Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

Abstract:

Since the railway transport sector is very important in many countries of the world, we have tried through this research to study the production function of this sector and to indicate the level of productivity under which it operates.

It was found through the estimation and analysis of the production function Kub - Duglas that the railway transport sector in Iraq suffers from a decline in the level of productivity, which was reflected in the deterioration of the level of services provided for the transport of passengers and goods. This led to the loss of the sector of importance in supporting the national economy and the reluctance of most passengers an

The efficiency evaluation of the railway lines performance is done through a set of indicators and criteria, the most important are transport density, the productivity of enrollee, passenger vehicle production, the productivity of freight wagon, and the productivity of locomotives. This study includes an attempt to calculate the most important of these indicators which transport density index from productivity during the four indicators, using artificial neural network technology. Two neural networks software are used in this study, (Simulnet) and (Neuframe), the results of second program has been adopted. Training results and test to the neural network data used in the study, which are obtained from the international in

This paper presents the Taguchi approach for optimization of hardness for  shape memory alloy (Cu-Al-Ni) . The influence of  powder metallurgy parameters on hardness has been investigated. Taguchi technique and ANOVA were used for analysis. Nine experimental runs based on Taguchi’s L9 orthogonal array were performed (OA),for two parameters was study (Pressure and sintering temperature) for three different levels (300 ,500 and 700) MPa ,(700 ,800 and  900)^oC respectively . Main effect, signal-to-noise (S/N) ratio was study, and analysis of variance (ANOVA) using  to investigate the micro-hardness characteristics of the shape memory alloy .after application the result of study shown the hei