scholarly journals An Automated Implementation of Academic Staff Performance Evaluation System based on Rough Sets Theory

2019 ◽  
Vol 23 ◽  
Author(s):  
Bolanle Ojokoh ◽  
Victor Akinsulire ◽  
Folasade O Isinkaye
Keyword(s):  
Performance Evaluation ◽  
Human Resource ◽  
Rough Set ◽  
Evaluation System ◽  
Rough Set Theory ◽  
Academic Staff ◽  
Evaluation Procedure ◽  
Rough Sets Theory ◽  
Imperfect Data ◽  
Automated Method

The essence of evaluating employees’ performance in any tertiary institution is to realize the goals of the institution by measuring the contribution of each employee. Effective human resource evaluation is paramount to the development of any organization. An automated method is needed to remove the limitations and facilitate the duties of human resource management. In this paper, rough set theory, a mathematical technique that deals with vagueness and uncertainty of imperfect data analysis is adopted for the evaluation of academic staff profile for promotion, grants and other academic purposes. The entire appraisal process of academic staff was translated into a web-based application where every user can fill, edit, update, and submit the annual performance evaluation report form. The indiscernible property of rough set approach is a unique factor in assessing every academic staff under the department and faculty/school by the head of department and dean respectively. With this, the system generates an information table handling all the necessary conditions for promoting academic staff and the corresponding decisions taken. A model for rating publications was proposed to reduce the sentiments involved in manual rating. Reports were generated as output of each evaluation procedure. One hundred (100) dataset of academic staff of the Federal University of Technology, Akure, Nigeria was used in the experiment to evaluate the performance of the system. The results of the system obtained score were compared with the institution standard and it was found that the system scores were above standard, the average precision of the system shows 60% effectiveness which showed that the proposed system is efficient for academic performance evaluation process.

An Analytical Employee Performance Evaluation Approach in Office Automation and Information Systems

2017 ◽  
pp. 324-343
Author(s):  
Maryam Kalhori ◽  
Mohammad Javad Kargar
Keyword(s):  
Performance Evaluation ◽  
Information Systems ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Evaluation System ◽  
Employee Performance ◽  
Office Automation ◽  
Performance Evaluation System ◽  
Evaluation Approach

With the extension of information technology, human resource management has experienced fundamental changes. One of the most important issues in human resource management is performance evaluation. Unlike number of studies in employee performance evaluation, there is a lack for systematic and quantitative approaches. Issues such as incomplete information, subjective and qualitative metrics, and also the difficulty of evaluating the performance are the main problems of this field. Hence, the current study exploits the capabilities of information systems and presents an approach for quantitative and automatic evaluation of employee performance in office automation systems. The results reveal the automatic employee performance evaluation system is a discrete dimension for employee performance evaluation systems.

Feature Extraction from Engine's Data Based on Rough Sets Theory

2011 ◽  
Vol 120 ◽  
pp. 410-413
Author(s):  
Feng Wang ◽  
Li Xin Jia
Keyword(s):  
Feature Extraction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Technical Condition ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Rough Sets Theory ◽  
Sets Theory

The speed signal of engine contains abundant information. This paper introduces rough set theory for feature extraction from engine's speed signals, and proposes a method of mining useful information from a mass of data. The result shows that the discernibility matrix algorithm can be used to reduce attributes in decision table and eliminate unnecessary attributes, efficiently extracted the features for evaluating the technical condition of engine.

Application of Rough Sets Theory in Graduate Enrollment

2014 ◽  
Vol 687-691 ◽  
pp. 1604-1607
Author(s):  
Juan Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Postgraduate Training ◽  
Rough Set Theory ◽  
Postgraduate Education ◽  
Rough Sets Theory ◽  
Graduate Enrollment ◽  
Enrollment Data ◽  
Sets Theory

Enrollment is the first step of the work of postgraduate education, and also a very crucial step. Therefore, in order to successfully carry out the postgraduate training work, be sure to do the enrollment work. In this paper, rough set theory is applied to the enrollment data of one college of a university. Then follow the general steps of data mining to research and analyze the enrollment data. Finally, draw some useful conclusions. These conclusions have a certain significance for graduate enrollment.

Traffic Congestion Identification and Analysis of Urban Road Network Based on Granular Computing

2014 ◽  
Vol 641-642 ◽  
pp. 916-922 ◽  
Author(s):  
Chen Yao ◽  
Jiu Chun Gu ◽  
Qi Yang
Keyword(s):  
Rough Set ◽  
Traffic Congestion ◽  
Evaluation System ◽  
Road Network ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Quotient Space ◽  
Urban Road Network ◽  
Evaluation Indexes ◽  
Urban Road

This paper proposes a generalized model based on the granular computing to recognize and analyze the traffic congestion of urban road network. Using the method of quotient space to reduce the attributes associating with traffic congestion, the identification of traffic congestion evaluation system is established including 3 first class indexes and second class indexes of 11. The weight of evaluation indexes are sorted by value in descending order, which are calculated based on rough set theory. In order to improve the efficiency of traffic congestion identification, the appropriate granular is determined by the model parameter μ. When μ is larger, the identification is more effective and the run time of model is longer conversely. Experiments show when the value of μ is between 0.8 and 0.98, the effect of traffic congestion identification is comprehensive optimal.

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