Search Results - (( using function method algorithm ) OR ( parameter classification based algorithm ))
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Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Second, propose an Optimized Time Sliding Window based Three Colour Marker. Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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Tree-based contrast subspace mining method
Published 2020“…Hence, this thesis presents the optimization of parameters values for the tree-based method by genetic algorithm. …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…Based on the concluding overall analysis, the classification accuracy derived from Sentinel 2 imagery utilizing SVM and RF, Landsat 8 applying SVM donated higher than other methods of classification. …”
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…To overcome such challenges, this study attempts to comprehend and improve the remote sensing technology for rooftop classification using the Worldview-3 (WV-3) image and object-based image analysis (OBIA) method. …”
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Personalized human emotion classification using genetic algorithm
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Analysis Of Personal Protective Equipment Classification Method Using Deep Learning
Published 2022“…Face mask has final accuracy of 95.60%, face shield 94.32%, safety goggle 89.79%, safety helmet 98.90% and lastly safety jacket has 88.45% testing accuracy. Based on the result, CNN algorithm is a good algorithm as the binary classification of PPE achieved high accuracy result.…”
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Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks
Published 2021“…In this study, we propose a learning algorithm for the training of MLP models. Conventionally back-propagation learning algorithm also termed as (BP-MLP) is used. …”
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Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…The ADAM optimizer effectively tackles challenges in continuous parameter optimization by dynamically updating the model's weights and biases, adapting the learning rate for each parameter based on accumulated historical gradient information to achieve more efficient minimization of the loss function during training. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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RMIL/AG: A new class of nonlinear conjugate gradient for training back propagation algorithm
Published 2018“…This paper introduced a new class of efficient second order conjugate gradient (CG) for training BP called Rivaie, Mustafa, Ismail and Leong (RMIL)/AG. The RMIL uses the value of adaptive gain parameter in the activation function to modify the gradient based search direction. …”
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Measuring height of high-voltage transmission poles using unmanned aerial vehicle (UAV) imagery
Published 2017“…The authors propose an UAV approach that utilises a digital surface model and incorporates a stereo matching algorithm based on UAV stereo images. The proposed algorithm was based on a graph-cut (GC) algorithm that measured the disparity map. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…The fitness function used is the correlation function in the SKF algorithm to optimize the cipher image produced using the Lorenz system. …”
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Fault classification in smart distribution network using support vector machine
Published 2023“…Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. …”
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