Estimating the Employment Opportunity of Engineering Students with the Aid of Fuzzy Logic Controller

Estimating the Employment Opportunity of Engineering Students with the Aid of Fuzzy Logic Controller

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Shitalkumar A Rawandale, Vijay N Kalbande, Arvind Bodhe, Ujjwala Rawandale, Ratna Patil
https://doi.org/10.14445/22315381/IJETT-V70I3P236

How to Cite?

Shitalkumar A Rawandale, Vijay N Kalbande, Arvind Bodhe, Ujjwala Rawandale, Ratna Patil, "Estimating the Employment Opportunity of Engineering Students with the Aid of Fuzzy Logic Controller," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 319-326, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P236

Abstract
The point of this exploration work exhibits a classification technique for analyzing engineering student`s employment opportunities. From the given 60 attributes, take 46 attributes as inputs. In view of this input data, we design the fuzzy logic system. This design is utilized for finding the employment opportunity potential score of particular individuals. Based on these 46 attributes, generate the rule as low and high. At that point, we need to take the count of low and high; after analyzing the count, find the output level (low, medium, high). In the outcome, three diverse membership functions such as trapezoidal, Gaussian and triangle have been designed. In this three-membership function, we explore diverse designing and validation points (50-50, 60-40, 70-30 and 80-20). The sensitivity value for the triangle is 76%, the specificity value for the triangle is 93%, and the accuracy value for a triangle is 89%. From this, the triangle membership function is enhanced contrasted with other membership functions.

Keywords
Accuracy, Engineering Employment, Fuzzy Logic System, Sensitivity, Skills.

Reference
[1] Raju Dasgupta, Status of Higher E ducation in Sustainable Development of Rural Areas: A Study on Goreswar Area of Baksa (BTAD) District, International Journal of Humanities & Social Science Studies. 1(4) (2015) 105-110.
[2] Smitha S. Murali, Krishnashree Achuthan and Shyam Diwakar, Comparative Study of Laboratory Education in Disparate Institutes of India, Proc.International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). (2016) 3678-3683.
[3] G.Manchala and Syedaamina Begum, Assessment of Quality in Indian Higher Education (With Special Reference to Engineering Stream), Adarsh Journal of Management Research. 8(1) (2015) 45-54.
[4] Lilia Gourier, Developing Professor Skills to Design the Content of Training, Proceedings of International Conference on Interactive Collaborative Learning (I.C.L.). (2013) 110-111.
[5] Mohamed Sayed and Faris Baker, E-Learning Optimization Using Supervised Artificial Neural-Network, Journal of Software Engineering and Applications. 8 (2015) 26-34.
[6] C. Anuradha and T. Velmurugan, A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance, Indian Journal of Science and Technology. 8(15) (2015) 1-12.
[7] B. Neelima, High-Performance Computing Education in an Indian Engineering Institute, Journal of Parallel and Distributed Computing. 105 (2017) 73-82.
[8] S.V.C.Aiya, Telecommunication Services for Rural India, IETE Journal of Research. 28(12) (2015) 738-744.
[9] Anne G. Short Gianotti and Patrick T. Hurley, Gathering Plants and Fungi along the Urban-Rural Gradient: Uncovering Differences in the Attitudes and Practices among Urban, Suburban, and Rural Land Owners, Land Use Policy. 57 (2016) 555-563.
[10] H.Tamer Hava and Ramazan Erturgut, An Evaluation of Education Relations Together with Technology, Employment and Economic Development Components, Procedia Social and Behavioral Sciences. 2 (2010) 1771-1775.
[11] M Narayana Swamy and M. Hanumanthappa, Predicting Academic Success from Student Enrolment Data using Decision Tree Technique, International Journal of Applied Information Systems. 4(3) (2012) 1-6.
[12] T.Miranda Lakshmi, A.Martin and V.Prasanna Venkatesan, An Analysis of Students Performance Using Genetic Algorithm, Journal of Computer Sciences and Applications. 1(4) (2013) 75-79.
[13] Shaobo Huang and Ning Fang, Predicting Student Academic Performance in an Engineering Dynamics Course: A Comparison of Four Types of Predictive Mathematical Models, Computers & Education. 61 (2013) 133-145.
[14] Saddam Khan, Analyzing Students`data Using a Classification Technique Based on Genetic Algorithm and Fuzzy Logic, Proceedings of International Conference on Computing, Communication and Automation. (2015) 227-232.
[15] Maja Zajec, Davorin Kofjasc and Matjaz Roblek, Eliminating Knowledge Bottlenecks Using Fuzzy Logic, Organizacija. 46(5) (2013) 206-213.
[16] Virginia Barba-Sanchez and Carlos Atienza-Sahuquillo, Entrepreneurial Intention among Engineering Students: The Role of Entrepreneurship Education, European Research on Management and Business Economics. (2017) 1-9.
[17] Hashmia Hamsa, Simi Indiradevi and Jubilant J.Kizhakkethottam, Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm, Procedia Technology. 25 (2016) 326-332.
[18] R Natarajan,The Current Status of Engineering Education in India, Proceedings of International Conference on Interactive Collaborative Learning (I.C.L). (2015) 1-5.
[19] R. Natarajan, Opportunities and Challenges for Engineering Education in India, Journal of Engineering Education Transformations. 27(4) (2014) 29-35.
[20] K.G. Durga Prasad, K.Venkata Subbaiah and G.Padmavathi, Application of Six Sigma Methodology in an Engineering Educational Institution, Int. J. Emerg. Sci. 2(2) (2012) 222-237.
[21] R. Patil, S. Tamane and K. Patil, Self Organising Fuzzy Logic Classifier for Predicting Type-2 Diabetes Mellitus using ACO-ANN, International Journal of Advanced Computer Science and Applications(IJACSA). 11(7) (2020) 348-353.
[22] P. Ratna and T. Sharavari, A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes, International Journal of Electrical and Computer Engineering (IJECE). 8(5) (2018) 3966-3975.
[23] Ratna Patil, Sharvari Tamane, Shitalkumar Adhar Rawandale, Kanishk Patil, A Modified Mayfly-SVM Approach for Early Detection of Type 2 Diabetes Mellitus, International Journal of Electrical and Computer Engineering (IJECE). 12(1) (2022) 524-533.