Ashima Aggarwal

Work place: Department of Computer Science &Engineering, Lovely Professional University, Phagwara, Punjab (India)-144411

E-mail: ashimasagitarius@gmail.com

Website:

Research Interests: Computer systems and computational processes, Neural Networks, Computer Architecture and Organization, Data Structures and Algorithms

Biography

Ashima Aggarwal, M.tech from Lovely Professional University, Punjab, India, B.Tech (CSE). Area of Interests are Fuzzy logic, Data structures, Real Time Systems and Neural Networks.

Author Articles
Design and Implementation of Fuzzy Rule Based Expert System for Employees Performance Appraisal in IT Organizations

By Ashima Aggarwal Gour Sundar Mitra Thakur

DOI: https://doi.org/10.5815/ijisa.2014.08.09, Pub. Date: 8 Jul. 2014

Performance Appraisal of employees plays a very critical role towards the growth of any organization. It has always been a tough task for any industry or organization as there is no unanimous scientific modus operandi for that. Performance Appraisal system is used to assess the capabilities and productiveness of the employees. In assessing employee performance, performance appraisal commonly includes assigning numerical values or linguistic labels to employees performance. However, the employee performance appraisal may include judgments which are based on imprecise data particularly when one employee tries to interpret another employee’s performance. Thus, the values assigned by the appraiser are only approximations and there is inherent vagueness in the evaluation. By fuzzy logic perspective, the performance of the appraisee includes the evaluation of his/her work ability, skills and adaptability which are absolutely fuzzy concepts that needs to be define in fuzzy terms. Hence, fuzzy approach can be used to examine these imprecise and uncertainty information. Consequently, the performance appraisal of employees can be accomplished by fuzzy logic approach and different defuzzification techniques are applied to rank the employees according to their performance, which shows inconsequential deviation in the rankings and hence proves the robustness of the system.

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