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Alexander Shknevsky, Yuval Shahar and Robert Moskovitch
We propose a new pruning constraint when mining frequent temporal patterns to be used as classification and prediction features, the Semantic Adjacency Criterion [SAC], which filters out temporal patterns that contain potentially semantically contradicto...
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Alexander Sboev, Roman Rybka, Dmitry Kunitsyn, Alexey Serenko, Vyacheslav Ilyin and Vadim Putrolaynen
In this paper, we demonstrate that fixed-weight layers generated from random distribution or logistic functions can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher?s Iris, Wisco...
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Armin Rashidi Nasab and Hazem Elzarka
The deterioration of a bridge?s deck endangers its safety and serviceability. Ohio has approximately 45,000 bridges that need to be monitored to ensure their structural integrity. Adequate prediction of the deterioration of bridges at an early stage is c...
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Shilpa Gite, Shruti Patil, Deepak Dharrao, Madhuri Yadav, Sneha Basak, Arundarasi Rajendran and Ketan Kotecha
Feature selection and feature extraction have always been of utmost importance owing to their capability to remove redundant and irrelevant features, reduce the vector space size, control the computational time, and improve performance for more accurate ...
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Yingze Song, Degang Yang, Weicheng Wu, Xin Zhang, Jie Zhou, Zhaoxu Tian, Chencan Wang and Yingxu Song
Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research. However, the problem of sample imbalance in landslide susceptibility assessment, where landslide samp...
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