In several countries, students who fail end-of-high-school high-stakes exams are faced with the choice of retaking them or forgoing postsecondary education. We explore exogenous variation generated by a 2006 policy that imposed a performance threshold for admission into postsecondary education...

This paper presents a type-2 fuzzy expert system (T2-FES) for load-side management, Alternative energy sources management, and electrical energy storage for a smart home. The presented T2-FES makes decisions automatically in a smart home energy management system (EMS), which includes renewable...

This paper presents a novel technique in modern power grids that is to operate the emulation of wind generators and plug-in hybrid electric vehicles (PHEVs) for modifying the difficulties. Despite all their benefits, these two kinds of sources are restricted by some physical constraints thus;...

In a variety of engineering, scientific challenges, mathematics, chemistry, physics, biology, machine learning, deep learning, regression classification, computer science, programming, artificial intelligence, in the military, medical and engineering industries, robotics and smart cars, fuzzy...

Dynamic viscosity of novel generated Copper Oxide (CuO)/Liquid Paraffin nanofluids is obtained experimentally for various temperatures and concentrations. To optimize the empirical process and for cost-efficiency, Feed-Forward Neural Networks (FFNNs) were modeled and compared with Recursive...

A fuzzy goal programming approach is applied in this paper for solving the vendor selection problem with multiple objectives, in which some of the parameters are fuzzy in nature. A vendor selection problem has been formulated as a fuzzy mixed integer goal programming vendor selection problem...

In the past, many researchers have developed intuitionistic fuzzy set (IFS) and clustering based fuzzy time series (FTS) forecasting methods to enhance the accuracy in forecast in the environment of uncertainty and non-determinacy. The current research work offers a computational approach for...

Gradient Boosting Machine (GBM) models are widely used in regression and classification problems and can give effective results. These models are models that use simple basic learners iteratively by weighting them repeatedly. Most of the time, decision stumps are used as the basic learners. In...

Similarity measures have been proven over time for their usefulness in solving real-time problems. Researchers modify and develop new methods to suit different areas of application relative to specific data sets. The existing similarity measures on some application domains gave ineffective...