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Application of Clustering Analysis in Brain Gene Data Based on Deep Learning

Yina Suo, Tingwei Liu, Xueyong Jia, Fuxing Yu
2019 IEEE Access  
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

Yanfei Zhong, Zexuan Zhu, Yew Soon Ong
2016 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
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

Jisha M. V, D. Vimal
2020 International Journal of Advanced Computer Science and Applications  
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

S. Gandhimathi Alias Usha, S. Vasuki
2017 Journal of Asian Scientific Research  
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  
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

O. Fink, E. Zio, U. Weidmann
2013 Chemical Engineering Transactions  
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

Xiuyou Wang, Dongqing Xu
2016 Innovative Computing Information and Control Express Letters, Part B: Applications  
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  
., 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

Omar AlShorman, Muhammad Irfan, Nordin Saad, D. Zhen, Noman Haider, Adam Glowacz, Ahmad AlShorman, Yongfang Zhang
2020 Shock and Vibration  
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

Guoliang Huang, Bingbing Chen, Yi Luo, Liming Chen, Shaojie Wu, Shijun Wang, Danilo Pelusi
2022 Scanning  
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

Yanqing Yang, Kangfeng Zheng, Chunhua Wu, Xinxin Niu, Yixian Yang
2019 Applied Sciences  
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

Zainab N. Ali, Iman Askerzade, Saddam Abdulwahab, Mohammed Yahya Alzahrani
2021 Applied Bionics and Biomechanics  
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

Zilong Jiang, Shu Gao, Mingjiang Li, Yong Deng
2018 PLoS ONE  
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]

Mohammad Reza Davahli
2020 arXiv   pre-print
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

Tong Li, Hai Zhao, Yaodong Tao, Donghua Huang, Chao Yang, Shuheng Xu, Jun Ye
2022 Computational Intelligence and Neuroscience  
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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