Non-iterative T–S fuzzy modeling with random hidden-layer structure for BFG pipeline pressure prediction

Lv, Zheng; Zhao, Jun; Zhai, Yanwei; Wang, Wei

doi:10.1016/j.conengprac.2018.04.002

Cited by 15 publications

(2 citation statements)

References 24 publications

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“…where p i,j ðtÞ represents the displacement characteristic of fuzzy medical computer vision image at T time, sp i,j ðtÞ represents the exchange fitting function, ΔpðtÞ represents the reference value of standard image, and z i ðtÞ represents the feature output of similarity image. The visual feature quantity of fuzzy medical computer vision image is extracted, based on which the machine learning and optimization are conducted [26][27][28][29][30][31][32]. The schematic diagram of extraction results is shown in Figure 1.…”

Section: Optimization Of Image Information Recoverymentioning

confidence: 99%

Fuzzy Medical Computer Vision Image Restoration and Visual Application

Tang

Qiu

Gao

2022

Computational and Mathematical Methods in Medicine

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In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration.

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Section: Optimization Of Image Information Recoverymentioning

confidence: 99%

Fuzzy Medical Computer Vision Image Restoration and Visual Application

Tang

Qiu

Gao

2022

Computational and Mathematical Methods in Medicine

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“…The random structure of hidden layer used in constructing the model increases the efficacy of the model, which is proved with increased accuracy of the model. The randomization of weight input with the hidden layers without affecting convergence would increase the fit of model with the process 17 . The number of hidden layers required for the model plays a vital role in optimization of the model to act as a good efficient model.…”

Section: Feed Forward Multilayer Perceptron Modelmentioning

confidence: 99%

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

Mahalingam

Kabra

Singh

2018

Preprint

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Leishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia and Latin America. It is associated with malnutrition, weak immune system of people and their housing locality. At present, it is diagnosed by microscopic identification, molecular and biochemical characterisation or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing Leishmaniasis using cognitive computing. The Genetic datasets of leishmaniasis are collected from Gene Expression Omnibus database and it's then processed. The algorithm for training and developing a model, based on the data is prepared and coded using python. The algorithm and their corresponding datasets are integrated using TensorFlow dataframe. A feed forward Artificial Neural Network trained model with multi-layer perceptron is developed as a diagnosing model for Leishmaniasis, using genetic dataset. It is developed using recurrent neural network. The cognitive model of the trained network is interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system is measured using the accuracy, loss and error of the system. This integrated system of the leishmaniasis genetic dataset and neural network proved to be the good choice for diagnosis with higher accuracy and lower error. Through this approach, all records of the data are effectively incorporated into the system. The experimental results of feed forward multilayer perceptron model after normalization; mean square error (219.84), loss function (1.94) and accuracy (85.71%) of the model, shows good fit of model with the process and it could possibly serve as a better solution for diagnosing Leishmaniasis in future, using genetic datasets.The code is available in Github repository: https://github.com/shailzasingh/Machine-Learning-code-for-analyzing-genetic-dataset-in-Leishmaniasis

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