Relevant bibliographies by topics / Deep Belief Network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep Belief Network'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Deep Belief Network"

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Ghasemi, Fahimeh, Alireza Mehridehnavi, Afshin Fassihi, and Horacio Pérez-Sánchez. "Deep neural network in QSAR studies using deep belief network." Applied Soft Computing 62 (January 2018): 251–58. http://dx.doi.org/10.1016/j.asoc.2017.09.040.

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Guang Huo, Qi Zhang, Yangrui Zhang, Yuanning Liu, Huan Guo, and Wenyu Li. "Multi-Source Heterogeneous Iris Recognition Using Stacked Convolutional Deep Belief Networks-Deep Belief Network Model." Pattern Recognition and Image Analysis 31, no. 1 (2021): 81–90. http://dx.doi.org/10.1134/s1054661821010119.

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Bello, Rotimi-Williams, Abdullah Zawawi Talib, and Ahmad Sufril Azlan Mohamed. "APPLICATION OF DEEP BELIEF NETWOEK FOR DETECTION OF TRYPANOSOMIASIS PARASITE IN CATTLE USING BLOOD SMEAR IMAGE." Journal of Telecommunications System & Management 9, no. 3 (2020): 4. https://doi.org/10.5281/zenodo.4453854.

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Abstract: Currently, disease management, symptoms classification and diagnoses are manually performed and are time consuming due to the fact that it takes longer time for infected animal to be manually diagnosed especially those animals which are remote from veterinary. These challenges motivate the development of detection tools that can perform automatically using deep learning approaches such as convolutional neural networks which have received great acceptance in literature. This paper seeks to improve on the deep learning methods of managing animal diseases by using a trained model based
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Chu, Joseph Lin, and Adam Krzyźak. "The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks." Journal of Artificial Intelligence and Soft Computing Research 4, no. 1 (2014): 5–19. http://dx.doi.org/10.2478/jaiscr-2014-0021.

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Abstract Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative
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Pundir, Arun Singh, and Balasubramanian Raman. "Deep Belief Network For Smoke Detection." Fire Technology 53, no. 6 (2017): 1943–60. http://dx.doi.org/10.1007/s10694-017-0665-z.

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Zhang, Shenglong. "Research on the Deep Learning Technology in the Hull Form Optimization Problem." Journal of Marine Science and Engineering 10, no. 11 (2022): 1735. http://dx.doi.org/10.3390/jmse10111735.

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A high−accuracy objective function evaluation method is crucial in ship hull form optimization. This study proposes a novel approximate ship hull form optimization framework using the deep learning technology, deep belief network algorithm. To illustrate the advantages of using the deep belief network algorithm in the prediction of total resistance, two traditional surrogate models (ELMAN and RBF neural networks) are also employed in this study to predict total resistance for different modified ship models. It can be seen from the results that the deep belief network algorithm is more suitable
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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 expressio
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Lee, Tae-Ju, and Kwee-Bo Sim. "Vowel Classification of Imagined Speech in an Electroencephalogram using the Deep Belief Network." Journal of Institute of Control, Robotics and Systems 21, no. 1 (2015): 59–64. http://dx.doi.org/10.5302/j.icros.2015.14.0073.

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Bello, Rotimi-Williams, Zidiegha Seiyaboh, Daniel A. Olubummo, and Abdullah Zawawi Talib. "Classification of Dataset Using Deep Belief Networks Clustering Method." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (2020): 2856–60. https://doi.org/10.30534/ijatcse/2020/57932020.

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ABSTRACT   Dataset in large collection involves considerable handling in its analysis especially when it is being employed in classification problems that involve big data. Due to the technology development, the manner and approach in which this dataset is being manipulated for classification purposes differ not only in one respect but in many respects with different uncorrelated results which sometimes make prediction inaccurate. By definition, classification is the act of arranging objects into classes or categories of the same type; these objects can be huge or otherwise, and to manual
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Wang, Yuhui, and Xiaoyang Tan. "Deep Recurrent Belief Propagation Network for POMDPs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 10236–44. http://dx.doi.org/10.1609/aaai.v35i11.17227.

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In many real-world sequential decision-making tasks, especially in continuous control like robotic control, it is rare that the observations are perfect, that is, the sensory data could be incomplete, noisy or even dynamically polluted due to the unexpected malfunctions or intrinsic low quality of the sensors. Previous methods handle these issues in the framework of POMDPs and are either deterministic by feature memorization or stochastic by belief inference. In this paper, we present a new method that lies somewhere in the middle of the spectrum of research methodology identified above and co
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Dissertations / Theses on the topic "Deep Belief Network"

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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 e
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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 f
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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 un
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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
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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
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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.
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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 o
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Josefsson, Alexandra. "Modeling an Embedded Climate System Using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290676.

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Recent advancements in processing power, storage capabilities, and availability of data, has led to improvements in many applications through the use of machine learning. Using machine learning in control systems was first suggested in the 1990s, but is more recently being implemented. In this thesis, an embedded climate system, which is a type of control system, will be looked at. The ways in which machine learning can be used to replicate portions of the climate system is looked at. Deep Belief Networks are the machine learning models of choice. Firstly, the functionality of a PID controller
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Yogeswaran, Arjun. "Self-Organizing Neural Visual Models to Learn Feature Detectors and Motion Tracking Behaviour by Exposure to Real-World Data." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37096.

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Advances in unsupervised learning and deep neural networks have led to increased performance in a number of domains, and to the ability to draw strong comparisons between the biological method of self-organization conducted by the brain and computational mechanisms. This thesis aims to use real-world data to tackle two areas in the domain of computer vision which have biological equivalents: feature detection and motion tracking. The aforementioned advances have allowed efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcr
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Imbulgoda, Liyangahawatte Gihan Janith Mendis. "Hardware Implementation and Applications of Deep Belief Networks." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1476707730643462.

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Books on the topic "Deep Belief Network"

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Big Data and Artificial Intelligence Based Early Risk Warning System of Fire Hazard for Smart Cities: Deep Belief Network ,Artificial Intelligence ,smart Cities,smart Cities Big Data. Independently Published, 2021.

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Deep belief nets in C++ and CUDA C. CreateSpace Independent Publishing Platform, 2015.

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Masters, Timothy. Deep Belief Nets in C++ and CUDA C: Volume 3: Convolutional Nets. Apress, 2018.

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Masters, Timothy. Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks. Apress, 2018.

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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 Pytha
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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 so
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Bouachrine, Ibtissam. Anthem of Misogyny. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9798881809782.

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Anthem of Misogyny: The War on Women in North Africa and the Middle East argues that misogyny—which operates through an interconnected network of ideologies, institutions, beliefs, aesthetics, and cultural trends—is too complex and too deep rooted to eradicate with superficial changes. Like a national anthem, misogyny in North Africa and the Middle East has acquired a sacred status. It is accepted uncritically and woven effortlessly into daily practices, creating a community of men of different ages, educational levels, and socioeconomic backgrounds who are united in their sense of entitlement
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Book chapters on the topic "Deep Belief Network"

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Duan, Tiehang, and Sargur N. Srihari. "Pseudo Boosted Deep Belief Network." In Artificial Neural Networks and Machine Learning – ICANN 2016. 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. 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. 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. 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. 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. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8639-7_30.

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Krasnoproshin, Viktor V., and Vadim V. Matskevich. "Neural Network Data Processing Technology Based on Deep Belief Networks." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60447-9_15.

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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. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23216-4_8.

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Mughees, Atif, and Linmi Tao. "Efficient Deep Belief Network Based Hyperspectral Image Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71598-8_31.

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Deshmukh, Vaidehi, Arti Khaparde, and Sana Shaikh. "Multi-focus Image Fusion Using Deep Belief Network." In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63673-3_28.

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Conference papers on the topic "Deep Belief Network"

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Elsersy, Wael Farouk, Moataz Samy, and Ahmed ElShamy. "Network Intrusion Detection Using Deep Belief Network (DBN)." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652732.

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Preethi, S., S. Maheswari, R. Sahila Devi, D. Karthikeyan, Samsudeen Shaffi S, and M. Perarasi. "Securing IoT Networks with Deep Belief Network-Based Intrusion Monitoring Systems." In 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2025. https://doi.org/10.1109/ispcc66872.2025.11039559.

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Wang, Zhao, Qingyang Song, Si Chen, et al. "Aerial Target Intention Recognition Based on Deep Belief Network." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10827086.

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S, Sandeep C., and Martin Margala. "Parkinson’s Disease Prediction Using Spectrograms and Deep Belief Networks." In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025. https://doi.org/10.1109/csnt64827.2025.10968302.

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Sharma, Manoj Kumar, Debdoot Sheet, and Prabir Kumar Biswas. "Abnormality Detecting Deep Belief Network." In the International Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2979779.2979790.

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Keyvanrad, Mohammad Ali, and Mohammad Mehdi Homayounpour. "Normal sparse Deep Belief Network." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280688.

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Sae-Lim, Wannipa, Wiphada Wettayaprasit, and Pattara Aiyarak. "Leukemia Classification using Deep Belief Network." In Artificial Intelligence and Applications. ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.793-043.

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Chen, Zhuyun, Xueqiong Zeng, Weihua Li, and Guanglan Liao. "Machine fault classification using deep belief network." In 2016 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2016. http://dx.doi.org/10.1109/i2mtc.2016.7520473.

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Li, Juncen, Sheng Gao, Ning Han, Zhou Fang, and Jianxin Liao. "Music Mood Classification via Deep Belief Network." In 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2015. http://dx.doi.org/10.1109/icdmw.2015.136.

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Zhang, Yajun, Zongtian Liu, Wen Zhou, and Yalan Zhang. "Object Recognition Base on Deep Belief Network." In 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2015. http://dx.doi.org/10.1109/iske.2015.60.

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Reports on the topic "Deep Belief Network"

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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condit
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