American Journal of Business and Operations Research

In today’s paced and data centric world the integration of Artificial Intelligence (AI) technologies has become a game changer, in industries. However effectively utilizing AI to make informed decisions is still a task due to the complexities of datasets and the need for predictive models. This study aims to explore and evaluate Machine Learning (ML) classifiers such as Gradient Boosting, Light Gradient Boosting Machine (LightGBM) Extreme Gradient Boosting (XGBoost) and stacking classifiers within decision making scenarios. The objective is to assess their effectiveness in handling datasets and gain insights into their performance metrics for improving decision making processes. Comparative analysis of these classifiers reveals strengths and capabilities when applied in decision making contexts. The experimental findings highlight the potential of classifiers Gradient Boosting, in optimizing decision making even in complex situations.

The Internet of Things (IoT) is a crucial and rapidly developing technology that is employed in a wide variety of essential real-life apps, including those in which it may be used to enhance decision making. However, the presence of a number of potential sources of uncertainty inside the IoT infrastructure might influence decision makers to take decisions that are not suitable. In the work that is being presented here, the primary emphasis is placed on the development of a risk-based decision-making methodology for the Internet of Things (IoT), with the goal of efficiently managing uncertainties and incorporating prior domain knowledge into the decision-making process. The creation of the framework is based on a systematic literature analysis that examines the risks and reasons of ambiguity in decision-making systems related to the internet of things (IoT). IoT risk contains many and conflicting criteria so, the concept of multi-criteria decision making is introduced to overcome this problem. The purpose of this article is to provide the comprehension survey of the decision making based on IoT risk evaluation.

COVID-19, a coronavirus pandemic unlike any seen before, is in a state of flux over the planet. Since the COVID-19 pandemic now poses a serious danger to all nations, it is critical that policymakers find the most effective response possible. The coronavirus is difficult to eradicate, however the COVID-19 vaccination may help with that. Everyone is wondering which vaccination would be best for them. Multi-criteria decision-making (MCDM) is an excellent method for assessing this maze. As a result, we have suggested a cutting-edge MCDM method for choosing COVID-19 vaccinations. The primary objective of this work is to deliver a technique for MCDM. In this investigation, we present a unique hybrid model that combines the strengths of the neutrosophic Analytic Hierarchy Process (N-AHP) and the neutrosophic VIKOR technique. Using the N-AHP, we can quantify the importance of the criterion, and using the N-VIKOR method, we can prioritize our options for interventions.

The study aimed to identify the effect of job satisfaction on the performance of employees in the Ministry of Finance in the Red Sea State, Sudan. The study relied on the descriptive analytical approach. The study was applied to a random sample of employees in the Ministry of Finance in the Red Sea State, Sudan, and the sample size was (158) male and female employees. The results of the study showed that the level of job satisfaction and the performance level of employees in the Ministry of Finance is medium. There was no statistically significant difference in the responses of the study sample members regarding job satisfaction and the performance of ministry employees due to personal variables. The results also showed an impact of the dimensions of job satisfaction (salaries and incentives, job stability, and working conditions) on the performance of employees in the Red Sea State's Ministry of Finance. The study reached several recommendations, the most important of which was working to raise the level of job satisfaction in the Ministry of Finance in the Red Sea State.

This paper introduce a multi- criteria decision making (MCDM) perfect to assess business risk in electricity retail company to decrease risk loss and mange risks of business. The evaluation of business risk in electricity company included many conflicting criteria such as risk of political, risk of economic, and risk of market. So, this paper presented an Evaluation based on distance from average solution (EDAS) MCDM method to compute the weights of these criteria and rank the alternatives. Distances between each option and the mean answer on each criteria form the basis of EDAS. It expedites the decision-making process by streamlining the computation of distances to the deal solution. But in this evaluation, there are many imperfect and unclear data. So, the neutrosophic sets is presented to overcome this vague information. The interval valued neutrosophic sets (IVNSs) is a type of neutrosophic sets is presented in this work. 

Businesses all across the globe are adopting sustainable supply chain practises in an effort to lessen their impact on the environment. Towards reaching that aim, suppliers in healthcare have a crucial role in creating a sustainable supply chain. One of the difficulties in achieving sustainability in supplier selection is the use of competing criteria. The use of several factors in making decisions is essential for sustainable supplier selection (MCDM). This study introduce the Multi-Attributive Border Approximation area Comparison (MABAC) methodology to select best supplier in healthcare industry.  The standards and substitutions are collected from the previous works. The weights of criteria are computed, then the alternatives are ranked by the MABAC method. MABAC is a common MCDM methodology for ordering substitutions. The criteria and substitutions are included the vague and incomplete data and information, so the nutrosophic environment is used to overcome uncertainty. The single valued neutrosophic numbers are used in the computations in this work.