Background: Over the past decade, the potential advantages of employing deep learning models and leveraging auxiliary data in data-driven end-to-end (E2E) frameworks to enhance inventory decision-making have gained recognition. However, current approaches predominantly rely on feed-forward...

Employees are often more likely to use social media for job searching, which sometimes causes withdrawal behaviour. This study proposes an ensemble learning model for predicting the intention to quit (IQ) based on selected features, such as job Involvement (JI), organizational commitment (OC),...

Follow the leader (FTL) algorithm is a newly developed optimization algorithm inspired by a sheep's movement within a flock. FTL has been successfully implemented to solve power prediction problems. However, the probability of falling in local optima is high due to randomness in the step...

Workflow scheduling aims to optimize task allocation and execution in cloud-fog​ computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms...

Passengers must use a set of modes and vehicles to reach their destination in complicated urban structures. Choosing an optimal route is a complicated optimization problem for these passengers. This study proposes a multi-objective optimization algorithm to solve the routing problem in the urban...

Purpose This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011-February 2020) and during the COVID-19 (March 2020-June...