model structure, however, the results from traditional machine-learning models have errors to some extent. We have developed a deep belief network (DBN)-based model with the input as continuous real values and the activation function as the rectified linear unit. We first calculated a variety of seismic attributes of the target coal seam to highlight the features of the coal seam, then we preprocessed the original attribute features, and finally developed the performance of the DBN model using the preprocessed features. We used 23,374 training data to train our model, 23,240 for pretraining, and 134 for fine-tuning. For the purpose of demonstrating the advantages of the DBN model, we compared it with two typical machine-learning models, including the multilayer perceptron model and the support vector regression model. These two models were trained based on the same labeled training data. The results, obtained from different models, indicated that the DBN model has the least error, which means that it is more accurate than the other two models when used to predict CBM content.
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Zhang, Kaiyu, Shanshan Shi, Shu Liu, Junjie Wan, and Lijia Ren. "Research on DBN-based Evaluation of Distribution Network Reliability." E3S Web of Conferences 242 (2021): 03004. http://dx.doi.org/10.1051/e3sconf/202124203004.
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In order to accurately and efficiently analyze the reliability of distribution network, this paper proposes a method of analyzing the reliability of distribution network based on a deep belief network. The Deep Belief Network (DBN) is composed of limiting Boltzmann machine layer-by-layer stacking. It has a strong advantage of automatic feature extraction, which overcomes the shortcomings of traditional neural networks in extracting data features. The entire training process of DBN can be roughly divided into two stages: pre-training and fine-tuning.First of all, the pre-training of the DBN model is realized by training the Restricted Boltzmann Machine (RBM) layer by layer, then the BP algorithm is used for reverse fine-tuning to complete the training process of the entire network. finally, the reliability analysis of distribution network is performed by the trained DBN. Compared with the BP neural network method and the traditional Monte Carlo simulation method, it is verified that the proposed model of distribution network reliability analysis has high accuracy.
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Yang, Lei, Chunqing Zhao, Chao Lu, Lianzhen Wei, and Jianwei Gong. "Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network." Sensors 21, no. 24 (December 20, 2021): 8498. http://dx.doi.org/10.3390/s21248498.
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Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.
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Sun, Lili. "Optimization of Physical Education Course Resource Allocation Model Based on Deep Belief Network." Mathematical Problems in Engineering 2023 (April 29, 2023): 1–8. http://dx.doi.org/10.1155/2023/8457760.
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In order to meet the optimization needs of physical education curriculum resource allocation, the author proposes a deep belief-based physical education curriculum resource allocation technology. The efficient feature abstraction and feature extraction capabilities of deep belief technology fully explore the interests and preferences of learners on course resources. Because deep belief has strong capabilities in feature detection and feature extraction, it has unique and efficient feature abstraction capabilities for different dimensional attributes of input data; the author proposes a DBN-MCPR model optimization method based on deep belief classification in the MOOC environment. Experimental results show that when the number of iterations reaches about 80, the RMSE of DBN-MCPR trained with the training dataset without learner feature vector is 77.94%, while the RMSE of DBN-MCPR trained with the dataset with learner feature vector is 77.01; DBN-MCPR with full eigenvectors tends to converge after about 40 iterations, while DBN-MCPR without learner eigenvectors starts to converge after about 15 iterations; this result is in line with the characteristics of the internal network structure of DBN. Conclusion. This application proves that the technical research based on deep belief can effectively meet the needs of the optimization of physical education curriculum resource allocation.
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Prabowo, Abram Setyo, Agus Sihabuddin, and Azhari SN. "Adaptive Moment Estimation On Deep Belief Network For Rupiah Currency Forecasting." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 1 (January 31, 2019): 31. http://dx.doi.org/10.22146/ijccs.39071.
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One approach that is often used in forecasting is artificial neural networks (ANN), but ANNs have problems in determining the initial weight value between connections, a long time to reach convergent, and minimum local problems.Deep Belief Network (DBN) model is proposed to improve ANN's ability to forecast exchange rates. DBN is composed of a Restricted Boltzmann Machine (RBM) stack. The DBN structure is optimally determined through experiments. The Adam method is applied to accelerate learning in DBN because it is able to achieve good results quickly compared to other stochastic optimization methods such as Stochastic Gradient Descent (SGD) by maintaining the level of learning for each parameter.Tests are carried out on USD / IDR daily exchange rate data and four evaluation criteria are adopted to evaluate the performance of the proposed method. The DBN-Adam model produces RMSE 59.0635004, MAE 46.406739, MAPE 0.34652. DBN-Adam is also able to reach the point of convergence quickly, where this result is able to outperform the DBN-SGD model.
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Tan, Xiaopeng, Shaojing Su, Zhen Zuo, Xiaojun Guo, and Xiaoyong Sun. "Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO." Sensors 19, no. 24 (December 14, 2019): 5529. http://dx.doi.org/10.3390/s19245529.
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With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.
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Yan, Yan, Xu-Cheng Yin, Sujian Li, Mingyuan Yang, and Hong-Wei Hao. "Learning Document Semantic Representation with Hybrid Deep Belief Network." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/650527.
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High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance.
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Yang, Huihua, Baichao Hu, Xipeng Pan, Shengke Yan, Yanchun Feng, Xuebo Zhang, Lihui Yin, and Changqin Hu. "Deep belief network-based drug identification using near infrared spectroscopy." Journal of Innovative Optical Health Sciences 10, no. 02 (March 2017): 1630011. http://dx.doi.org/10.1142/s1793545816300111.
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Near infrared spectroscopy (NIRS) analysis technology, combined with chemometrics, can be effectively used in quick and nondestructive analysis of quality and category. In this paper, an effective drug identification method by using deep belief network (DBN) with dropout mechanism (dropout-DBN) to model NIRS is introduced, in which dropout is employed to overcome the overfitting problem coming from the small sample. This paper tests proposed method under datasets of different sizes with the example of near infrared diffuse reflectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs, aluminum and nonaluminum packaged. Meanwhile, it gives experiments to compare the proposed method’s performance with back propagation (BP) neural network, support vector machines (SVMs) and sparse denoising auto-encoder (SDAE). The results show that for both binary classification and multi-classification, dropout mechanism can improve the classification accuracy, and dropout-DBN can achieve best classification accuracy in almost all cases. SDAE is similar to dropout-DBN in the aspects of classification accuracy and algorithm stability, which are higher than that of BP neural network and SVM methods. In terms of training time, dropout-DBN model is superior to SDAE model, but inferior to BP neural network and SVM methods. Therefore, dropout-DBN can be used as a modeling tool with effective binary and multi-class classification performance on a spectrum sample set of small size.
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Sharipuddin, Sharipuddin, Eko Arip Winanto, Zulwaqar Zain Mohtar, Kurniabudi Kurniabudi, Ibnu Sani Wijaya, and Dodi Sandra. "Improvement detection system on complex network using hybrid deep belief network and selection features." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (July 1, 2023): 470. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp470-479.
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The challenge for intrusion detection system on internet of things networks (IDS-IoT) as a complex networks is the constant evolution of both large and small attack techniques and methods. The IoT network is growing very rapidly, resulting in very large and complex data. Complex data produces large data dimensions and is one of the problems of IDS in IoT networks. In this work, we propose a dimensional reduction method to improve the performance of IDS and find out the effect of the method on IDS-IoT using deep belief network (DBN). The proposed method for feature selection uses information gain (IG) and principle component analysis (PCA). The experiment of IDS-IoT with DBN successfully detects attacks on complex networks. The calculation of accuracy, precision, and recall, shows that the performance of the combination DBN with PCA is superior to DBN with information gain for Wi-Fi datasets. Meanwhile, the Xbee dataset with information gain is superior to using PCA. The final result of measuring the average value of accuracy, precision, and recall from each IDSDBN test for IoT is 99%. Other results also show that the proposed method has better performance than previous studies increasing by 4.12%.
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Anh, Duong Tuan, and Ta Ngoc Huy Nam. "Chaotic time series prediction with deep belief networks: an empirical evaluation." Science & Technology Development Journal - Engineering and Technology 3, SI1 (December 4, 2020): SI102—SI112. http://dx.doi.org/10.32508/stdjet.v3isi1.571.
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Chaotic time series are widespread in several real world areas such as finance, environment, meteorology, traffic flow, weather. A chaotic time series is considered as generated from the deterministic dynamics of a nonlinear system. The chaotic system is sensitive to initial conditions; points that are arbitrarily close initially become exponentially further apart with progressing time. Therefore, it is challenging to make accurate prediction in chaotic time series. The prediction using conventional statistical techniques, k-nearest-nearest neighbors algorithm, Multi-Layer-Perceptron (MPL) neural networks, Recurrent Neural Networks, Radial-Basis-Function (RBF) Networks and Support Vector Machines, do not give reliable prediction results for chaotic time series. In this paper, we investigate the use of a deep learning method, Deep Belief Network (DBN), combined with chaos theory to forecast chaotic time series. DBN should be used to forecast chaotic time series. First, the chaotic time series are analyzed by calculating the largest Lyapunov exponent, reconstructing the time series by phase-space reconstruction and determining the best embedding dimension and the best delay time. When the forecasting model is constructed, the deep belief network is used to feature learning and the neural network is used for prediction. We also compare the DBN –based method to RBF network-based method, which is the state-of-the-art method for forecasting chaotic time series. The predictive performance of the two models is examined using mean absolute error (MAE), mean squared error (MSE) and mean absolute percentage error (MAPE). Experimental results on several synthetic and real world chaotic datasets revealed that the DBN model is applicable to the prediction of chaotic time series since it achieves better performance than RBF network.
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Rajadnya, Prof Kirti. "Speech Recognition using Deep Neural Network Neural (DNN) and Deep Belief Network (DBN)." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 1543–48. http://dx.doi.org/10.22214/ijraset.2020.5359.
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Tong, Guiying. "Music Emotion Classification Method Using Improved Deep Belief Network." Mobile Information Systems 2022 (March 18, 2022): 1–7. http://dx.doi.org/10.1155/2022/2715765.
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Aiming at the problems of difficult data feature selection and low classification accuracy in music emotion classification, this study proposes a music emotion classification algorithm based on deep belief network (DBN). The traditional DBN network is improved by adding fine-tuning nodes to enhance the adjustability of the model. Then, two music data features, pitch frequency and band energy distribution, are fused as the input of the model. Finally, the support vector machine (SVM) classification algorithm is used as a classifier to realize music emotion classification. The fusion algorithm is tested on real datasets. The results show that the fused feature data of pitch frequency and band energy distribution can effectively represent music emotion. The accuracy of the improved DBN network fused with the SVM classification algorithm for music emotion classification can reach 88.31%, which shows good classification accuracy compared with the existing classification methods.
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Alom, Zahangir, Venkata Ramesh Bontupalli, and Tarek M. Taha. "Intrusion Detection Using Deep Belief Network and Extreme Learning Machine." International Journal of Monitoring and Surveillance Technologies Research 3, no. 2 (April 2015): 35–56. http://dx.doi.org/10.4018/ijmstr.2015040103.
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Security threats for computer networks have increased dramatically over the last decade, becoming bolder and more brazen. There is a strong need for effective Intrusion Detection Systems (IDS) that are designed to interpret intrusion attempts in incoming network traffic intelligently. In this paper, the authors explored the capabilities of Deep Belief Networks (DBN) – one of the most influential deep learning approach – in performing intrusion detection after training with the NSL-KDD dataset. Additionally, they examined the impact of using Extreme Learning Machine (ELM) and Regularized ELM on the same dataset to evaluate the performance against DBN and Support Vector Machine (SVM) approaches. The trained system identifies any type of unknown attack in the dataset examined. In addition to detecting attacks, the proposed system also classifies them into five groups. The implementation with DBN and SVM give a testing accuracy of about 97.5% and 88.33% respectively with 40% of training data selected from the NSL-KDD dataset. On the other hand, the experimental results show around 98.20% and 98.26% testing accuracy respectively for ELM and RELM after reducing the data dimensions from 41 to 9 essential features with 40% training data. ELM and RELM perform better in terms of testing accuracy upon comparison with DBN and SVM.
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Zulfa, Ira, and Edi Winarko. "Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 11, no. 2 (July 31, 2017): 187. http://dx.doi.org/10.22146/ijccs.24716.
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Sentiment analysis is a computational research of opinion sentiment and emotion which is expressed in textual mode. Twitter becomes the most popular communication device among internet users. Deep Learning is a new area of machine learning research. It aims to move machine learning closer to its main goal, artificial intelligence. The purpose of deep learning is to change the manual of engineering with learning. At its growth, deep learning has algorithms arrangement that focus on non-linear data representation. One of the machine learning methods is Deep Belief Network (DBN). Deep Belief Network (DBN), which is included in Deep Learning method, is a stack of several algorithms with some extraction features that optimally utilize all resources. This study has two points. First, it aims to classify positive, negative, and neutral sentiments towards the test data. Second, it determines the classification model accuracy by using Deep Belief Network method so it would be able to be applied into the tweet classification, to highlight the sentiment class of training data tweet in Bahasa Indonesia. Based on the experimental result, it can be concluded that the best method in managing tweet data is the DBN method with an accuracy of 93.31%, compared with Naive Bayes method which has an accuracy of 79.10%, and SVM (Support Vector Machine) method with an accuracy of 92.18%.
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Li, Chenming, Yongchang Wang, Xiaoke Zhang, Hongmin Gao, Yao Yang, and Jiawei Wang. "Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data." Sensors 19, no. 1 (January 8, 2019): 204. http://dx.doi.org/10.3390/s19010204.
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With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.
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Arum, Aprilisa, and Pramono Pramono. "Penerapan Algoritma Deep Belief Networks (DBNs) Untuk Prediksi Kanker Serviks." DutaCom 17, no. 1 (February 28, 2023): 50–57. http://dx.doi.org/10.47701/dutacom.v17i1.3790.
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Kanker serviks adalah kanker yang mematikan dan paling umum yang menyerang wanita di seluruh dunia. Prediksi yang tepat mengenai kanker serviks berperan penting dalam upaya pencegahan dan pengobatan yang efektif. Dalam konteks ini, penerapan algoritma Deep Believe Network (DBNs) telah menarik perhatian sebagai metode potensial untuk prediksi kanker serviks. Tujuan penelitian ini untuk dapat mengevaluasi kemampuan algoritma Deep Believe Network (DBNs) dalam memprediksi kemungkinan kanker serviks berdasarkan faktor risiko terkait. Dengan memanfaatkan data klinis yang tersedia, langkah-langkah analitis seperti prapemrosesan data, pelatihan model DBN, dan evaluasi kinerja model dapat dilakukan. Berdasarkan hal tersebut menggunakan algoritma Deep Belief Networks (DBNs) yaitu terbentuk 250 epoch, dengan nilai loss 0,0112 dan mendapatkan nilai akurasi sebesar 0,9940, recall 0,99 dan f1-score 0,99. Dari hasil pemodelan algoritma dan evalusi maka algoritma Deep Belief Networks (DBNs) sangat baik digunakan untuk memprediksi penyakit kanker serviks.
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Constantin Menteng, Arief Setyanto, and Hanif Al Fatta. "MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK." Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika 7, no. 2 (August 5, 2023): 101–10. http://dx.doi.org/10.47111/jti.v7i2.8151.
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Deep Belief Networks are deep learning models that utilize stacks of Restricted Boltzmann Machines (RBM) or sometimes Autoencoders. Autoencoder is a neural network model that has the same input and output. The autoencoder learns the input data and attempts to reconstruct the input data. The solution in this study can provide several tests on DBN such as detecting recall accuracy and better classification precision. By using this algorithm, it is hoped that we as users can overcome problems that occur quite often such as brute force attacks in our accounts and within the company. And the results obtained from this DBN experiment are with an accuracy value of 90.27%, recall 90.27%, precession 91.67%, F1-score 90.51%. The results of this study are the data values of accuracy, recall, precession, and f1-score data used to detect brute force attacks are quite efficient using the deep model of the deep belief network.
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Jiang, Keruo, Zhen Huang, Xinyan Zhou, Chudong Tong, Minjie Zhu, and Heshan Wang. "Deep belief improved bidirectional LSTM for multivariate time series forecasting." Mathematical Biosciences and Engineering 20, no. 9 (2023): 16596–627. http://dx.doi.org/10.3934/mbe.2023739.
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<abstract> <p>Multivariate time series (MTS) play essential roles in daily life because most real-world time series datasets are multivariate and rich in time-dependent information. Traditional forecasting methods for MTS are time-consuming and filled with complicated limitations. One efficient method being explored within the dynamical systems is the extended short-term memory networks (LSTMs). However, existing MTS models only partially use the hidden spatial relationship as effectively as LSTMs. Shallow LSTMs are inadequate in extracting features from high-dimensional MTS; however, the multilayer bidirectional LSTM (BiLSTM) can learn more MTS features in both directions. This study tries to generate a novel and improved BiLSTM network (DBI-BiLSTM) based on a deep belief network (DBN), bidirectional propagation technique, and a chained structure. The deep structures are constructed by a DBN layer and multiple stacked BiLSTM layers, which increase the feature representation of DBI-BiLSTM and allow for the model to further learn the extended features in two directions. First, the input is processed by DBN to obtain comprehensive features. Then, the known features, divided into clusters based on a global sensitivity analysis method, are used as the inputs of every BiLSTM layer. Meanwhile, the previous outputs of the shallow layer are combined with the clustered features to reconstitute new input signals for the next deep layer. Four experimental real-world time series datasets illustrate our one-step-ahead prediction performance. The simulating results confirm that the DBI-BiLSTM not only outperforms the traditional shallow artificial neural networks (ANNs), deep LSTMs, and some recently improved LSTMs, but also learns more features of the MTS data. As compared with conventional LSTM, the percentage improvement of DBI-BiLSTM on the four MTS datasets is 85.41, 75.47, 61.66 and 30.72%, respectively.</p> </abstract>
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Ruiz Cárdenas, Luis Carlos, Dario Amaya Hurtado, and Robinson Jiménez Moreno. "Predicción de radiación solar mediante deep belief network." Revista Tecnura 20, no. 47 (February 18, 2016): 39. http://dx.doi.org/10.14483/udistrital.jour.tecnura.2016.1.a03.
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<p>El desarrollo continuo de las herramientas computacionales ofrece la posibilidad de realizar procesos con la capacidad de <em>llevar a cabo</em> actividades con mayor eficiencia, exactitud y precisión. Entre estas herramientas se encuentra la arquitectura neuronal, Deep Belief Network (DBN), diseñada con el propósito de colaborar en el desarrollo de técnicas de predicción para hallar información que permita estudiar el comportamiento de los fenómenos naturales, como lo es la radiación solar. En el presente trabajo se presentan los resultados obtenidos al manejar la arquitectura DBN para predicción de radiación solar, la cual se simula mediante la herramienta de programación Visual Studio C#, indicando el nivel de profundidad que posee esta arquitectura, como afecta la cantidad de capas y de neuronas en el entrenamiento y los resultados obtenidos para poder predecir los valores deseados en el 2014, con errores cercanos al 2 % y mayor rapidez para el entrenamiento, respecto a errores obtenidos por métodos convencionales de entrenamiento neuronal, que se encuentran por el 5% y que a su vez llevan largos periodos de entrenamiento.</p>
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Zhong, P., Z. Q. Gong, and C. Schönlieb. "A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 443–49. http://dx.doi.org/10.5194/isprs-archives-xli-b7-443-2016.
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In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work turns to investigate the deep belief networks (DBNs), which allow unsupervised training. The DBN trained over limited training samples usually has many “dead” (never responding) or “potential over-tolerant” (always responding) latent factors (neurons), which decrease the DBN’s description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.
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Zhong, P., Z. Q. Gong, and C. Schönlieb. "A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 443–49. http://dx.doi.org/10.5194/isprsarchives-xli-b7-443-2016.
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In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work turns to investigate the deep belief networks (DBNs), which allow unsupervised training. The DBN trained over limited training samples usually has many “dead” (never responding) or “potential over-tolerant” (always responding) latent factors (neurons), which decrease the DBN’s description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.
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Wang, Shuqin, Gang Hua, Guosheng Hao, and Chunli Xie. "A Cycle Deep Belief Network Model for Multivariate Time Series Classification." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9549323.
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Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.
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Wang, Hai, Yingfeng Cai, and Long Chen. "A Vehicle Detection Algorithm Based on Deep Belief Network." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/647380.
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Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN) is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.
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Gourisaria, Mahendra Kumar, Harshvardhan GM, Rakshit Agrawal, Sudhansu Shekhar Patra, Siddharth Swarup Rautaray, and Manjusha Pandey. "Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals." International Journal of E-Health and Medical Communications 12, no. 6 (November 2021): 1–24. http://dx.doi.org/10.4018/ijehmc.20211101.oa9.
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Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.
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Zhang, Yue, and Fangai Liu. "An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer." Future Internet 12, no. 11 (October 29, 2020): 188. http://dx.doi.org/10.3390/fi12110188.
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A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (IDBN): first, the basic DBN structure is pre-trained and the learned weight parameters are fixed; secondly, the learned weight parameters are transferred to the new neuron and hidden layer through the method of knowledge transfer, thereby constructing the optimal network width and depth of DBN; finally, the top-down layer-by-layer partial least squares regression method is used to fine-tune the weight parameters obtained by the pre-training, which avoids the traditional fine-tuning problem based on the back-propagation algorithm. In order to verify the prediction performance of the model, this paper conducts benchmark experiments on the Movielens-20M (ML-20M) and Last.fm-1k (LFM-1k) public data sets. Compared with other traditional algorithms, IDBN is better than other fixed models in terms of prediction performance and training time. The proposed IDBN model has higher prediction accuracy and convergence speed.
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Xi, Shuyue, and Xiaozhong Xu. "Dynamic gaussian deep belief network design and stock market application." Intelligent Data Analysis 27, no. 2 (March 15, 2023): 519–34. http://dx.doi.org/10.3233/ida-216340.
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Stock price forecasting has been an important topic for investors, researchers, and analysts. In this paper, a prediction model of Dynamic Gaussian Deep Belief Network (DGDBN) is proposed. Generally, the network structure of traditional Deep Belief Network (DBN) determines the performance of its time series prediction. Most previous research uses artificial experience to adjust the network structure, it is difficult to ensure performance and time efficiency by constantly trying. In addition, the accuracy of the traditional DBN stacked by binary Restricted Boltzmann Machines(RBM) needs to be improved when solving the time series problem. The DGDBN designed in this paper contains two points: The first point is to add Gaussian noise to the RBM. The second point is to realize the increase or decrease branch algorithm of hidden layer structure according to the connection weights and average percentage error (MAPE). Finally, the forecast for the stocks of United Technologies Corporation and Unisys Corp, DGDBN is compared with DBN and LSTM. The root means square error (RMSE) increases by 15% and 65%. The interesting thing we found is that the number of neurons in the last layer of the DGDBN network has a greater effect than other layers.
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Yang, Jianjian, Boshen Chang, Xiaolin Wang, Qiang Zhang, Chao Wang, Fan Wang, and Miao Wu. "Design and Application of Deep Belief Network Based on Stochastic Adaptive Particle Swarm Optimization." Mathematical Problems in Engineering 2020 (August 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/6590765.
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Due to the problem of poor recognition of data with deep fault attribute in the case of traditional superficial network under semisupervised and weak labeling, a deep belief network (DBN) was proposed for deep fault detection. Due to the problems of deep belief network (DBN) network structure and training parameter selection, a stochastic adaptive particle swarm optimization (RSAPSO) algorithm was proposed in this study to optimize the DBN. A stochastic criterion was proposed in this method to make the particles jump out of the original position search with a certain probability and reduce the probability of falling into the local optimum. The RSAPSO-DBN method used sample data to train the DBN and used the final diagnostic error rate to construct the fitness value function of the particle swarm algorithm. By comparing the minimum fitness value of each particle to determine the advantages and disadvantages of the model, the corresponding minimum fitness value was selected. Using the number of network nodes, learning rate, and momentum parameters, the optimal DBN classifier was generated for fault diagnosis. Finally, the validity of the method was verified by bearing data from Case Western Reserve University in the United States and data collected in the laboratory. Comparing BP (BP neural network), support vector machine, and heterogeneous particle swarm optimization DBN methods, the proposed method demonstrated the highest recognition rates of 87.75% and 93.75%. This proves that the proposed method possesses universality in fault diagnosis and provides new ideas for data identification with different fault depth attributes.
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Shen, Changqing, Jiaqi Xie, Dong Wang, Xingxing Jiang, Juanjuan Shi, and Zhongkui Zhu. "Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis." Applied Sciences 9, no. 16 (August 16, 2019): 3374. http://dx.doi.org/10.3390/app9163374.
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Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN.
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Srinivasa Rao, T. C., S. S. Tulasi Ram, and J. B. V. Subrahmanyam. "Fault Signal Recognition in Power Distribution System using Deep Belief Network." Journal of Intelligent Systems 29, no. 1 (March 13, 2018): 459–74. http://dx.doi.org/10.1515/jisys-2017-0499.
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Abstract Nowadays, electrical power system is considered as one of the most complicated artificial systems all over the globe, as social and economic development depends on intact, consistent, stable and economic functions. Owing to diverse random causes, accidental failures occur in electrical power systems. Considering this issue, this article aimed to propose the use of deep belief network (DBN) in detecting and classifying fault signals such as transient, sag and swell in the transmission line. Here, wavelet-decomposed fault signals are extracted and the fault is diagnosed based on the decomposed signal by the DBN model. Further, this article provides the performance analysis by determining the types I and II measures and root-mean-square-error (RMSE) measure. In the performance analysis, it compares the performance of the DBN model to various conventional models like linear support vector machine (SVM), quadratic SVM, radial basis function SVM, polynomial SVM, multilayer perceptron SVM, Levenberg-Marquardt neural network and gradient descent neural network models. The simulation results validate that the proposed DBN model effectively detects and classifies the fault signal in power distribution system when compared to the traditional model.
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M., Arshey, and Angel Viji K. S. "An optimization-based deep belief network for the detection of phishing e-mails." Data Technologies and Applications 54, no. 4 (July 16, 2020): 529–49. http://dx.doi.org/10.1108/dta-02-2020-0043.
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PurposePhishing is a serious cybersecurity problem, which is widely available through multimedia, such as e-mail and Short Messaging Service (SMS) to collect the personal information of the individual. However, the rapid growth of the unsolicited and unwanted information needs to be addressed, raising the necessity of the technology to develop any effective anti-phishing methods.Design/methodology/approachThe primary intention of this research is to design and develop an approach for preventing phishing by proposing an optimization algorithm. The proposed approach involves four steps, namely preprocessing, feature extraction, feature selection and classification, for dealing with phishing e-mails. Initially, the input data set is subjected to the preprocessing, which removes stop words and stemming in the data and the preprocessed output is given to the feature extraction process. By extracting keyword frequency from the preprocessed, the important words are selected as the features. Then, the feature selection process is carried out using the Bhattacharya distance such that only the significant features that can aid the classification are selected. Using the selected features, the classification is done using the deep belief network (DBN) that is trained using the proposed fractional-earthworm optimization algorithm (EWA). The proposed fractional-EWA is designed by the integration of EWA and fractional calculus to determine the weights in the DBN optimally.FindingsThe accuracy of the methods, naive Bayes (NB), DBN, neural network (NN), EWA-DBN and fractional EWA-DBN is 0.5333, 0.5455, 0.5556, 0.5714 and 0.8571, respectively. The sensitivity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.4558, 0.5631, 0.7035, 0.7045 and 0.8182, respectively. Likewise, the specificity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.5052, 0.5631, 0.7028, 0.7040 and 0.8800, respectively. It is clear from the comparative table that the proposed method acquired the maximal accuracy, sensitivity and specificity compared with the existing methods.Originality/valueThe e-mail phishing detection is performed in this paper using the optimization-based deep learning networks. The e-mails include a number of unwanted messages that are to be detected in order to avoid the storage issues. The importance of the method is that the inclusion of the historical data in the detection process enhances the accuracy of detection.
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Sheng, Dali, Jinlian Deng, Wei Zhang, Jie Cai, Weisheng Zhao, and Jiawei Xiang. "A Statistical Image Feature-Based Deep Belief Network for Fire Detection." Complexity 2021 (August 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/5554316.
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Detecting fires is of significance to guarantee the security of buildings and forests. However, it is difficult to fast and accurately detect fire stages in complex environment because of the large variations of the fire features of color, texture, and shapes for flame and smoke images. In this paper, a statistic image feature-based deep belief network (DBN) is proposed for fire detections. Firstly, for each individual image, all the statistic image features extracted from a flame and smoke image in time domain, frequency domain, and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBN to classify the multiple fire stages in complex environment. DBN can automatically learn fault features layer by layer using restricted Boltzmann machine (RBM). Experiments using the benchmark data of three groups of fire and fire-like images are classified by the present method, and the classification results are also compared with those commonly used support vector machine (SVM) and convolutional deep belief networks (CDBNs) to manifest the superiority of the classification accuracy.
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Kadam, Vinod Jagannath, and Shivajirao Manikrao Jadhav. "Optimal weighted feature vector and deep belief network for medical data classification." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 02 (December 3, 2019): 2050006. http://dx.doi.org/10.1142/s021969132050006x.
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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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Munish, Saran, Kumar Yadav Rajan, Maurya Pranjal, Devi Sangeeta, and Nath Tripathi Upendra. "A novel methodology for enhancing intrusion detection system." i-manager’s Journal on Software Engineering 17, no. 4 (2023): 9. http://dx.doi.org/10.26634/jse.17.4.20009.
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An Intrusion Detection System (IDS) monitors network traffic for suspicious activity and alerts when such an activity is discovered. In this study, the NSL-KDD cup 99 dataset was used to evaluate anomaly detection from intruders. Intrusion Detection System, Distributed Denial of Service (DDoS), Deep Belief Network (DBN), Random Forest, Naïve Bayes, Security Attack, Machine Learning. Pre-processing and normalization processes were performed on the dataset with inadequate, noisy, or duplicate data. A hybrid K-means clustering algorithm is used to combine clusters, which are classified using Deep Belief Networks (DBNs), Random Forest and Naïve Bayes. The study analyzed the dataset based on accuracy, precision, F-score, and false alarm rate, among which the DBN showed better performance than the other two ML algorithms.
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Kamada, Shin, Takumi Ichimura, and Toshihide Harada. "Knowledge Extraction of Adaptive Structural Learning of Deep Belief Network for Medical Examination Data." International Journal of Semantic Computing 13, no. 01 (March 2019): 67–86. http://dx.doi.org/10.1142/s1793351x1940004x.
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Deep learning has a hierarchical network structure to represent multiple features of input data. The adaptive structural learning method of Deep Belief Network (DBN) can reach the high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation–annihilation algorithm, and generate a new hidden layer in DBN by the extension of the algorithm. In this paper, the proposed adaptive structural learning of DBN (Adaptive DBN) was applied to the comprehensive medical examination data for cancer prediction. The developed prediction system showed higher classification accuracy for test data (99.5% for the lung cancer and 94.3% for the stomach cancer) than the several learning methods such as traditional RBM, DBN, Non-Linear Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Moreover, the explicit knowledge that makes the inference process of the trained DBN is required in deep learning. The binary patterns of activated neurons for given input in RBM and the hierarchical structure of DBN can represent the relation between input and output signals. These binary patterns were classified by C4.5 for knowledge extraction. Although the extracted knowledge showed slightly lower classification accuracy than the trained DBN network, it was able to improve inference speed by about 1/40. We report that the extracted IF-THEN rules from the trained DBN for medical examination data showed some interesting features related to initial condition of cancer.
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Xiao-Hong Qiu, Xiao-Hong Qiu, Jia-Li Chen Xiao-Hong Qiu, and Zi-Ying Ao Jia-Li Chen. "Stall Warning Algorithm of Axial Compressor Based on SSA-DBN." 電腦學刊 33, no. 3 (June 2022): 059–71. http://dx.doi.org/10.53106/199115992022063303005.
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<p>To solve the problem of stall warning for axial compressors, this paper proposes a stall warning algorithm based on the sparrow search algorithm, which optimizes the deep belief network. The deep belief network is trained by using deep learning to extract the FFT spectrum of compressor stall experiment data directly as the feature vector. To improve the accuracy of DBN classification, parameters of hidden layer nodes n and initial weights w of DBN were optimized by SSA algorithm, and stall warning algorithm model of SSA-DBN axial-flow compressor was established. The experimental results of the algorithms show that the stall data at each speed is at least 0.1～0.3s in advance for early warning. This method is 0.075~0.281s ahead of the various models from the past to the present, and noise is superimposed on the experimental data to verify the Robustness of the way, better surge warning margin performance, and engineering practicability. </p> <p> </p>
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Zhao, Huimin, Xiaoxu Yang, Baojie Chen, Huayue Chen, and Wu Deng. "Bearing fault diagnosis using transfer learning and optimized deep belief network." Measurement Science and Technology 33, no. 6 (March 7, 2022): 065009. http://dx.doi.org/10.1088/1361-6501/ac543a.
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Abstract Bearing is an important component in mechanical equipment. Its main function is to support the rotating mechanical body and reduce the friction coefficient and axial load. In the actual operating environment, the bearings are affected by complex working conditions and other factors. Therefore, it is very difficult to effectively obtain data that meets the conditions of independent and identical distribution of training data and test data, which result in unsatisfactory fault diagnosis results. As a transfer learning method, joint distribution adaptive (JDA) can effectively solve the learning problem of inconsistent distribution of training data and test data. In this paper, a new bearing fault diagnosis method based on JDA and deep belief network (DBN) with improved sparrow search algorithm (CWTSSA), namely JACADN is proposed. In the JACADN, the JDA is employed to carry out feature transfer between the source domain samples and target domain samples, that is, the source domain samples and target domain samples are mapped into the same feature space by the kernel function. Then the maximum mean difference is used as the metric to reduce the joint distribution difference between the samples in the two domains. Aiming at the parameter selection of the DBN, an improved sparrow search algorithm (CWTSSA) with global optimization ability is used to optimize the parameters of the DBN in order to construct an optimized DBN model. The obtained source domain samples and target domain samples are divided into training set and test set, which are input the optimized DBN to construct a bearing fault diagnosis model for improving the diagnosis accuracy. The effectiveness of the proposed method is verified by vibration data of QPZZ-II rotating machinery. The experimental results show that the proposed JACADN method can effectively improve the fault diagnosis accuracy of rolling bearings under variable operating conditions.
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Luu, Do Ngoc, Nguyen Ngoc Phien, and Duong Tuan Anh. "Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 274–80. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1047.
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There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.
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Ye, Zilin. "Application of Improved Deep Belief Network Model in 3D Art Design." Mathematical Problems in Engineering 2022 (April 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/2213561.
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In recent years, driven by the high-speed computing performance of computers and massive data on the Internet, deep nervine networks with highly abstract feature extraction and classification capabilities have been widely used in 3D art design and other fields, and a large number of breakthrough results have emerged. 3D art design is a research hotspot in the field of computer vision, which has broad application prospects and practical application value. Aiming at the problems of slow convergence and long training time of traditional deep belief network in the process of data feature expression, this paper proposes an unsupervised learning algorithm, namely adaptive deep belief network, and applies it to 3D art design. Its linear correction unit has good sparsity, which can improve the network performance well. The deep belief network DBN is formed by stacking the restricted Boltzmann machine RBM. The recognition research of 3D art design by optimizing the wavelet deep belief network can effectively improve the recognition rate and recognition speed of handwritten character recognition and achieve good results.
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Wei, Yuan, Huanchang Zhang, Jiahui Dai, Ruili Zhu, Lihong Qiu, Yuzhuo Dong, and Shuai Fang. "Deep Belief Network with Swarm Spider Optimization Method for Renewable Energy Power Forecasting." Processes 11, no. 4 (March 26, 2023): 1001. http://dx.doi.org/10.3390/pr11041001.
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Renewable energy power prediction plays a crucial role in the development of renewable energy generation, and it also faces a challenging issue because of the uncertainty and complex fluctuation caused by environmental and climatic factors. In recent years, deep learning has been increasingly applied in the time series prediction of new energy, where Deep Belief Networks (DBN) can perform outstandingly for learning of nonlinear features. In this paper, we employed the DBN as the prediction model to forecast wind power and PV power. A novel metaheuristic optimization algorithm, called swarm spider optimization (SSO), was utilized to optimize the parameters of the DBN so as to improve its performance. The SSO is a novel swarm spider behavior based optimization algorithm, and it can be employed for addressing complex optimization and engineering problems. Considering that the prediction performance of the DBN is affected by the number of the nodes in the hidden layer, the SSO is used to optimize this parameter during the training stage of DBN (called SSO-DBN), which can significantly enhance the DBN prediction performance. Two datasets, including wind power and PV power with their influencing factors, were used to evaluate the forecasting performance of the proposed SSO-DBN. We also compared the proposed model with several well-known methods, and the experiment results demonstrate that the proposed prediction model has better stability and higher prediction accuracy in comparison to other methods.
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Patil, Balasaheb H. "Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics." International Journal of Swarm Intelligence Research 13, no. 3 (July 1, 2022): 1–21. http://dx.doi.org/10.4018/ijsir.304401.
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This paper intends to propose a new model for detecting the patch based inpainting operation using Enhanced Deep Belief Network (E-DBN). The proposing model makes strong supervising of DBN that will capture the manipulated information. In fact, the enhancement is done under optimization concept, where the activation function and weight of DBN is optimally tuned by a new hybrid algorithm termed as Swarm Mutated Lion Algorithm (SM-LA). The hybridization model combines two conventional models: Group Search Optimizer (GSO) and Lion Algorithm (LA). Finally, the performance of proposed model is compared over other conventional models with respect to certain performance measures.
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Li, Zhengying, Hong Huang, Zhen Zhang, and Guangyao Shi. "Manifold-Based Multi-Deep Belief Network for Feature Extraction of Hyperspectral Image." Remote Sensing 14, no. 6 (March 19, 2022): 1484. http://dx.doi.org/10.3390/rs14061484.
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Deep belief networks (DBNs) have been widely applied in hyperspectral imagery (HSI) processing. However, the original DBN model fails to explore the prior knowledge of training samples which limits the discriminant capability of extracted features for classification. In this paper, we proposed a new deep learning method, termed manifold-based multi-DBN (MMDBN), to obtain deep manifold features of HSI. MMDBN designed a hierarchical initialization method that initializes the network by local geometric structure hidden in data. On this basis, a multi-DBN structure is built to learn deep features in each land-cover class, and it was used as the front-end of the whole model. Then, a discrimination manifold layer is developed to improve the discriminability of extracted deep features. To discover the manifold structure contained in HSI, an intrinsic graph and a penalty graph are constructed in this layer by using label information of training samples. After that, the deep manifold features can be obtained for classification. MMDBN not only effectively extracts the deep features from each class in HSI, but also maximizes the margins between different manifolds in low-dimensional embedding space. Experimental results on Indian Pines, Salinas, and Botswana datasets reach 78.25%, 90.48%, and 97.35% indicating that MMDBN possesses better classification performance by comparing with some state-of-the-art methods.
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Wang, Xiangqian, Fang Huang, Wencong Wan, and Chengyuan Zhang. "Academic Activities Transaction Extraction Based on Deep Belief Network." Advances in Multimedia 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/5067069.
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Extracting information about academic activity transactions from unstructured documents is a key problem in the analysis of academic behaviors of researchers. The academic activities transaction includes five elements: person, activities, objects, attributes, and time phrases. The traditional method of information extraction is to extract shallow text features and then to recognize advanced features from text with supervision. Since the information processing of different levels is completed in steps, the error generated from various steps will be accumulated and affect the accuracy of final results. However, because Deep Belief Network (DBN) model has the ability to automatically unsupervise learning of the advanced features from shallow text features, the model is employed to extract the academic activities transaction. In addition, we use character-based feature to describe the raw features of named entities of academic activity, so as to improve the accuracy of named entity recognition. In this paper, the accuracy of the academic activities extraction is compared by using character-based feature vector and word-based feature vector to express the text features, respectively, and with the traditional text information extraction based on Conditional Random Fields. The results show that DBN model is more effective for the extraction of academic activities transaction information.
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Cai, Yulin, Puran Fan, Sen Lang, Mengyao Li, Yasir Muhammad, and Aixia Liu. "Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network." Remote Sensing 14, no. 22 (November 10, 2022): 5681. http://dx.doi.org/10.3390/rs14225681.
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The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil surface moisture were analyzed, and the significantly correlated ones were selected as predictors for the downscaling model. Second, a DBN model was trained and used to downscale the 9 km SMAP L4 SM to 1 km in the study area on 25 September 2019. Validation was performed using original SMAP L4 SM data and in situ measurements from SM and temperature wireless sensor network with 34 sites. Finally, the DBN method was compared with another commonly used machine learning model-random forest (RF). Results showed that (1) the downscaled 1 km SM data are in good agreement with the original SMAP L4 SM data and field measured data, and (2) DBN has a higher correlation coefficient and a lower root mean square error than those of RF. The coefficients of determination for fitting the two models with the measured data at the site were 0.5260 and 0.4816, with relative mean square errors of 0.0303 and 0.0342 m3/m3, respectively. The study also demonstrated the applicability of the DBN method to AMSR SM data downscaling besides SMAP. The proposed method can provide a framework to support future hydrological modeling, regional drought monitoring, and agricultural research.
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Zeng, Qingwen, Chunyan Hu, Jiaxian Sun, Yafeng Shen, and Keqiang Miao. "Monitoring of Thermoacoustic Combustion Instability via Recurrence Quantification Analysis and Optimized Deep Belief Network." Symmetry 16, no. 3 (February 22, 2024): 266. http://dx.doi.org/10.3390/sym16030266.
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Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of recurrence quantification analysis (RQA) and an optimized deep belief network (DBN). Six samples of combustion state data were collected using two distinct types of burners to facilitate the training and validation of GA-DBN. The proposed methodology leverages RQA to extract intricate patterns and dynamic features from time series data representing combustion behavior. By quantifying the recurrence plot of specific patterns, the analysis provides valuable insights into the underlying thermoacoustic dynamics. Among three different feature extraction methods, RQA stands out remarkably in performance. These RQA-derived features serve as input to a carefully tuned DBN, which is trained to learn the complex relationships within the combustion process. The classification accuracy of deep belief network optimized by genetic algorithm (GA-DBN) reached an impressive 99.8%. Subsequent multiple comparisons were conducted between GA-DBN, DBN, and support vector machine (SVM), revealing that GA-DBN consistently demonstrated satisfactory classification results. This method holds significant importance in monitoring intricate combustion states.
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Yu, He, Zaike Tian, Hongru Li, Baohua Xu, and Guoqing An. "A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps." International Journal of Acoustics and Vibration 25, no. 3 (September 30, 2020): 373–82. http://dx.doi.org/10.20855/ijav.2020.25.31669.
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Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.
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Wang, Ching-Hsin, Kuo-Ping Lin, Yu-Ming Lu, and Chih-Feng Wu. "Deep Belief Network with Seasonal Decomposition for Solar Power Output Forecasting." International Journal of Reliability, Quality and Safety Engineering 26, no. 06 (December 2019): 1950029. http://dx.doi.org/10.1142/s0218539319500293.
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Solar power is a type of renewable energy system that uses solar energy to produce electricity, and is regarded as one of the most important power sources in Taiwan. Since sunshine duration affects the amount of energy that can be generated by a solar power, the seasons of the year are important factors that should be considered for accurate solar power prediction. In the last decade, the use of artificial intelligence for forecasting systems have been quite popular, and the deep belief network (DBN) models started getting more attention. In this study, a seasonal deep belief network (SDBN) was developed to forecast monthly solar power output data. The SDBN was constructed by combining seasonal decomposition method and DBN. Further, this study used monthly solar power output data from the Taiwan Power Company. The results indicated that the proposed forecasting system demonstrated a superior performance in terms of forecasting accuracy. Also, the performance of autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), and DBN obtained from a separate study were compared to the performance of the proposed SDBN model and showed that the latter was better than the other three models. Thus, the SDBN model can be used as an alternative method for monthly solar power output data forecasting.
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Obaid, Ahmed J., and Hassanain K. Alrammahi. "An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications." Applied Sciences 13, no. 21 (November 5, 2023): 12049. http://dx.doi.org/10.3390/app132112049.
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Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. This paper introduces a hybrid feature extraction network model to bolster the discriminative capacity of emotional features for multimedia applications. The proposed model comprises a convolutional neural network (CNN) and deep belief network (DBN) series. First, a spatial CNN network processed static facial images, followed by a temporal CNN network. The CNNs were fine-tuned based on facial expression recognition (FER) datasets. A deep belief network (DBN) model was then applied to integrate the segment-level spatial and temporal features. Deep fusion networks were jointly used to learn spatiotemporal features for discrimination purposes. Due to its generalization capabilities, we used a multi-class support vector machine classifier to classify the seven basic emotions in the proposed model. The proposed model exhibited 98.14% recognition performance for the JaFFE database, 95.29% for the KDEF database, and 98.86% for the RaFD database. It is shown that the proposed method is effective for all three databases, compared with the previous schemes for JAFFE, KDEF, and RaFD databases.
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Tian, Shengwei, Yilin Yan, Long Yu, Mei Wang, and Li Li. "Prediction of Anti-Malarial Activity Based on Deep Belief Network." International Journal of Computational Intelligence and Applications 17, no. 03 (September 2018): 1850012. http://dx.doi.org/10.1142/s1469026818500128.
Full textAbstract:
Malaria is a kind of disease that greatly threatens human health. Nearly half of the world’s population is at risk of malaria. Anti-malarial drugs which are sought, developed and synthesized keep malaria under control, having received increasing attention in drug discovery field. Machine learning techniques have been used widely in drug research and development. On the basis of semi-supervised machine learning for molecular descriptions, this research develops a multilayer deep belief network (DBN) that can be used to identify whether compounds have the anti-malarial activity. Firstly, the influence of feature dimensions on predicting accuracy is discussed. Furthermore, the proposed model is applied to contrast shallow machine learning and supervised machine learning with the similar deep architecture. The research results show that the proposed model can predict anti-malarial activity accurately. The stable performance on the evaluation metrics confirms the practicability of our model. The proposed DBN model performs better than other shallow supervised models and deep supervised models. Moreover, it could be applied to reduce the cost and the time of drug discovery.
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Zhu, Chang-Hao, and Jie Zhang. "Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks." International Journal of Automation and Computing 17, no. 1 (November 5, 2019): 44–54. http://dx.doi.org/10.1007/s11633-019-1203-x.
Full textAbstract:
Abstract This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN). The important quality variable melt index of polypropylene is hard to measure in industrial processes. Lack of online measurement instruments becomes a problem in polymer quality control. One effective solution is to use soft sensors to estimate the quality variables from process data. In recent years, deep learning has achieved many successful applications in image classification and speech recognition. DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture. It can meet the demand of modelling accuracy when applied to actual processes. Compared to the conventional neural networks, the training of DBN contains a supervised training phase and an unsupervised training phase. To mine the valuable information from process data, DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation. Selection of DBN structure is investigated in the paper. The modelling results achieved by DBN and feedforward neural networks are compared in this paper. It is shown that the DBN models give very accurate estimations of the polymer melt index.
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Yu, Long, Xinyu Shi, Shengwei Tian, Shuangyin Gao, and Li Li. "Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors Based on Deep Belief Network." International Journal of Computational Intelligence and Applications 16, no. 01 (March 2017): 1750002. http://dx.doi.org/10.1142/s146902681750002x.
Full textAbstract:
The cytochrome P450 (CYP) superfamily, exists in the human liver, is responsible for more than 90% of the metabolism of clinical drugs. So it is necessary to adopt a new kind of computer simulation methods that can predict the rejection capability of compounds for a concrete CYPs isoform. In this work, a model is presented for classification of CYP450 1A2 inhibitors and noninhibitors based on a multi-tiered deep belief network (DBN) on a large dataset. The dataset composed of more than 13,000 heterogeneous compounds was acquired from PubChem. Firstly, 139 2D and 53 3D descriptors are calculated and preprocessed. Then, the unsupervised learning method is used to train DBN model to automatically extract multiple levels of distributed representation from the descriptors of training set. Finally, by using testing set and external validation set, we evaluate the classified performance of DBN for the inhibition of CYP1A2. Meanwhile, the proposed model is compared with shallow machine learning models (support vector machine (SVM) and artificial neural network (ANN)). We also discussed the performance of DBN by comparing it with different features combination. The experimental results showed that DBN has a better prediction ability compared with SVM and ANN. And these models combined with the features of 2D and 3D obtain the best forecast accuracy.