Academic literature on the topic 'DBN (Deep Belief Network)'
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Journal articles on the topic "DBN (Deep Belief Network)"
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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.
Dissertations / Theses on the topic "DBN (Deep Belief Network)"
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Faulkner, Ryan. "Dyna learning with deep belief networks." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97177.
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The objective of reinforcement learning is to find "good" actions in an environment where feedback is provided through a numerical reward, and the current state (i.e. sensory input) is assumed to be available at each time step. The notion of "good" is defined as maximizing the expected cumulative returns over time. Sometimes it is useful to construct models of the environment to aid in solving the problem. We investigate Dyna-style reinforcement learning, a powerful approach for problems where not much real data is available. The main idea is to supplement real trajectories with simulated ones sampled from a learned model of the environment. However, in large state spaces, the problem of learning a good generative model of the environment has been open so far. We propose to use deep belief networks to learn an environment model. Deep belief networks (Hinton, 2006) are generative models that have been effective in learning the time dependency relationships among complex data. It has been shown that such models can be learned in a reasonable amount of time when they are built using energy models. We present our algorithm for using deep belief networks as a generative model for simulating the environment within the Dyna architecture, along with very promising empirical results.L'objectif de l'apprentissage par renforcement est de choisir de bonnes actions dansun environnement où les informations sont fournies par une récompense numérique, etl'état actuel (données sensorielles) est supposé être disponible à chaque pas de temps. Lanotion de "correct" est définie comme étant la maximisation des rendements attendus cumulatifsdans le temps. Il est parfois utile de construire des modèles de l'environnementpour aider à résoudre le problème. Nous étudions l'apprentissage par renforcement destyleDyna, une approche performante dans les situations où les données réelles disponiblesne sont pas nombreuses. L'idée principale est de compléter les trajectoires réelles aveccelles simulées échantillonnées partir d'un modèle appri de l'environnement. Toutefois,dans les domaines à plusieurs états, le problème de l'apprentissage d'un bon modèlegénératif de l'environnement est jusqu'à présent resté ouvert. Nous proposons d'utiliserles réseaux profonds de croyance pour apprendre un modèle de l'environnement. Lesréseaux de croyance profonds (Hinton, 2006) sont des modèles génératifs qui sont efficaces pourl'apprentissage des relations de dépendance temporelle parmi des données complexes. Ila été démontré que de tels modèles peuvent être appris dans un laps de temps raisonnablequand ils sont construits en utilisant des modèles de l'énergie. Nous présentons notre algorithmepour l'utilisation des réseaux de croyance profonds en tant que modèle génératifpour simuler l'environnement dans l'architecture Dyna, ainsi que des résultats empiriquesprometteurs.
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Kaabi, Rabeb. "Apprentissage profond et traitement d'images pour la détection de fumée." Electronic Thesis or Diss., Toulon, 2020. http://www.theses.fr/2020TOUL0017.
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Cette thèse aborde le problème de la détection des feux de forêt par des outils de traitement d’images et apprentissage machine. Un incendie de forêt est un feu qui se propage sur une étendue boisée. Il peut être d'origine naturelle (dû à la foudre ou à une éruption volcanique) ou humaine. Dans le monde entier, l’impact des feux de forêts sur de nombreux aspects de notre vie quotidienne se fait de plus en plus apparente sur l’écosystème entier. De nombreuses méthodes ont montré l’efficacité pour la détection des incendies de forêt. L’originalité du présent travail réside dans la détection précoce des incendies par la détection de la fumée de forêt et la classification des régions de fumée et de non fumée à l’aide d’apprentissage profond et des outils de traitement d’image. Un ensemble de techniques de prétraitement nous a aidé à avoir une base de donnée importante (ajout du bruit aux entrées, augmentation des données) qui nous a permis après de tester la robustesse du modèle basée sur le DBN qu’on a proposé et évaluer la performance en calculant les métriques suivantes (IoU, Précision, Rappel, F1 score). Finalement, l’algorithme proposé est testé sur plusieurs images afin de valider son efficacité. Les simulations de notre algorithme ont été comparées avec celles traités dans l’état de l’art (Deep CNN, SVM…) et ont fourni de très bons résultatsThis thesis deals with the problem of forest fire detection using image processing and machine learning tools. A forest fire is a fire that spreads over a wooded area. It can be of natural origin (due to lightning or a volcanic eruption) or human. Around the world, the impact of forest fires on many aspects of our daily lives is becoming more and more apparent on the entire ecosystem.Many methods have been shown to be effective in detecting forest fires. The originality of the present work lies in the early detection of fires through the detection of forest smoke and the classification of smoky and non-smoky regions using deep learning and image processing tools. A set of pre-processing techniques helped us to have an important database which allowed us afterwards to test the robustness of the model based on deep belief network we proposed and to evaluate the performance by calculating the following metrics (IoU, Accuracy, Recall, F1 score). Finally, the proposed algorithm is tested on several images in order to validate its efficiency. The simulations of our algorithm have been compared with those processed in the state of the art (Deep CNN, SVM...) and have provided very good results. The results of the proposed methods gave an average classification accuracy of about 96.5% for the early detection of smoke
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Bosello, Michael. "Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21467/.
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Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework allows the development of BDI agents having both explicitly programmed plans and plans learned by the agent using RL. The two kinds of plans are seamlessly integrated and can be used without differences. Here, we take autonomous driving as a case study to verify the advantages of the proposed approach and framework. The BDI agent has hard-coded plans that define high-level directions while fine-grained navigation is learned by trial and error. This approach – compared to plain RL – is encouraging as RL struggles in temporally extended planning. We defined and trained an agent able to drive in a track with an intersection, at which it has to choose the correct path to reach the assigned target. A first step towards porting the system in the real-world has been done by building a 1/10 scale racecar prototype which learned how to drive in a simple track.
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de, Giorgio Andrea. "A study on the similarities of Deep Belief Networks and Stacked Autoencoders." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174341.
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Restricted Boltzmann Machines (RBMs) and autoencoders have been used - in several variants - for similar tasks, such as reducing dimensionality or extracting features from signals. Even though their structures are quite similar, they rely on different training theories. Lately, they have been largely used as building blocks in deep learning architectures that are called deep belief networks (instead of stacked RBMs) and stacked autoencoders. In light of this, the student has worked on this thesis with the aim to understand the extent of the similarities and the overall pros and cons of using either RBMs, autoencoders or denoising autoencoders in deep networks. Important characteristics are tested, such as the robustness to noise, the influence on training of the availability of data and the tendency to overtrain. The author has then dedicated part of the thesis to study how the three deep networks in exam form their deep internal representations and how similar these can be to each other. In result of this, a novel approach for the evaluation of internal representations is presented with the name of F-Mapping. Results are reported and discussed.
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Larsson, Marcus, and Christoffer Möckelind. "The effects of Deep Belief Network pre-training of a Multilayered perceptron under varied labeled data conditions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187374.
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Sometimes finding labeled data for machine learning tasks is difficult. This is a problem for purely supervised models like the Multilayered perceptron(MLP). A Discriminative Deep Belief Network(DDBN) is a semi-supervised model that is able to use both labeled and unlabeled data. This research aimed to move towards a rule of thumb of when it is beneficial to use a DDBN instead of an MLP, given the proportions of labeled and unlabeled data. Several trials with different amount of labels, from the MNIST and Rectangles-Images datasets, were conducted to compare the two models. It was found that for these datasets, the DDBNs had better accuracy when few labels were available. With 50% or more labels available, the DDBNs and MLPs had comparable accuracies. It is concluded that a rule of thumb of using a DDBN when less than 50% of labels are available for training, would be in line with the results. However, more research is needed to make any general conclusions.Märkt data kan ibland vara svårt att hitta för maskininlärningsuppgifter. Detta är ett problem för modeller som bygger på övervakad inlärning, exem- pelvis Multilayerd Perceptron(MLP). Ett Discriminative Deep Belief Network (DDBN) är en semi-övervakad modell som kan använda både märkt och omärkt data. Denna forskning syftar till att närma sig en tumregel om när det är för- delaktigt att använda en DDBN i stället för en MLP, vid olika proportioner av märkt och omärkt data. Flera försök med olika mängd märkt data, från MNIST och Rectangle-Images datamängderna, genomfördes för att jämföra de två mo- dellerna. Det konstaterades att för dessa datamängder hade DDBNerna bättre precision när ett fåtal märkt data fanns tillgängligt. När 50% eller mer av datan var märkt, hade DDBNerna och MLPerna jämförbar noggrannhet. Slutsatsen är att en tumregel att använda en DDBN när mindre än 50% av av träningsdatan är märkt, skulle vara i linje med resultaten. Det behövs dock mer forskning för att göra några generella slutsatser.
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Sadli, Rahmad. "Étude et développement d'un dispositif routier d'anticollision basé sur un radar ultra large bande pour la détection et l'identification notamment des usagers vulnérables." Thesis, Valenciennes, 2019. http://www.theses.fr/2019VALE0005.
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Dans ce travail de thèse, nous présentons nos travaux qui portent sur l’identification des cibles en général par un radar Ultra-Large Bande (ULB) et en particulier l’identification des cibles dont la surface équivalente radar est faible telles que les piétons et les cyclistes. Ce travail se décompose en deux parties principales, la détection et la reconnaissance. Dans la première approche du processus de détection, nous avons proposé et étudié un détecteur de radar ULB robuste qui fonctionne avec des données radar 1-D (A-scan) à une dimension. Il exploite la combinaison des statistiques d’ordres supérieurs et du détecteur de seuil automatique connu sous le nom de CA-CFAR pour Cell-Averaging Constant False Alarm Rate. Cette combinaison est effectuée en appliquant d’abord le HOS sur le signal reçu afin de supprimer une grande partie du bruit. Puis, après avoir éliminé le bruit du signal radar reçu, nous implémentons le détecteur de seuil automatique CA-CFAR. Ainsi, cette combinaison permet de disposer d’un détecteur de radar ULB à seuil automatique robuste. Afin d’améliorer le taux de détection et aller plus loin dans le traitement, nous avons évalué l’approche des données radar 2-D (B-Scan) à deux dimensions. Dans un premier temps, nous avons proposé une nouvelle méthode de suppression du bruit, qui fonctionne sur des données B-Scan. Il s’agit d’une combinaison de WSD et de HOS. Pour évaluer les performances de cette méthode, nous avons fait une étude comparative avec d’autres techniques de suppression du bruit telles que l’analyse en composantes principales, la décomposition en valeurs singulières, la WSD, et la HOS. Les rapports signal à bruit -SNR- des résultats finaux montrent que les performances de la combinaison WSD et HOS sont meilleures que celles des autres méthodes rencontrées dans la littérature. A la phase de reconnaissance, nous avons exploité les données des deux approches à 1-D et à 2-D obtenues à partir du procédé de détection. Dans la première approche à 1-D, les techniques SVM et le DBN sont utilisées et évaluées pour identifier la cible en se basant sur la signature radar. Les résultats obtenus montrent que la technique SVM donne de bonnes performances pour le système proposé où le taux de reconnaissance global moyen atteint 96,24%, soit respectivement 96,23%, 95,25% et 97,23% pour le cycliste, le piéton et la voiture. Dans la seconde approche à 1-D, les performances de différents types d’architectures DBN composées de différentes couches ont été évaluées et comparées. Nous avons constaté que l’architecture du réseau DBN avec quatre couches cachées est meilleure et la précision totale moyenne peut atteindre 97,80%. Ce résultat montre que les performances obtenues avec le DBN sont meilleures que celles obtenues avec le SVM (96,24%) pour ce système de reconnaissance de cible utilisant un radar ULB. Dans l’approche bidimensionnelle, le réseau de neurones convolutifs a été utilisé et évalué. Nous avons proposé trois architectures de CNN. La première est le modèle modifié d’Alexnet, la seconde est une architecture avec les couches de convolution arborescentes et une couche entièrement connectée, et la troisième est une architecture avec les cinq couches de convolution et deux couches entièrement connectées. Après comparaison et évaluation des performances de ces trois architectures proposées nous avons constaté que la troisième architecture offre de bonnes performances par rapport aux autres propositions avec une précision totale moyenne qui peut atteindre 99,59%. Enfin, nous avons effectué une étude comparative des performances obtenues avec le CNN, DBN et SVM. Les résultats montrent que CNN a les meilleures performances en termes de précision par rapport à DBN et SVM. Cela signifie que l’utilisation de CNN dans les données radar bidimensionnels permet de classer correctement les cibles radar ULB notamment pour les cibles à faible SER et SNR telles que les cyclistes ou les piétonsIn this thesis work, we focused on the study and development of a system identification using UWB-Ultra-Wide-Band short range radar to detect the objects and particularly the vulnerable road users (VRUs) that have low RCS-Radar Cross Section- such as cyclist and pedestrian. This work is composed of two stages i.e. detection and recognition. In the first approach of detection stage, we have proposed and studied a robust UWB radar detector that works on one dimension 1-D radar data ( A-scan). It relies on a combination of Higher Order Statistics (HOS) and the well-known CA-CFAR (Cell-Averaging Constant False Alarm Rate) detector. This combination is performed by firstly applying the HOS to the received radar signal in order to suppress the noise. After eliminating the noise of the received radar signal, we apply the CA-CFAR detector. By doing this combination, we finally have an UWB radar detector which is robust against the noise and works with the adaptive threshold. In order to enhance the detection performance, we have evaluated the approach of using two dimensions 2-D (B-Scan) radar data. In this 2-D radar approach, we proposed a new method of noise suppression, which works on this B-Scan data. The proposed method is a combination of WSD (Wavelet Shrinkage Denoising) and HOS. To evaluate the performance of this method, we performed a comparative study with the other noise removal methods in literature including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), WSD and HOS. The Signal-to-Noise Ratio (SNR) of the final result has been computed to compare the effectiveness of individual noise removal techniques. It is observed that a combination of WSD and HOS has better capability to remove the noise compared to that of the other applied techniques in the literature; especially it is found that it allows to distinguish efficiency the pedestrian and cyclist over the noise and clutters whereas other techniques are not showing significant result. In the recognition phase, we have exploited the data from the two approaches 1-D and 2-D, obtained from the detection method. In the first 1-D approach, Support Vector Machines (SVM) and Deep Belief Networks (DBN) have been used and evaluated to identify the target based on the radar signature. The results show that the SVM gives good performances for the proposed system where the total recognition accuracy rate could achieve up to 96,24%. In the second approach of this 1-D radar data, the performance of several DBN architectures compose of different layers have been evaluated and compared. We realised that the DBN architecture with four hidden layers performs better than those of with two or three hidden layers. The results show also that this architecture achieves up to 97.80% of accuracy. This result also proves that the performance of DBN is better than that of SVM (96.24%) in the case of UWB radar target recognition system using 1-D radar signature. In the 2-D approach, the Convolutional Neural Network (CNN) has been exploited and evaluated. In this work, we have proposed and investigated three CNN architectures. The first architecture is the modified of Alexnet model, the second is an architecture with three convolutional layers and one fully connected layer, and the third is an architecture with five convolutional layers and two fully connected layers. The performance of these proposed architectures have been evaluated and compared. We found that the third architecture has a good performance where it achieves up to 99.59% of accuracy. Finally, we compared the performances obtained using CNN, DBN and SVM. The results show that CNN gives a better result in terms of accuracy compared to that of DBN and SVM. It allows to classify correctly the UWB radar targets like cyclist and pedestrian
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Tong, Zheng. "Evidential deep neural network in the framework of Dempster-Shafer theory." Thesis, Compiègne, 2022. http://www.theses.fr/2022COMP2661.
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Les réseaux de neurones profonds (DNN) ont obtenu un succès remarquable sur de nombreuses applications du monde réel (par exemple, la reconnaissance de formes et la segmentation sémantique), mais sont toujours confrontés au problème de la gestion de l'incertitude. La théorie de Dempster-Shafer (DST) fournit un cadre bien fondé et élégant pour représenter et raisonner avec des informations incertaines. Dans cette thèse, nous avons proposé un nouveau framework utilisant DST et DNNs pour résoudre les problèmes d'incertitude. Dans le cadre proposé, nous hybridons d'abord DST et DNN en branchant une couche de réseau neuronal basée sur DST suivie d'une couche utilitaire à la sortie d'un réseau neuronal convolutif pour la classification à valeur définie. Nous étendons également l'idée à la segmentation sémantique en combinant des réseaux entièrement convolutifs et DST. L'approche proposée améliore les performances des modèles DNN en attribuant des modèles ambigus avec une incertitude élevée, ainsi que des valeurs aberrantes, à des ensembles multi-classes. La stratégie d'apprentissage utilisant des étiquettes souples améliore encore les performances des DNN en convertissant des données d'étiquettes imprécises et non fiables en fonctions de croyance. Nous avons également proposé une stratégie de fusion modulaire utilisant ce cadre proposé, dans lequel un module de fusion agrège les sorties de la fonction de croyance des DNN évidents selon la règle de Dempster. Nous utilisons cette stratégie pour combiner des DNN formés à partir d'ensembles de données hétérogènes avec différents ensembles de classes tout en conservant des performances au moins aussi bonnes que celles des réseaux individuels sur leurs ensembles de données respectifs. De plus, nous appliquons la stratégie pour combiner plusieurs réseaux superficiels et obtenir une performance similaire d'un DNN avancé pour une tâche compliquéeDeep neural networks (DNNs) have achieved remarkable success on many realworld applications (e.g., pattern recognition and semantic segmentation) but still face the problem of managing uncertainty. Dempster-Shafer theory (DST) provides a wellfounded and elegant framework to represent and reason with uncertain information. In this thesis, we have proposed a new framework using DST and DNNs to solve the problems of uncertainty. In the proposed framework, we first hybridize DST and DNNs by plugging a DSTbased neural-network layer followed by a utility layer at the output of a convolutional neural network for set-valued classification. We also extend the idea to semantic segmentation by combining fully convolutional networks and DST. The proposed approach enhances the performance of DNN models by assigning ambiguous patterns with high uncertainty, as well as outliers, to multi-class sets. The learning strategy using soft labels further improves the performance of the DNNs by converting imprecise and unreliable label data into belief functions. We have also proposed a modular fusion strategy using this proposed framework, in which a fusion module aggregates the belief-function outputs of evidential DNNs by Dempster’s rule. We use this strategy to combine DNNs trained from heterogeneous datasets with different sets of classes while keeping at least as good performance as those of the individual networks on their respective datasets. Further, we apply the strategy to combine several shallow networks and achieve a similar performance of an advanced DNN for a complicated task
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Pasa, Luca. "Linear Models and Deep Learning: Learning in Sequential Domains." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3425865.
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With the diffusion of cheap sensors, sensor-equipped devices (e.g., drones), and sensor networks (such as Internet of Things), as well as the development of inexpensive human-machine interaction interfaces, the ability to quickly and effectively process sequential data is becoming more and more important. There are many tasks that may benefit from advancement in this field, ranging from monitoring and classification of human behavior to prediction of future events. Most of the above tasks require pattern recognition and machine learning capabilities.
There are many approaches that have been proposed in the past to learn in sequential domains, especially extensions in the field of Deep Learning. Deep Learning is based on highly nonlinear systems, which very often reach quite good classification/prediction performances, but at the expenses of a substantial computational burden.
Actually, when facing learning in a sequential, or more in general structured domain, it is common practice to readily resort to nonlinear systems. Not always, however, the task really requires a nonlinear system. So the risk is to run into difficult and computational expensive training procedures to eventually get a solution that improves of an epsilon (if not at all) the performances that can be reached by a simple linear dynamical system involving simpler training procedures and a much lower computational effort. The aim of this thesis is to discuss about the role that linear dynamical systems may have in learning in sequential domains. On one hand, we like to point out that a linear dynamical system (LDS) is able, in many cases, to already provide good performances at a relatively low computational cost. On the other hand, when a linear dynamical system is not enough to provide a reasonable solution, we show that it can be used as a building block to construct more complex and powerful models, or how to resort to it to design quite effective pre-training techniques for nonlinear dynamical systems, such as Echo State Networks (ESNs) and simple Recurrent Neural Networks (RNNs).
Specifically, in this thesis we consider the task of predicting the next event into a sequence of events. The datasets used to test various discussed models involve polyphonic music and contain quite long sequences. We start by introducing a simple state space LDS. Three different approaches to train the LDS are then considered. Then we introduce some brand new models that are inspired by the LDS and that have the aim to increase the prediction/classification capabilities of the simple linear models.
We then move to study the most common nonlinear models. From this point of view, we considered the RNN models, which are significantly more computationally demanding. We experimentally show that, at least for the addressed prediction task and the considered datasets, the introduction of pre-training approaches involving linear systems leads to quite large improvements in prediction performances. Specifically, we introduce pre-training via linear Autoencoder, and an alternative based on Hidden Markov Models (HMMs).
Experimental results suggest that linear models may play an important role for learning in sequential domains, both when used directly or indirectly (as basis for pre-training approaches): in fact, when used directly, linear models may by themselves return state-of-the-art performance, while requiring a much lower computational effort with respect to their nonlinear counterpart. Moreover, even when linear models do not perform well, it is always possible to successfully exploit them within pre-training approaches for nonlinear systems.Con la diffusione di dispositivi a basso costo, e reti di sensori (come ad esempio l'Internet of Things), nonché lo sviluppo di interfacce di interazione uomo-macchina a basso costo, la capacità di processare dati sequenziali in maniera veloce, e assicurando un basso consumo di risorse, è diventato sempre più importante. Molti sono i compiti che trarrebbero beneficio da un avanzamento in questo ambito, dal monitoraggio e classificazione di comportamenti umani fino alla predizioni di eventi futuri. Molti dei task citati richiedono l'uso di tecniche di pattern recognition e di abilità correlate con metodi tipici dell’apprendimento automatico. Molti sono gli approcci per eseguire apprendimento su domini sequenziali proposti nel recente passato, e molti sono basati su tecniche tipiche dell'ambito del Deep Learning. I metodi di Deep Learning sono tipicamente basati su sistemi fortemente non lineari, capaci di ottenere ottimi risultati in problemi di predizione/classificazione, ma che risultano anche essere molto costosi dal punto di vista computazionale. Quando si cerca di eseguire un compito di apprendimento su domini sequenziali, e più in generale su dati strutturati, tipicamente si ricorre all'utilizzo di sistemi non lineari. Non è però sempre vero che i task considerati richiedono modelli non lineari. Quindi il rischio è di andare ad utilizzare metodi troppo complessi, e computazionalmente costosi, per poi ottenere alla fine soluzioni che migliorano di un’epsilon (o anche no migliorano) i risultati ottenibili tramite l'utilizzo di sistemi lineari dinamici, che risultano essere molto meno costosi dal punto di vista dell'apprendimento, e del costo computazionale. L'obiettivo di questa tesi è di discutere del ruolo che i sistemi lineari dinamici possono avere nelle esecuzioni di compiti di apprendimento su dati strutturati. In questa tesi vogliamo mettere in luce le capacità dei sistemi lineari dinamici (LDS) di ottenere soluzioni molto buone ad un costo computazionale relativamente basso. Inoltre risulta interessante vedere come, nel caso in cui un sistema lineare non sia sufficiente per ottenere il risultato sperato, esso possa essere usato come base per costruire modelli più complessi, oppure possa essere utilizzato per eseguire la fase di pre-training per un modello non lineare, come ad esempio Echo State Networks (ESNs) e Recurrent Neural Networks (RNNs). Nello specifico in questa tesi è stato considerato un task di predizione dell'evento successivo, data una sequenza di eventi. I dataset usati per testare i vari modelli proposti nella tesi, contengono sequenze di musica polifonica, che risultano essere particolarmente lunghe e complesse. Nella prima parte della tesi viene proposto l'utilizzo del semplice modello LDS per affrontare il compito considerato. In particolare vengono considerati tre approcci diversi per eseguire l'apprendimento con questo modello. Viene poi introdotti nuovi modelli, ispirati al modello LDS, che hanno l'obiettivo di migliorare le prestazioni di quest'ultimo nei compiti di predizione/classificazione. Vengono poi considerati i più comuni modelli non lineari, in particolare il modello RNN il quale risulta essere significativamente più complesso e computazionalmente costoso da utilizzare. Viene quindi empiricamente dimostrato che, almeno per quanto riguarda il compito di predizione e i dataset considerati, l'introduzione di una fase di pre-training basati su sistemi lineari porta ad un significativo miglioramento delle prestazioni e della accuratezza nell'eseguire la predizione. In particolare 2 metodi di pre-training vengono proposti, il primo chiamato pre-training via Linear Autoencoder, ed il secondo basato su Hidden Markov Models (HMMs). I risultati sperimentali suggeriscono che i sistemi lineari possono giocare un ruolo importante per quanto riguarda il compito di apprendimento in domini sequenziali, sia che siano direttamente usati oppure siano usati indirettamente (come base per eseguire la fase di pre-training): infatti, usandoli direttamente, essi hanno permesso di raggiungere risultati che rappresentano lo stato dell'arte, andando però a richiedere uno sforzo computazionale molto limitato se confrontato con i più comuni modelli non lineari. Inoltre, anche quando le performance ottenute sono risultate non soddisfacenti, si è dimostrato che è possibile utilizzarli con successo per eseguire la fase di pre-training di sistemi non lineari.
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Nassar, Alaa S. N. "A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16917.
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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level.
Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image.
Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level.
Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image.
Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Iraq
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Nguyen, Tien Dung. "Multimodal emotion recognition using deep learning techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180753/1/Tien%20Dung_Nguyen_Thesis.pdf.
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This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.
Books on the topic "DBN (Deep Belief Network)"
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Dutsch, Dorota M. Pythagorean Women Philosophers. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198859031.001.0001.
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Modern scholarly accounts of Greek philosophical history usually exclude women. And yet, from Dixaearchus of Messana to Diogenes Laertius, classical writers record the names of women philosophers from various schools. What is more, pseudonymous treatises and letters (likely dating after the first century CE) articulate the teachings of Pythagorean women. How can this literature inform our understanding of Greek intellectual history? To take these texts at face value would be naïve; to reject them, narrow-minded. This book is a deep examination of the literary tradition surrounding female Pythagoreans; it envisions the tradition as a network of texts that does not represent female philosophers but enacts their role in Greek culture. Part I, “Portraits,” assembles and contextualizes excerpts from historical accounts and wisdom literature. Part II, “Impersonations,” analyzes pseudonymous treatises and letters. Texts are approached with a mixture of suspicion and belief, inspired by Paul Ricoeur’s hermeneutics. Suspicion serves to disclose the misogyny of the epistemic regimes that produced the texts about and by women philosophers. Belief takes us beyond the circumstances of the texts’ production to possible worlds of diverse readers, institutions, and practices that grant agency to the female knower. In the process, the book uncovers traces of a fascinating dialogue about the gender of philosophical knowledge, which includes female voices.
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van Onselen, Charles. The Night Trains. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197568651.001.0001.
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The full physical and social cost of South Africa’s twentieth-century mining revolution, based on the exploitation of cheap, commoditised, black, migrant labour, has yet to be fully understood. The success of the system, which contributed to the evolution of the policies of spatial segregation and apartheid, depended, in large measure, on the physical distance between the labourer’s home and places of work being successfully bridged by steam locomotives and a rail network. These night trains left deep scars in the urban and rural cultures of black communities, whether in the form of popular songs or in a belief in nocturnal witches’ trains that captured and conveyed zombie workers to the region’s most unpopular places of employment. Through careful analysis of the contrasting inward- and outward-bound legs of the migrants’ rail journey, van Onselen shows how black bodies (and minds) were ‘recruited’, transported and worked in the repressive compound system—sometimes to the point of insanity—and then returned broken, deranged, disabled or maimed to their country of origin, Mozambique. It offers a startling new analysis of the commodification of African labour in an inter-colonial setting.
Book chapters on the topic "DBN (Deep Belief Network)"
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Srikanth, M., D. Pravena, and D. Govind. "Tamil Speech Emotion Recognition Using Deep Belief Network(DBN)." In Advances in Intelligent Systems and Computing, 328–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67934-1_29.
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Ji, Jinbao, Zongxiang Hu, Weiqi Zhang, and Sen Yang. "Development of Deep Learning Algorithms, Frameworks and Hardwares." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 696–710. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_71.
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AbstractAs the core algorithm of artificial intelligence, deep learning has brought new breakthroughs and opportunities to all walks of life. This paper summarizes the principles of deep learning algorithms such as Autoencoder (AE), Boltzmann Machine (BM), Deep Belief Network (DBM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Recursive Neural Network (RNN). The characteristics and differences of deep learning frameworks such as Tensorflow, Caffe, Theano and PyTorch are compared and analyzed. Finally, the application and performance of hardware platforms such as CPU and GPU in deep learning acceleration are introduced. In this paper, the development and application of deep learning algorithm, framework and hardware technology can provide reference and basis for the selection of deep learning technology.
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Lopes, Noel, and Bernardete Ribeiro. "Deep Belief Networks (DBNs)." In Studies in Big Data, 155–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06938-8_8.
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Duan, Tiehang, and Sargur N. Srihari. "Pseudo Boosted Deep Belief Network." In Artificial Neural Networks and Machine Learning – ICANN 2016, 105–12. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44781-0_13.
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Ndehedehe, Christopher. "Deep Belief Network for Groundwater Modeling." In Springer Climate, 279–324. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37727-3_8.
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Xu, Shaoxun, Yufei Chen, Chao Ma, and Xiaodong Yue. "Deep Evidential Fusion Network for Image Classification." In Belief Functions: Theory and Applications, 185–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88601-1_19.
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Xue, Like, and Feng Su. "Auditory Scene Classification with Deep Belief Network." In MultiMedia Modeling, 348–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14445-0_30.
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Ghojogh, Benyamin, Mark Crowley, Fakhri Karray, and Ali Ghodsi. "Restricted Boltzmann Machine and Deep Belief Network." In Elements of Dimensionality Reduction and Manifold Learning, 501–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10602-6_18.
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Verma, Maneesh Kumar, Shankar Yadav, Bhoopesh Kumar Goyal, Bakshi Rohit Prasad, and Sonali Agarawal. "Phishing Website Detection Using Neural Network and Deep Belief Network." In Advances in Intelligent Systems and Computing, 293–300. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8639-7_30.
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Hu, Dan, Xingshe Zhou, and Junjie Wu. "Visual Tracking Based on Convolutional Deep Belief Network." In Lecture Notes in Computer Science, 103–15. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23216-4_8.
Full textConference papers on the topic "DBN (Deep Belief Network)"
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Tamilselvan, Prasanna, Pingfeng Wang, and Byeng D. Youn. "Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48352.
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Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Networks (DBN) based state classification. The DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked Restricted Boltzmann Machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using the DBN based state classification can be structured in three consecutive stages: first, defining health states and collecting sensory data for DBN training and testing; second, developing DBN based classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. The performance of health diagnostics using DBN based health state classification is compared with four existing classification methods and demonstrated with two case studies.
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Wang, Weiyan, Chen Jia, and Huijuan Gao. "LAI Inversion from MODIS Data Using Deep Belief Network (DBN)." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323211.
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Li, Yaqiong, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, and Scott A. Sisson. "Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/342.
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The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks; (3) the computational cost scales to the number of positive links only. In addition, we develop a new inference strategy, which first upward-and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.
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Deshmukh, Monika S., and Pavan Ravikesh Bhaladhare. "Intrusion Detection System (DBN-IDS) for IoT using Optimization Enabled Deep Belief Neural Network." In 2021 5th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2021. http://dx.doi.org/10.1109/iscon52037.2021.9702505.
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Rini Novitasari, Dian Candra, Ahmad Zoebad Foeady, Muhammad Thohir, Ahmad Zaenal Arifin, Khoirun Niam, and Ahmad Hanif Asyhar. "Automatic Approach for Cervical Cancer Detection Based on Deep Belief Network (DBN) Using Colposcopy Data." In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065196.
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Jin, Xiaoming, Tao He, Cheng Wan, Lan Yi, Guiguang Ding, and Dou Shen. "Automatic Gating of Attributes in Deep Structure." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/319.
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Deep structure has been widely applied in a large variety of fields for its excellence of representing data. Attributes are a unique type of data descriptions that have been successfully utilized in numerous tasks to enhance performance. However, to introduce attributes into deep structure is complicated and challenging, because different layers in deep structure accommodate features of different abstraction levels, while different attributes may naturally represent the data in different abstraction levels. This demands adaptively and jointly modeling of attributes and deep structure by carefully examining their relationship. Different from existing works that treat attributes straightforwardly as the same level without considering their abstraction levels, we can make better use of attributes in deep structure by properly connecting them. In this paper, we move forward along this new direction by proposing a deep structure named Attribute Gated Deep Belief Network (AG-DBN) that includes a tunable attribute-layer gating mechanism and automatically learns the best way of connecting attributes to appropriate hidden layers. Experimental results on a manually-labeled subset of ImageNet, a-Yahoo and a-Pascal data set justify the superiority of AG-DBN against several baselines including CNN model and other AG-DBN variants. Specifically, it outperforms the CNN model, VGG19, by significantly reducing the classification error from 26.70% to 13.56% on a-Pascal.
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Colbert, Ian, Ken Kreutz-Delgado, and Srinjoy Das. "AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852476.
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LV, Zhining, Ziheng HU, Baifeng NING, Lifu DING, Gangfeng YAN, and Xiasheng SHI. "Non-intrusive Runtime Monitoring for Power System Intelligent Terminal Based on Improved Deep Belief Networks (I-DBN)." In 2019 4th International Conference on Power and Renewable Energy (ICPRE). IEEE, 2019. http://dx.doi.org/10.1109/icpre48497.2019.9034805.
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singh, Kunal, and K. James Mathai. "Performance Comparison of Intrusion Detection System Between Deep Belief Network (DBN)Algorithm and State Preserving Extreme Learning Machine (SPELM) Algorithm." In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019. http://dx.doi.org/10.1109/icecct.2019.8869492.
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Ravikumaran, P., K. Vimala Devi, and K. Valarmathi. "Prediction of Chronic and Non- Chronic Kidney disease using Modified DBN with Map and Reduce Framework." In 8th International Conference on Artificial Intelligence and Fuzzy Logic System (AIFZ 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121615.
Full textAbstract:
Modern medical information comes in the form of an enormous volume of data that is challenging to maintain using conventional methods. The advancement of big data in the medical and basic healthcare societies is facilitated by precision medical data research, which focuses on comprehending early illness, patient healthcare facilities, and providers. It concentrates primarily on anticipating and discovering direct analysis of some of the substantial health effects that have increased in numerous countries. The existing health industry cannot retrieve detailed information from the chronic disease directory. The advancement of CKD (chronic kidney disease) and the methods used to identify the disease is a difficult task that can lower the cost of diagnosis. In this research, a modified MapReduce and pruning layer-based classification model using the deep belief network (DBN) and the dataset used as CKD were acquired from the UCI repository of machine learning. We have utilized the full potentiality of the DBNs by deploying deep learning methodology to establish better classification of the patient's kidney. Finally, data will be trained and classified using the classification layer and the quality will be compared to the existing method.