Nguyen H, Drebenstedt C, Bui XN, Bui DT (2020 Apr) Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res 29(2):691–709 Article Google Scholar Ni J, Zhang K, Lin X, Shen X (201...
A simple diagnostic tool built on existing model-based clustering procedure is proposed. This procedure allows us to check whether there is more than one separate data cloud in the data after cleaning. It also supplies the central locations of the separated moderate-sized clusters, which allows ...
A plethora of different clustering methods has been proposed, including classical and state-of-the-art ones that, according to their nature, are usually categorized into either partitional or agglomerative hierarchical ones. Yet, another dichotomy consists of distinguishing between algorithms that model ...
S15A–D). We used graph-based clustering from standard single-cell workflows17 and applied the clustering algorithm to the top 50 principal components (PCs) calculated on the top 1000 SVGs or HVGs. The results demonstrated that nnSVG and Moran’s I (which take spatial information into ...
Then, based on that, I will introduce a new type of stability notion, which can improve over classical stability notions with respect to generalization behavior in certain situations. Specifically, among different notions of stability, uniform stability is arguably the most popular one, which yields...
{clustering,split_over_h,split_over_k}] [--owt OUTPUT_WRITE_TILES] [--output-sparsity-enabled] optional arguments: -h, --help show this help message and exit -o {convolution,dw_convolution,eltwise,maxpool,avepool,cm_convolution}, --op {convolution,dw_convolution,eltwise,maxpool,avepool,...
Experimental results before preprocessing of the proposed concatenated based CNN model This was done to measure the performance of the proposed model before noise removal, segmentation of datasets, and augmentation, but the dataset size of 256 × 256 must be resized to 128 × 128 after ...
Example showing how to use Kmeans module to do standard Kmeans clustering.attempts = 10 iter = 100 -- Number of iterations bestKm = nil bestLoss = math.huge learningRate = 1 for j=1, attempts do local km = nn.Kmeans(k, dim) km:initKmeansPlus(samples) for i=1, iter do km:...
Example showing how to use Kmeans module to do standard Kmeans clustering.attempts = 10 iter = 100 -- Number of iterations bestKm = nil bestLoss = math.huge learningRate = 1 for j=1, attempts do local km = nn.Kmeans(k, dim) km:initKmeansPlus(samples) for i=1, iter do km:...
DNNL based on the inframetric model In this section, we prove that it is feasible to design an accurate and fast DNNL algorithm for the inframetric model. Without loss of generality, assume that each DNNL step needs to locate another node that is β(β∈(0,1]) times closer to the ...