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Research on a Vehicle-Mounted Intelligent TCM Syndrome Differentiation System Based on Deep Belief Network

Zhaohui Ning, Yihao Wang, Lin Wang, Lijuan Wu, Jianqiang Song
2021 IEEE Access  
the multi-label into a single label, and combines the deep belief network for each The classification model obtained from syndrome training solves the complex relationship between symptoms and syndrome  ...  DEEP BELIEF NETWORK Deep learning is a more complex machine learning, a new research field.  ... 
doi:10.1109/access.2021.3105588 fatcat:jyjyvrumszhthbj5yysbniwdhu

Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

Prasanalakshmi Balaji, B. Sri Revathi, Praveetha Gobinathan, Shermin Shamsudheen, Thavavel Vaiyapuri
2022 Computers Materials & Continua  
Based on this insight, a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network (ODBNN) is proposed in this article.  ...  The investigation evident the performance of incorporating optimization techniques for medical image classification is better than conventional techniques.  ...  Acknowledgement: The authors would like to express her gratitude to King Khalid University, Saudi Arabia for providing administrative and technical support.  ... 
doi:10.32604/cmc.2022.028560 fatcat:kyh6zneu4fhwlnf6cvizsux6wm

Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks

Annapareddy V. N. Reddy, Ch. Phani Krishna, Pradeep Kumar Mallick, Sandeep Kumar Satapathy, Prayag Tiwari, Mikhail Zymbler, Sachin Kumar
2020 Journal of Big Data  
In this study, we have proposed a classification model using hybrid deep belief networks (DBN) to classify magnetic resonance imaging (MRI) for GBM tumor.  ...  DBN often requires a large number of hidden layers that consists of large number of neurons to learn the best features from the raw image data.  ...  This work is financially supported by the Ministry of Science and Higher Education of the Russian Federation (Government Order FENU-2020-0022).  ... 
doi:10.1186/s40537-020-00311-y fatcat:u6bijodeqvaoxjyywlr6vdaj7m

Deep Learning Methods for Cardiovascular Image

Yankun Cao, Zhi Liu, Pengfei Zhang, Yushuo Zheng, Yongsheng Song, Lizhen Cui
2019 Journal of Artificial Intelligence and Systems  
learning, and then summarizes the application of deep learning in heart image segmentation, classification and other aspects combined with existing technologies.  ...  In recent years, more and more researchers have begun to pay attention to such processing technologies as pattern recognition, classification and segmentation in medical image processing.  ...  Medical image segmentation is a preprocessing step for feature extraction and classification, which separates abnormal or special parts in medical images.  ... 
doi:10.33969/ais.2019.11006 fatcat:egx5tibvm5dhriemz4dvx5ktsm

Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

Areej A. Malibari, Marwa Obayya, Mohamed K. Nour, Amal S. Mehanna, Manar Ahmed Hamza, Abu Sarwar Zamani, Ishfaq Yaseen, Abdelwahed Motwakel
2022 Computers Materials & Continua  
The optimized features are applied to the classifier called Deep belief network (DBN) to classify the benign and malignant images.  ...  The two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function (TL-CNN-EBOG) to select the final most relevant features.  ...  Classification Using DBN The optimized feature set detects the benign and malignant input dataset using a deep belief network (DBN).  ... 
doi:10.32604/cmc.2022.030492 fatcat:ciemee4nuzfqfjj7lzn37gocei

A Kernel-Based Framework for Medical Big-Data Analytics [chapter]

David Windridge, Miroslaw Bober
2014 Lecture Notes in Computer Science  
The recent trend towards standardization of Electronic Health Records (EHRs) represents a significant opportunity and challenge for medical big-data analytics.  ...  For pre-processing of image-based MR data we advocate a Deep Learning solution for contextual areal segmentation, with edit-distance based kernel measurement then used to characterize relevant morphology  ...  This will require experimentation with the methodology of convolutional deep belief networks in order to optimize the approach for hierarchical image decomposition in a manner most useful to medical objectives  ... 
doi:10.1007/978-3-662-43968-5_11 fatcat:44khrkn4mbcl5acquwkluy2tky

Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Xiaoling Liu, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated.  ...  First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed.  ...  In Section 4, the deep belief network classification algorithm and Big Data analysis in the time domain are used to construct a model for calculating dot product of English feature data vectors and an  ... 
doi:10.1155/2021/5609885 fatcat:mc4kvdrsifcujkhtw4qjayo5xu

Deep Principal Correlated Auto-encoders With Application to imaging and Genomics Data Integration

Gang Li, Chao Wang, De-Peng Han, Yi-Pu Zhang, Peng Peng, Vince D. Calhoun, Yu-Ping Wang
2020 IEEE Access  
INDEX TERMS Classification, data fusion, dimensionality reduction, belief network, optimization algorithm, principal component analysis.  ...  In order to complete a better result of correlation analysis and classification, the output nodes of multi-layer belief network are used for back propagation (BP) network training.  ...  weight within its own layer achieve the optimal eigenvector mapping for the layer, but not for the entire multi-layer belief networks.  ... 
doi:10.1109/access.2020.2968634 fatcat:pcvqm2edkbhkfmz6qhbnbxwkai

Breast cancer classification using deep belief networks

Ahmed M. Abdel-Zaher, Ayman M. Eldeib
2016 Expert systems with applications  
The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN).  ...  In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path.  ...  In classification task, the weight of each feature is computed by optimization technique.  ... 
doi:10.1016/j.eswa.2015.10.015 fatcat:bjbn4zhx5jbfjfzfmvnb34v7ye

A Deep Belief Network Based Brain Tumor Detection in MRI Images

2017 International Journal of Science and Research (IJSR)  
A DBN (Deep Belief Network) based classification method is used to identify brain tumor in MRI images which can yield the result more accurately.  ...  The system may mainly include three steps namely preprocessing, classification and post processing.  ...  To segment and classify the features in MR images many classification techniques are used such as SVM (Support Vector Machine), k-NN (k-Nearest Neighbor), NN (Neural Networks), DBN (Deep Belief Network  ... 
doi:10.21275/art20175321 fatcat:gdfqhhhy7jdcpnymeqcuezwlzm

Application of Multiacoustic Data in Feature Extraction of Anemometer

Dawei Chen, Xu Guo, Muhammad Javaid
2021 Complexity  
Through the deep belief network classification algorithm for self-learning, the instrument recognition method with strong applicability is established.  ...  Using multiple acoustic data for feature extraction, the recognition and matching between multiple acoustic data and wind measuring instrument are realized.  ...  Construction of Multiple Acoustic Data Analysis Model Based on Deep Belief Network Classification Algorithm Basic Idea of Deep Belief Network Classification Algorithm. e characteristics of the instrument  ... 
doi:10.1155/2021/7955909 fatcat:iv25nsi3zfdgfgpswrygmitnee

Online Social Network Image Classification and Application Based on Deep Learning

Chunde Yang, Yongchao Wang
2017 DEStech Transactions on Engineering and Technology Research  
Apply deep belief networks to online social network image classification, taking Sina microblog as an example of online social network.  ...  In this paper, image classification which combines text features and content features obtained from the deep learning method is proposed.  ...  The deep level feature vector of the image is extracted from bottom-up, and obtain robustness feature vector.  ... 
doi:10.12783/dtetr/iceta2016/6970 fatcat:foyoaxur4rb2lodddst3oempxu

Dermoscopic Image Classification Using Deep Belief Learning Network Architecture

Lubna Farhi, Saadia Mansoor Kazmi, Hassan Imam, Mejdal Alqahtani, Farhan Ur Rehman
2022 Wireless Communications and Mobile Computing  
In this paper, deep belief learning network architecture (DBL) is proposed for medical image classification in a bid to improve the diagnostics of dermal melanoma as an alternative to traditional dermoscopy  ...  The DBL network was then applied to the segmented image for classification.  ...  Acknowledgments The authors extend their appreciation to King Saud University for funding this work through Researchers Supporting Project number RSP2022R426, King Saud University, Riyadh, Saudi Arabia  ... 
doi:10.1155/2022/2415726 doaj:fafbbca0a743452ba53bf93aefd0edda fatcat:vfqixupmprgarpqzb4r6cbnwza

Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

K. Babu, C. Kumar, C. Kannaiyaraju
2022 Intelligent Automation and Soft Computing  
The neural network is highly used in deep learning techniques for classification. Here we use a deep belief network (DBN) for classifying the recognized image.  ...  Then the feature extraction is performed using swarm intelligence-based grey wolf with particle swarm optimization techniques.  ...  Finally, classification is done using deep belief networks (DBN).  ... 
doi:10.32604/iasc.2022.023756 fatcat:rxumzn7gmrcfbn7sptxxu36fnq

Ontology and crow optimization-based deep belief network for privacy preservation of medical data

Rubin Thottupurathu Jose, Associate Professor, Amal Jyothi College of Engineering, Kottayam, Kerala, India, Sojan Lal Poulse, Principal and Professor, Mar-Baselious Institute of Technology and Science, Kerala, India
2021 Maǧallaẗ al-abḥāṯ al-handasiyyaẗ  
Accordingly, this paper proposes a classification method termed as crow optimization-based deep belief neural network (CS-DBN) to preserve the privacy of confidential medical data automatically.  ...  Finally, the classification is performed using the deep belief network (DBN), which is trained using the crow search algorithm (CSA).  ...  CSA algorithm 1 Input: Solution vector Update crow memory Stop Architecture of Deep Belief Network: CSA trains the DBN in order to find the weights of the DBN so as to classify the medical data of a  ... 
doi:10.36909/jer.v9i1.9037 fatcat:67y2l4tltnge5g7havvdnj74li
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