Deep learning is widely used to automate processes, improve performance, detect patterns,
and solve problems. Thus, applications of deep learning are limitless. Particle swarm …

In machine learning classification problems, it is overly assumed that training and test data
must be drawn from the same locale and essentially follow the same distribution for optimal …

In today's world, where complexity abounds and challenges are multifaceted, mathematical
modeling and optimization emerge as essential tools for addressing real-life phenomena …

Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable
performance for problem solving. The convergence analysis of the method is still in …

High dimensional data have brobdingnagian number of features, but not all features are
useful. Irrelevant and redundant features may even reduce the classification accuracy …

Particle Swarm Optimization (PSO) is first introduced in the year 1995. It is mostly an applied
populationbased meta-heuristic optimization algorithm. PSO is diversely used in the areas of …

The residential load sector ensures power system stability and effective energy
management. Despite efforts to integrate renewable energy sources and develop …

The purpose of this study is to investigate the relationship between knowledge sharing and
academic performance in the context of quality-assurance related work. It furthermore aims …

Problems in healthcare and medicine are worldwide and multifaceted. This dissertation
explores optimal and efficient designs in global and other important clinical trials with …

The characteristics of a linear Quadratic Regulator (LQR) are hinged upon two parameters
and they are, the state weighting matrix Q and the Control weighting matrix R. In this study …