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Feedforward neural network for solving particular fractional differential equations
Published 2024“…Then, a single hidden layer of FNN based on Chelyshkov polynomials with an extreme learning machine algorithm (SHLFNNCP-ELM) is constructed for solving FDEsC. …”
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A new modern scheme for solving fractal–fractional differential equations based on deep feedforward neural network with multiple hidden layer
Published 2024“…The recent development of knowledge in fractional calculus introduced an advanced superior operator known as fractal–fractional derivative (FFD). …”
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A novel softsign fractional-order controller optimized by an intelligent nature-inspired algorithm for magnetic levitation control
Published 2025“…The performance of the proposed approach was extensively benchmarked against four modern metaheuristic algorithms (greater cane rat algorithm, catch fish optimization algorithm, RIME algorithm and artificial hummingbird algorithm) under identical conditions. …”
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A hybrid technique of deep learning neural networks with finite difference method for higher order fractional Volterra-Fredholm integro-differential equations with φ-Caputo operato...
Published 2025“…Moreover, a new hybrid technique which is the combination of deep learning artificial neural network and finite difference method (FDL-ANN) is developed to approximate the solution of higher order VFIDEs. …”
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Predictive modelling of nanofluids thermophysical properties using machine learning
Published 2021“…This thesis aimed to develop machine learning algorithms to estimate the thermophysical properties of commonly used nanofluids. …”
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Dynamic force-directed graph with weighted nodes for scholar network visualization
Published 2022“…The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. …”
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Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations
Published 2024“…In the second part of the thesis, we developed the Hilfer fractal-fractional derivative definition. …”
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Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider
Published 2011“…The studies show beyond technical algorithms, physical aspects plays a big rule in client server model. …”
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Development of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learning
Published 2023“…The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. …”
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Application of artificial neural network to predict brake specific fuel consumption of retrofitted cng engine
Published 2009“…The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. …”
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Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique
Published 2017“…Using the number of trees between 2,500-3,500, learning rate of 0.01, and interaction depth of 5 were found to be the best setting for developing the ozone boosting model. …”
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Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models
Published 2024“…Finally, an artificial neural network model is developed using the backpropagation learning technique coupled with the gradient descent optimization algorithm. …”
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Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
Published 2020“…Next, the system was successfully developed by implementing the best CNN model as a classifier. …”
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Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization
Published 2022“…Finally, the optimal values are (P = 400 bar, T = 3.38 K 102, Y = 1.064 10ˉ³ mol fraction) using this model.…”
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