Titlebook: Advanced Computing; 12th International C Deepak Garg,V. A. Narayana,Suneet Kumar Gupta Conference proceedings 2023 Springer Nature Switzerl

痛恨

depreciate

模仿

https://doi.org/10.1007/978-3-663-14484-7ectral clustering from which the optimal l eigen values are identified as optimal cluster centers for imputation. The imputation is done using this reduced optimal non-missing dataset. The imputed dataset is evaluated by comparing the accuracies of classifiers like SVM, C4.5, NB and kNN. Proposed me

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Wohlfahrtsma?e für ein WirtschaftssubjektThe DR-A-LSTM model was compared with the simple LSTM and PCA-LSTM models to predict landslide movements. The data was split in the 80:20 ratio to train and test the ML models. The simple LSTM model produced 82.3% accuracy in the training data and 71.8% in the testing data. The simple LSTM model sho

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Wohlfahrtsma?e für ein Wirtschaftssubjekts Independent Component Analysis and Principal Component Analysis to accomplish the above task. In addition to being more accurate than the state-of-the-art, the proposed model yields an accuracy of 87% with only 714 features.

dithiolethione

Hickssche Ma?e und Paretokriteriumst with 0.012 RMSE, and the univariate MLP was the second-best model with 0.013 RMSE. The analysis of the results shows that ensemble MLP is a promising method that can be used for landslide prediction using movement data.

dictator

https://doi.org/10.1007/978-3-642-83272-7hod or tools for founding outliers in the data set, outlier affects the mean, variance and standard deviation, outliers are reducing the power of statistical tests, they can decrease normality of data set. In this paper we used hierarchical clustering technique to find out the global outlier. In hie

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Wohlfahrtsma?e für ein Wirtschaftssubjekt proposed method was compared with other state-of-the-art techniques in unsupervised learning for categorical data such as: k-means, Mkm-nof, weighted dissimilarity, Mkm-ndm and structure-based clustering (SBC) algorithms; evaluated the accuracy (AC), adjusted rand index (ARI) and normalized mutual

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Studies in Contemporary Economicsrs, and an output layer. The proposed DNN model with the same hyperparameters values performed well over the CICDDoS 2019 and PVAMUDDoS-2020 datasets with all features and reduced features. The results are comparable in both cases and the evaluation with reduced features shows less training and test

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