Backpropagation Shuffled Leaping Neural Network Implementation in Classification of DDoS Packet Flow Traffic in Data MiningAdaboost random optimal selectionBackpropagation Shuffled Leaping Neural Network (BSLNN)Gabor filterTrace out the source IP addressPreprocessing...
Backpropagation is designed to test for errors working back from output nodes to input nodes. It's an important mathematical tool for improving the accuracy of predictions indata miningand machine learning (ML) processes. Essentially, backpropagation is an algorithm used to quickly calculate derivativ...
Book2022, Predictive Modeling in Biomedical Data Mining and Analysis Aman Kataria, ... Meetali Chauhan Explore book 3 Methodology The dataset used in this work consists of parameters of Fibrinogen and Globulin of one person, which was recorded for 125 days. The Back Propagation neural network is...
The orange curve in the right panel represents the gradient error when the deformation is not infinitesimal and the response of the MNN is nonlinear. The experimental error by using adjoint method is also shown. Source data are provided as a Source Data file. Full size image Figure 1 b ...
The backpropagation algorithm starts with random weights, and the goal is to adjust them to reduce this error until the ANN learns the training data. Standard backpropagation is a gradient descent algorithm in which the network weights are moved along the negative of the gradient of the ...
Proceedings of the 6th International Conference on Advanced Data Mining and Applications (ADMA) 2010, 294–301 (2010). Article Google Scholar Luo, F. Q., Wu, C. M. & Hou, R. Study on hybrid optimisation algorithm in ALM ring path search. Computer Applications and Software 32, 115–...
For the completeness of analysis, all introns in human chromosome 1 (NCBI human genome build 36.2) were extracted, and the final data set comprised 22,448 sequences. 3.2. Weighted UFPs and MFPs The weighted UFPs and MFPs discovered by the proposed SAHS-BP mining system and sensitivity ...
18. Accurate detection and mining of certain dissolved gases in dielectric transformer oil has become the fastest-growing procedure in the diagnosis of transformer faults. Insulation breakdown occurs over time and is affected by heat, humidity, and oxygen concentration. Sophisticated oil conservation ...
41 For the static datasets, we use the direct input encoding used in Wu et al.32 as well as the voting strategy. For the neuromorphic dataset, we use the same data preprocessing strategy used in SpikingJelly.42 For different datasets, we designed three different network structures to adapt ...
Single-objective optimization for four data sets were classified using bat algorithm and MLFFNN in [82,83]. The bat method is based on the echolocation characteristics of the bat. The parameters for each bat are the position xi, the velocity vi, the wavelength λ, the pulse emission r, fre...