# Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector +
## 2 Related Work and Contributions
This supported the idea of a hybrid system that, using ANFIS, could give the right recommendation for the employee selection process [18]. ANFIS was in many cases selected in terms of efficiency among neuro fuzzy inference systems as a decision support system for candidate classification in the employment process [19]. The target ANFIS hybrid model was proposed for the objective classification of candidates during the employment process of recruiting personnel in companies in the field of information technology. Optical Character Recognition (OCR) was deployed in order to extract metadata (attributes) available in these documents for subsequent analysis through ANFIS [19]. Employee performance evaluation was also approached in terms of neuro fuzzy logic by [PERSON] [20], providing valuable information regarding the objectivity and transparency of the process. [PERSON] et al. [21] demonstrated the robustness of ANFIS in order to meet the optimum design for magnification mechanisms while a bi-algorithm with pure fuzzy logic and ANFIS was followed for the optimisation of compliant gripper mechanism design [22]. In research conducted by [PERSON] et al. [23], ANFIS was also selected, in order to rank the abnormalities of six-hundred records for ten different classes of cardiac states. Although the accuracy was subject to the size of the training set, it was highlighted that, using a three-input system with a bell-shaped membership function, the adaptive neuro fuzzy inference system showed better results in terms of efficiency compared to a pure Artificial Neural Network (ANN) method. A similar, but quite novel approach combined the use of neuro fuzzy networks with IoT services, in order to evaluate the performance of employees. The activities were tracedusing Radio-Frequency Identifiers (RFIDs) for a total of fifty data records, while ANFIS was deployed for learning and automated decision optimisation. The accuracy rate almost reached 95% with an RMSE of 0.0717, thus leading to a unique proposed model for real-time tracking of jobs, outperforming techniques using ANNs [24]. In similar algorithms of fuzzy inference systems, the factors selected for applicants' evaluation and ranking, using the Mamdani approach in the HR department, included: education, language, job experience, and teamwork ability among many others. The output provided sufficient information regarding the quality of the selected personnel [12]. [PERSON] et al. [25] suggested the ANFIS selection model for the supplier selection problem. Factors that influence the final choice, such as quality and cost, were filtered through the Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to overcome the supplier selection dilemma. The approach showed exceptional performance compared to multiple linear regression, even when new criteria were added to the algorithm [25]. ANFIS was also selected as the main method for recommending employee mutations in manufacturing companies [26], while a quite remarkable approach demonstrated an optimisation method of a queuing system in warehouses. The study referred to the simulation of a queuing system in a logistics company where ANFIS was introduced for modelling the time component of the system as the criterion for optimisation. This could well be incorporated into later stages of the current research since task management and job allocation will be thoroughly discussed in the next sections [27]. A more comprehensive approach that takes full advantage of hesitant fuzzy sets and multi-granulation rough sets was introduced in [28], which provides human resource managers with more flexible access to convey their understanding of the recruitment knowledge base. Additionally, the work in [29] proposed a hybrid model named adjustable HFL MG-DTRSs over two universes, which provides a new perspective by depicting the subjective and hesitant opinions of decision-makers by utilising multiple linguistic terms.