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Journal articles on the topic 'Deep Belief Network'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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4

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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9

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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10

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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11

Jaratrotkamjorn, Apichart. "Bimodal Emotion Recognition Using Deep Belief Network." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, no. 1 (2021): 73–81. http://dx.doi.org/10.37936/ecti-cit.2021151.226446.

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The emotions are very important in human daily life. In order to make the machine can recognize the human emotional state, and it can intelligently respond to need for human, which are very important in human-computer interaction. The majority of existing work concentrate on the classification of six basic emotions only. In this research work propose the emotion recognition system through the multimodal approach, which integrated information from both facial and speech expressions. The database has eight basic emotions (neutral, calm, happy, sad, angry, fearful, disgust, and surprised). Emotio

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12

Choi, Hosik. "Forecasting Biogas Production with Deep Belief Network." Korean Data Analysis Society 21, no. 6 (2019): 2801–12. http://dx.doi.org/10.37727/jkdas.2019.21.6.2801.

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13

Jiancheng, Guo, and Wenhui Guo. "Rock Granularity Analysis by Deep Belief Network." MATEC Web of Conferences 139 (2017): 00154. http://dx.doi.org/10.1051/matecconf/201713900154.

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14

Zhang, Pinyi, and Bicong Ci. "Deep belief network for gold price forecasting." Resources Policy 69 (December 2020): 101806. http://dx.doi.org/10.1016/j.resourpol.2020.101806.

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15

WEI, Hua, Chun SHAN, Changzhen HU, Yu ZHANG, and Xiao YU. "Software Defect Prediction via Deep Belief Network." Chinese Journal of Electronics 28, no. 5 (2019): 925–32. http://dx.doi.org/10.1049/cje.2019.06.012.

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Brocardo, Marcelo Luiz, Issa Traore, Isaac Woungang, and Mohammad S. Obaidat. "Authorship verification using deep belief network systems." International Journal of Communication Systems 30, no. 12 (2017): e3259. http://dx.doi.org/10.1002/dac.3259.

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17

Dong, Qinglin, Fangfei Ge, Qiang Ning, et al. "Modeling Hierarchical Brain Networks via Volumetric Sparse Deep Belief Network." IEEE Transactions on Biomedical Engineering 67, no. 6 (2020): 1739–48. http://dx.doi.org/10.1109/tbme.2019.2945231.

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18

Zhang, Shu, Qinglin Dong, Wei Zhang, Heng Huang, Dajiang Zhu, and Tianming Liu. "Discovering hierarchical common brain networks via multimodal deep belief network." Medical Image Analysis 54 (May 2019): 238–52. http://dx.doi.org/10.1016/j.media.2019.03.011.

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19

Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING TECHNOLOGY IN DISEASE DIAGNOSIS." NATURE AND SCIENCE 04, no. 05 (2020): 4–11. http://dx.doi.org/10.36719/2707-1146/05/4-11.

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The rapid development of deep learning technology provides new methods and ideas for assisting physicians in high-precision disease diagnosis. This article reviews the principles and features of deep learning models commonly used in medical disease diagnosis, namely convolutional neural networks, deep belief networks, restricted Boltzmann machines, and recurrent neural network models. Based on several typical diseases, the application of deep learning technology in the field of disease diagnosis is introduced; finally, the future development direction is proposed based on the limitations of cu

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20

Tran, Van Tung, Faisal AlThobiani, Tiedo Tinga, Andrew Ball, and Gang Niu. "Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 20 (2017): 3767–80. http://dx.doi.org/10.1177/0954406217740929.

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In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified fuzzy ARTMAP (SFAM) for fault classification. In the pre-training procedure, an algorithm for selecting local receptive fields is used to group the most similar features into the receptive fields of which top values are the units of each layer, and then restricted Boltzmann machine is applied to these

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21

Dewi, Irma Amelia, and Muhammad Aldi Rizqullah. "Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization." E3S Web of Conferences 484 (2024): 02001. http://dx.doi.org/10.1051/e3sconf/202448402001.

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Deep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data. However, Deep Belief Network has a drawback of being sensitive to hyperparameters. DBN has several hyperparameters that need to be chosen appropriately for the network to function effectively. Poor hyperparameter choices can lead to unstable training or poor performance. Therefore, the Particle Swarm Optimization algorithm is used to search for the best hyperparameters, which can lead to stable trainin

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22

Hasan, Asil, and Bagherzadeh Jamshid. "Proposing a new method of image classification based on the AdaBoost deep belief network hybrid method." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 5 (2019): 2650–58. https://doi.org/10.12928/TELKOMNIKA.v17i5.11797.

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Image classification has different applications. Up to now, various algorithms have been presented for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate is an issue which many researches have been carried out about it. This research intends to optimize the problem with hybrid methods and deep learning. The hybrid methods were developed to improve the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative probabilistic modelwith multiple layers of latent variables and is used to solve the unlabe

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23

Rajadnya, Prof Kirti. "Speech Recognition using Deep Neural Network Neural (DNN) and Deep Belief Network (DBN)." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 1543–48. http://dx.doi.org/10.22214/ijraset.2020.5359.

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24

Cao, Man, Yajun Wang, Jinning Liu, Zhiyong Yin, Xin Guo, and Xiaokun Ren. "Day Ahead Electricity Price Forecasting Based on the Deep Belief Network." Wireless Communications and Mobile Computing 2022 (September 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/3960597.

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With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in

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25

Liu, Xiaoling. "Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis." Computational Intelligence and Neuroscience 2021 (July 13, 2021): 1–10. http://dx.doi.org/10.1155/2021/5609885.

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Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English featur

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26

Joshila Grace, L. K., K. Rahul, and P. S. Sidharth. "An Efficient Action Detection Model Using Deep Belief Networks." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3232–36. http://dx.doi.org/10.1166/jctn.2019.8168.

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Computer Vision and image processing have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Action detection and Pattern Recognition. Action recognition is a new advancement of pattern recognition approaches where the actions performed by any action or living being is tracked and monitored. Action recognition still encounters some challenges that needs to be looked upon and perform recognize the actions is a very minimal time. Networks like SVM and Neural Networks are used to train the network in such a way they

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27

Bello, Rotimi-Williams, Abdullah Zawawi Hj Talib, and Ahmad Sufril Azlan Bin Mohamed. "Deep Learning-Based Architectures for Recognition of Cow Using Cow Nose Image Pattern." Gazi University Journal of Science 33, no. 3 (2020): 831–44. https://doi.org/10.35378/gujs.605631.

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  Abstract  Stacked denoising auto-encoder and deep belief network are proposed as methods of deep learning for cow nose image texture feature extraction, and for learning the extracted features for better representation. While stacked denoising auto-encoder is applied for encoding and decoding of the extracted features, a deep belief network is applied for learning the extracted features and representing the cow nose image in feature space. Stacked denoising auto-encoder and deep belief network help in animal biometrics. Biometrics emanated from computer vision and pattern recogniti

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28

Zhang, Ying, Jinchen Ji, and Bo Ma. "Reciprocating compressor fault diagnosis using an optimized convolutional deep belief network." Journal of Vibration and Control 26, no. 17-18 (2020): 1538–48. http://dx.doi.org/10.1177/1077546319900115.

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This article proposes an optimized convolutional deep belief network for fault diagnosis of reciprocating compressors. Sparse filtering is first used to compress raw signal into compact time series by refining the most representative information and to reduce the computational burden. Then, the proposed convolutional deep belief network is adopted to learn the unsupervised features of the compressed signal without the need of feature extraction by human effort. To improve the generalization ability of the network, an optimized probabilistic pooling out is proposed in this article to replace th

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OYEDOTUN, Oyebade, and Adnan KHASHMAN. "Iris nevus diagnosis: convolutional neural network and deep belief network." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 25 (2017): 1106–15. http://dx.doi.org/10.3906/elk-1507-190.

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30

Dong, Hongzheng, and Chao Guo. "An online social network image retrieval using deep belief network." International Journal of Web Based Communities 19, no. 3 (2023): 1. http://dx.doi.org/10.1504/ijwbc.2023.10049535.

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31

Guo, Chao, and Hongzheng Dong. "An online social network image retrieval using deep belief network." International Journal of Web Based Communities 19, no. 4 (2023): 320–31. http://dx.doi.org/10.1504/ijwbc.2023.134866.

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32

Arum, Aprilisa, and Pramono Pramono. "Penerapan Algoritma Deep Belief Networks (DBNs) Untuk Prediksi Kanker Serviks." DutaCom 17, no. 1 (2023): 50–57. http://dx.doi.org/10.47701/dutacom.v17i1.3790.

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Kanker serviks adalah kanker yang mematikan dan paling umum yang menyerang wanita di seluruh dunia. Prediksi yang tepat mengenai kanker serviks berperan penting dalam upaya pencegahan dan pengobatan yang efektif. Dalam konteks ini, penerapan algoritma Deep Believe Network (DBNs) telah menarik perhatian sebagai metode potensial untuk prediksi kanker serviks. Tujuan penelitian ini untuk dapat mengevaluasi kemampuan algoritma Deep Believe Network (DBNs) dalam memprediksi kemungkinan kanker serviks berdasarkan faktor risiko terkait. Dengan memanfaatkan data klinis yang tersedia, langkah-langkah an

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33

Ye, Qing, and Changhua Liu. "A Multichannel Data Fusion Method Based on Multiple Deep Belief Networks for Intelligent Fault Diagnosis of Main Reducer." Symmetry 12, no. 3 (2020): 483. http://dx.doi.org/10.3390/sym12030483.

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Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and random forest fusion for fault diagnosis. Multiple deep belief networks (MDBNs) are constructed to obtain deep representative features from multiple modalities of multichannel data. Random forest can fuse deep representative features achieved from MDBNs to construct the model of multiple deep belief networks fusion (MDBNF). The proposed method is app

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34

Ramalingappa, Likhitha, Prathibha Ekanthaiah, MD Irfan Ali, and Aswathnarayana Manjunatha. "Reliability analysis in distribution system by deep belief neural network." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 753–61. http://dx.doi.org/10.11591/eei.v13i2.6324.

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Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy o

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35

Zhuang, Xu, Yan Zhu, Qiang Peng, and Faisal Khurshid. "Using deep belief network to demote web spam." Future Generation Computer Systems 118 (May 2021): 94–106. http://dx.doi.org/10.1016/j.future.2020.12.023.

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36

Burlet, Gregory, and Abram Hindle. "Isolated guitar transcription using a deep belief network." PeerJ Computer Science 3 (March 27, 2017): e109. http://dx.doi.org/10.7717/peerj-cs.109.

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Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians. In response, several algorithms have been proposed to automatically analyze and transcribe the notes sounding in an audio recording; however, these algorithms are often general-purpose, attempting to process any number of instruments producing any number of notes sounding simultaneously. This paper presents a polyphonic transcription algorithm tha

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37

Yin, Peng. "Modified Deep Belief Network Model and Its Application." Journal of Physics: Conference Series 1769, no. 1 (2021): 012025. http://dx.doi.org/10.1088/1742-6596/1769/1/012025.

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38

Abid, Azal Minshed. "Multi-Document Text Summarization Using Deep Belief Network." International Journal of Advances in Scientific Research and Engineering 08, no. 08 (2022): 56–65. http://dx.doi.org/10.31695/ijasre.2022.8.8.7.

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Recently, there is a lot of information available on the Internet, which makes it difficult for users to find what they're looking for. Extractive text summarization methods are designed to reduce the amount of text in a document collection by focusing on the most important information and reducing the redundant information. Summarizing documents should not affect the main ideas and the meaning of the original text. This paper proposes a new automatic, generic, and extractive multi-document summarizing model aiming at producing a sufficiently informative summary. The idea of the proposed model

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Lee-Leon, Abigail, Chau Yuen, and Dorien Herremans. "Underwater Acoustic Communication Receiver Using Deep Belief Network." IEEE Transactions on Communications 69, no. 6 (2021): 3698–708. http://dx.doi.org/10.1109/tcomm.2021.3063353.

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40

Xinhua, Jiang, Xue Heru, Zhang Lina, and Zhou Yanqing. "HYPERSPECTRAL DATA FEATURE EXTRACTION USING DEEP BELIEF NETWORK." International Journal on Smart Sensing and Intelligent Systems 9, no. 4 (2016): 1991–2009. http://dx.doi.org/10.21307/ijssis-2017-949.

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Ahmad, Mubashir, Danni Ai, Guiwang Xie, et al. "Deep Belief Network Modeling for Automatic Liver Segmentation." IEEE Access 7 (2019): 20585–95. http://dx.doi.org/10.1109/access.2019.2896961.

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Ruiz Cárdenas, Luis Carlos, Dario Amaya Hurtado, and Robinson Jiménez Moreno. "Predicción de radiación solar mediante deep belief network." Revista Tecnura 20, no. 47 (2016): 39. http://dx.doi.org/10.14483/udistrital.jour.tecnura.2016.1.a03.

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<p>El desarrollo continuo de las herramientas computacionales ofrece la posibilidad de realizar procesos con la capacidad de <em>llevar a cabo</em> actividades con mayor eficiencia, exactitud y precisión. Entre estas herramientas se encuentra la arquitectura neuronal, Deep Belief Network (DBN), diseñada con el propósito de colaborar en el desarrollo de técnicas de predicción para hallar información que permita estudiar el comportamiento de los fenómenos naturales, como lo es la radiación solar. En el presente trabajo se presentan los resultados obtenidos al manejar la arquite

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Wu, Fei, Zhuhao Wang, Weiming Lu, et al. "Regularized Deep Belief Network for Image Attribute Detection." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 7 (2017): 1464–77. http://dx.doi.org/10.1109/tcsvt.2016.2539604.

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Wang, Haibo, and Xiaojun Bi. "Contractive Slab and Spike Convolutional Deep Belief Network." Neural Processing Letters 49, no. 3 (2018): 1697–722. http://dx.doi.org/10.1007/s11063-018-9897-2.

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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 (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

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Wang, Gongming, Junfei Qiao, Xiaoli Li, Lei Wang, and Xiaolong Qian. "Improved Classification with Semi-supervised Deep Belief Network." IFAC-PapersOnLine 50, no. 1 (2017): 4174–79. http://dx.doi.org/10.1016/j.ifacol.2017.08.807.

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Hassan, Mohammad Mehedi, Md Golam Rabiul Alam, Md Zia Uddin, Shamsul Huda, Ahmad Almogren, and Giancarlo Fortino. "Human emotion recognition using deep belief network architecture." Information Fusion 51 (November 2019): 10–18. http://dx.doi.org/10.1016/j.inffus.2018.10.009.

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48

Mahato, Om Prakash, Uday K. Yadav, Basanta Joshi, and Aman Shakya. "Drug-Target Interaction Prediction Using Deep Belief Network." International Journal of Bioinformatics Research and Applications 18, no. 5 (2022): 1. http://dx.doi.org/10.1504/ijbra.2022.10052907.

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49

Bankar, Vishal Kiran, Vaishnav Nandkishor Patil, Omkar Satishkumar Shirure, Samruddha Satish Dambalkar, and Prof Dheeraj Patil. "Stock Market Predictions through Deep Belief Neural Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4881–89. http://dx.doi.org/10.22214/ijraset.2023.52756.

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Abstract: Owing to the huge potential returns, the proportionate degree of exposure, and the adaptability of the transaction, a big number of people choose to invest their money in stocks. This is due to the fact that stocks come with both of these factors. If investors are able to successfully foresee changes in stock values, they have the opportunity to generate considerable returns from their financial investments. The value of a stock may be affected by a wide variety of variables, including the current state of the market and the macroeconomic environment as a whole, significant events in

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Obaida, Tameem Hameed, Sarah Saadoon Jasim, and Hasan Najim Zugair. "Robust Skin Disease Diagnosis with Deep Belief Network." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 1 (2024): 26–35. http://dx.doi.org/10.46649/fjiece.v3.1.3a.12.4.2024.

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Abstract:

The skin is one of the first lines of defenceagainst environmental influences such as sunlight, bacteriaand germs, which cause various skin diseases. In addition to the bad psychological and physical impact caused by skin disease. So,in recent years,many artificial intelligence (AI) algorithms have appeared that canrecognize Images, through which skin diseases can be diagnosed, avoiding traditional methods that rely on visual examination and self-evaluation based on experience. The paper aims to classify a group of skin diseases according to the type of disease, such as Atopic Dermatitis, Dysh

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