Shahaboddin Shamshirband

TL;DR: The findings suggest that agile requirements engineering as a research context needs additional attention and more empirical results are required to better understand the impact of agile requirements Engineering practices e.g. dealing with non-functional requirements and self-organising teams.

TL;DR: There is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models.

TL;DR: The experimental results show that the proposed model detects all the stages of DR unlike the current methods and performs better compared to state-of-the-art methods on the same Kaggle dataset.

TL;DR: In this article, a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined, which is developed by hybridizing the Support Vector Machines (SVMs) with Firefly Algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration (n¯), maximum temperature (Tmax), and minimum temperature(Tmin) as inputs.

TL;DR: A hybrid approach that involves a sentiment analyzer that includes machine learning and a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naive Bayes and support vector machines (SVM).