SVM

Geospatial and Wavelet-Based Feature Fusion for Advanced RUL Forecasting in Agricultural Machinery

This study extends previous research on Remaining Useful Life (RUL) prediction for agricultural vehicles by utilizing an enriched dataset to overcome earlier limitations in forecasting RUL for electric and hydraulic system components. Influential features have been identified through Pearson correlation and Random Forest feature importance analysis. Discrete Wavelet Transform (DWT) has been applied to extract additional approximation and detail coefficients, enhancing the feature set.

Enhancing flood forecasting accuracy through improved SVM and ANFIS techniques

Extreme rainfall in upstream watersheds often results in the rise of river water levels, leading to severe flood disasters in the downstream catchment.  Therefore, monitoring river water levels and flow is crucial for flood forecasting in early warning systems and disaster risk reduction.

Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis

Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text.  Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score.  However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming.  In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange