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Application of Clustering Analysis in Brain Gene Data Based on Deep Learning
2019
IEEE Access
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Then, a clustering model based on deep learning is proposed, and a clustering algorithm is implemented by using deep belief network (DBN) and fuzzy c-means algorithm (FCM). ...
and more convenient for clustering high-dimensional data. ...
FUZZY CLUSTERING MODEL BASED ON DAN Gene expression data has the characteristics of high dimensionality, high complexity and difficult to identify. ...
doi:10.1109/access.2018.2886425
fatcat:23bdpd22m5ezjheilmhpmsoqby
Soft computing in remote sensing image processing
2016
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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The third contribution entitled "Difference Representation Learning Using Stacked Restricted Boltzmann Machines for Change Detection in SAR Images" established a deep neural network using stacked Restricted ...
The seventh paper entitled "Adaptive Pixel Unmixing Based on a Fuzzy ARTMAP Neural Network with Selective Endmembers" applied a new selective endmember spectral mixture model to fuzzy ARTMAP neural network-based ...
doi:10.1007/s00500-016-2368-7
fatcat:x2bofaqnwfd7fmx7vunh6b7lue
Population based Optimized and Condensed Fuzzy Deep Belief Network for Credit Card Fraudulent Detection
2020
International Journal of Advanced Computer Science and Applications
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The fuzzy deep belief network greatly handles the complex pattern of credit card transactions with its deep knowledge and stacked restricted Boltzmann machine the pattern of dataset is analyzed. ...
Keywords-Credit card fraudulent; uncertainty; intuitionistic fuzzy; fuzzy deep belief network; sea turtle foraging Vaishnavi et al. [4] in their work anticipated a novel approach for fraud detection on ...
Wish this article will be beneficial for future scholars. ...
doi:10.14569/ijacsa.2020.0110970
fatcat:kdexmkkiw5c4pfz2sstx2gxpe4
Unsupervised Change Detection of Multispectral Imagery Using Multi Level Fuzzy Based Deep Representation
2017
Journal of Asian Scientific Research
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It is described based on the feature vector of each data set and their interconnecting vectors [5] . ...
This paper proposes a robust methodology for the analysis of multispectral imagery using Deep belief network (DBN) and Fuzzy interference system (FIS). ...
Deep learning is a branch of machine learning which depends on a set of algorithms that aims for high level intellections in data. ...
doi:10.18488/journal.2.2017.76.206.213
fatcat:sja4sqyqgfarvh4aecejk3npeq
Deep Learning and Fuzzy Rule-Based Hybrid Fusion Model for Data Classification
2019
International journal of recent technology and engineering
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The main intension of this research is to design and develop a data classification strategy based on hybrid fusion model using the deep learning approach, Adaptive Lion Fuzzy System (ALFS), and Robust ...
In the second phase, the data is classified using Robust Grey wolf based Sine Cosine Algorithm based Fuzzy System (RGSCA-FS), which is used for selecting the optimal fuzzy rules. ...
[4] developed a Fuzzy-Deep Belief Network (Fuzzy DBN) for pre-training Fuzzy Restricted Boltzmann Machines (FRBMs) in a layer-wise manner and stacking them one on top of another. ...
doi:10.35940/ijrte.b2304.078219
fatcat:74gupeyadnginmssjk3jbickvi
Anticipating Railway Operation Disruption Events Based on the Analysis of Discrete-Event Diagnostic Data
2013
Chemical Engineering Transactions
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This paper describes our research applying Echo-State Networks (ESN) in combination with Restricted Boltzmann Machines (RBM) and fuzzy logic to predict potential railway network disruptions based on discrete-event ...
Approaches to predict component failures and remaining useful life are usually based on continuously measured diagnostic signal data. The use of event-based diagnostic data is limited. ...
The authors would like to thank ALSTOM Transportation for providing the data for this research project. ...
doi:10.3303/cet1333120
doaj:fd1d1af797c6494faf0f09f21e816d4b
fatcat:23nrcmml45eajaryjstz447c2q
Facial Expression Recognition Using Deep Neural Network and Decision Fusion
2016
Innovative Computing Information and Control Express Letters, Part B: Applications
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Second, the Local Binary Pattern features are extracted and a deep neural network is trained by Restricted Boltzmann Machine. ...
Third, the output of the neural network is used for semantic inference, and fuzzy inference system is adopted to implement the high level decision system. ...
The variances in local visual features are handled by DBN, and the learning procedure relies on the successful initialization by Restricted Boltzmann Machine. ...
doi:10.24507/icicelb.07.09.2055
fatcat:onu3rgosfzefjeno7gp5uaguem
2020 Index IEEE Transactions on Fuzzy Systems Vol. 28
2020
IEEE transactions on fuzzy systems
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., An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS; TFUZZ June 2020 1062-1072 Weinstein, A., see Veloz, A., TFUZZ Jan. 2020 100-111 ...
., +, TFUZZ Jan. 2020 72-84
Boltzmann machines
A Fuzzy Deep Model Based on Fuzzy Restricted Boltzmann Machines for
High-Dimensional Data Classification. ...
., +, TFUZZ May 2020 806-817
A Fuzzy Deep Model Based on Fuzzy Restricted Boltzmann Machines for
High-Dimensional Data Classification. ...
doi:10.1109/tfuzz.2020.3048828
fatcat:vml5fun6szcqbhpceebk3xfg2u
A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
2020
Shock and Vibration
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Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. ...
However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. ...
Moreover, the restricted Boltzmann machine (RBM) is used to build and train the DBN using a layer-by-layer pretraining algorithm. ...
doi:10.1155/2020/8843759
fatcat:h4zyvhct6nb7lpsj7j5f3yror4
Clinical Characteristics and Mathematical Analysis of Curative Effect of Hemodialysis in Curing Poisoning Caused by Snakebite
2022
Scanning
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In order to explore the clinical characteristics of hemodialysis in curing poisoning from snakebites, a two-classification model of nuclear logistic neural network based on restricted Boltzmann machine ...
The network first performs feature learning through unsupervised training of restricted Boltzmann machines and obtains the initial values of the parameters to be identified, which reduces the influence ...
Conclusion A two-classification model and a multiclassification model of nuclear logistic neural network based on restricted Boltzmann machine are proposed. ...
doi:10.1155/2022/2312972
pmid:35601870
pmcid:PMC9106513
fatcat:n6v7wbnpivecbfoa7nrcvj5jey
Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks
2019
Applied Sciences
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The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. ...
In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). ...
Acknowledgments: The authors would like to thank the anonymous reviewers for their contribution to this paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app9020238
fatcat:incoxpewuvahhgi7upxp2u2goa
Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier
2021
Applied Bionics and Biomechanics
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The efficient Fuzzy Weighted Relevance Vector Machine (FWRVM) classifier model is developed for this achieving this objective. ...
time which is better than the compared Support vector machine (SVM), RVM, and Deep Neural Networks (DNN) classifiers. ...
[20] developed a prediction model to predict the rheological and chemical properties of wheat dough using deep neural network (DNN) where each layer is trained greedily using restricted Boltzmann machine ...
doi:10.1155/2021/6670316
pmid:33727954
pmcid:PMC7935609
fatcat:7d4fvfpug5aazmwlru5hhwi4ja
An improved advertising CTR prediction approach based on the fuzzy deep neural network
2018
PLoS ONE
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Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). ...
In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. ...
A. FRBM and its Learning Algorithm Fuzzy restricted Boltzmann machine (FRBM) is a symmetric neural network with binary nodes that is based on an energy model. ...
doi:10.1371/journal.pone.0190831
pmid:29727443
pmcid:PMC5935396
fatcat:27ssnqmcgrfxpcvt6xtyujhzu4
The Last State of Artificial Intelligence in Project Management
[article]
2020
arXiv
pre-print
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This paper reports on a systematic review of the published studies used to investigate the application of AI in PM. ...
The results indicated that the application of AI in PM was in its early stages and AI models have not applied for multiple PM processes especially in processes groups of project stakeholder management, ...
Unsupervised learning predicts based on unlabeled data, and it is associated with dimensionality reduction and clustering [10, 76] . ...
arXiv:2012.12262v1
fatcat:q3qxxsb6rbailg7leuoputgphy
Power Intelligent Terminal Intrusion Detection Based on Deep Learning and Cloud Computing
2022
Computational Intelligence and Neuroscience
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It analyzes the structure of the power knowledge network and cloud computing through deep learning-based methods and provides a network interference detection model. ...
At the same time, for big data network data retrieval, it retrieves and analyzes data flow quickly and accurately with the help of deep learning of data components. ...
Restricted Boltzmann Machines. Restricted Boltzmann Machine (RBM) was proposed in 1986, which is a new type of stochastic neural network model. ...
doi:10.1155/2022/1415713
pmid:35586098
pmcid:PMC9110159
fatcat:gjggxz4nzrbozdxjofw33e64ly
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