Three different classification algorithms were applied to a data set including 143 soil profiles of a 150000- ha area in High Murgia (south Italy) to map soil order classes. The first approach consisted in allocating each node of the interpolation grid to the category with the largest local ...
K. Srivastava, "A statistical significance of differences in classification accuracy of crop types using different classifica- tion algorithms," Geocarto International, vol. 32, no. 2, pp. 206- 224, 2017.Kumar P, Prasad R, Choudhary A, Mishra VN, Gupta DK, Srivastava PK (2016a) A ...
However, ML algorithms may be inappropriate when they encounter imbalanced data. An imbalanced data set is common in medical data. It occurs when there are many more instances of one class (majority class) than the other class (minority class). In such cases, the predictive ability of the cl...
Training and evaluating the ML model with different learning algorithms : Here is the summary of all the algorithm trained and tested : K-Nearest Neighbour seems to perform better on the test data. DAY3:NewsGroup data classification Data obtained fromSKlearn inbuilt dataset ...
Deep learning algorithms have shown exceptional effectiveness in a wide range of supervised and unsupervised learning tasks in a variety of fields, including image processing, computer vision, natural language processing, and speech or voice processing. In this paper, a comprehensive analysis is conducte...
Accurate and precise identification of lithological facies is vital to understand geological variation in a proven reservoir. Four specific different machine learning (ML) classification algorithms are implemented to predict facies on an open dataset in the Panoma gas field in southwest Kansas, USA. The...
The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial
This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. Th
SuraJ,Z,Peters,J.F,Rzasa.W.A comparison of different decision algorithms used in volumetric storm cells classification. Fundamenta lnformaticae . 2002Z. Suraj, J.F. Peters, W. Rzasa, A comparison of different decision algorithms used in volumetric storm cells classification, Fundamenta Informaticae...
Bi-LSTM is an extension of the traditional LSTM, which can improve the performance of the model in sequence classification problems. It consists of two LSTMs. One LSTM was trained by acquiring sequence input data from the front and the other LSTM was trained by acquiring data from the back ...