# Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector +
## 2 Related Work and Contributions  
The personnel selection and ranking problem have been extensively reported in the recent literature. Compared to the public sector, the private sector has been more concerned with and enthusiastic about adopting a technology-driven human resources function in every organisation, and the focus has been on cost optimisation, system and process efficiency, and the timely performing of business partnering functions as the management objective for securing strategic advantages, in order to secure the survival of each business [6]. Heading towards the era of digital transformation, the public sector cannot remain impassive, and it is imperative that the private sector's philosophy be followed. Social security, insurance, and appropriate working conditions are some of the motivating means in the public sector [7]. The academic level, work knowledge, work skills, work experience, and age were selected among a multitude of skills from a multicriteria selection model for employee selection [8]. In a similar case for personnel selection in IT companies, decision-making, familiarisation with IT technology, as well as professional experience and educational background have been considered as main factors for every proposed decision model [9], while personality traits and foreign languages have been proposed as fundamental criteria for logistics management based on a hybrid AHP TOPSIS approach in order to define the selection criteria weights and rank the alternatives [10]. In a more generic approach, management support, organisational climate, job environment, adaptability, and intrinsic motivation play a vital role in determining employee performance [11], while recommendation letters and IQ tests are considered as crucial inputs for the decisions of every HR department regarding employee admission [12]. The initial approach was performed on the vague and unstructured knowledge of personnel skills and capabilities included the adoption of fuzzy decision-making and the corresponding procedure in order to determine the most-suitable candidate for personnel selection [13]. Following the same pattern, a pure fuzzy model for competency-based employee evaluation and selection was introduced by [PERSON] and [PERSON] (2006) by deploying a comprehensive hierarchical structure, consistent with organisational goals and strategies [14]. However, the lack of a generalised method for determining the weight of each factor in a classification procedure has led to the adoption of neural networks in fuzzy sets, where the importance of each skill is extracted, based on the strength of related links in the trained neural network. The ranking algorithm based on a candidate's CV through an automated process has always been a need of any HR department, and such a proposed scheme could make use of a neuro fuzzy system [15]. The impact of neuro fuzzy systems in business has been documented with extended deployment in finance and marketing. With respect to human resource management, the production of a fair and consistent resume ranking policy has always been the ultimate goal considering also the performance evaluation of the employees of an organisation [16]. The soft skill evaluation of the employment of personnel holding a Ph.D. has been analysed, leading to valuable information regarding strategic decisions by the HR department. The results showed that the neuro fuzzy approach was able to more accurately represent the HR department's reasoning when assessing profiles was required, compared to the Mamdani approach. The system was able to select from a pool of candidates those that better fulfilled the set of predefined HR manager requirements. The outcome was illustrated as a vector representation of profiles with flexibility in adaptations when required [17]. In similar research for the employee selection process in child daycare, the proposed system was able to provide the decision with an error rate of 0.00016 based on both observations and interviews.