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Efficacy of Artificial Neural Network based Decision Support System for Career Counseling
Objectives: This paper presents the use of machine learning technique in order to eliminate the components affecting the human decision making process. To assist decision making in career counseling, an Artificial Intelligence model was implemented using Artificial Neural Network (ANN) in MATLAB for predicting vocational stream of pursuit based on the behavioral characteristic of the beneficiary. Methods/Statistical Analysis: The Differential Aptitude Test (DAT) battery; and Scientific Knowledge and Aptitude Test (SKAT) were used to assess an individual’s specific abilities in different areas. The training data set for the ANN model was procured in the form of normalized scores based on occupational/vocational profiles as given by the authors of DAT battery and SKAT. The trained ANN was tested with normalized stanine scores of 100 tenth class studentsraised through cluster random sampling technique for predicting a vocational stream of pursuit. In order to evaluate its accuracy and efficiency, three techniques of classification were employed. The unclassified data was classified by using Discriminant function analysis, ANN and two classifications were obtained from the trained counselors. Findings: The classification result obtained from the above mentioned techniques were compared and was found that the ANN system and Discriminant function analysis agreed approximately by 91% over all the test cases. The results of the statistical method support the classification made by the ANN. The two counsellors were in agreement with ANN’s classification output by approximately 81%. However, the counsellors disagreed with each other’s prediction approximately by 27% over all the test cases. The experimental results support the hypothesis that the proposed machine learning technique performed better than the prediction made by counsellors. Application/Improvements: In the Indian scenario, the developed machine learning system may be used as a standalone system in places where there is the paucity of counsellorsor can assist in the career decision making provided by human beings.
Artificial Neural Network, Career Counseling, Decision Support System, Differential Aptitude Test Battery, Discriminant Function Analysis, Scientific Aptitude and Knowledge Test.
- Dutta S, Shekhar S. Bond rating: A non-conservative application of neural networks. Proceedings of the 2nd Annual IEEE International Conference on Neural Networks; San Diegi, CA, USA. 1988 Jul. p. 443–50.
- Odom MD, Sharda R. A neural network model for bankruptcy prediction. Proceedings of the IEEE International Joint Conference on Neural Networks; San Diego,CA. 1990 Jun. p. 163–8.
- Brause R, Langsdorf T, Hepp M. Neural data mining for credit card fraud detection. Proceedings of 11th IEEE International Conference on Tools with Artificial Intelligence; Washington: IEEE press. 1999. p. 103.
- Dorronsoro JR, Ginel F, Sanchez C, Cruz CS. Neural fraud detection in credit card operations. IEEE Transactions on Neural Networks. 1997 Jul; 8(4):827–34.
- Ghosh S, Reilly DL. Credit card fraud detection with neural networks. Proceedings of the 27th Annual Hawaii International Conference on System Science; Wailea, HI, USA. 1994 Jan. p. 621–30.
- Ibourk A, Aazzab A. The artificial neural networks for classification: Case of business failures. Journal of Research in Business and Management. 2016 Jul; 4(3):10–15.
- Ozerdem MS, Akpolat V. Classification of bone density with using neural networks. Proceedings of the 15th Signal Processing and Communications Applications; Eskisehir: IEEE. 2007 Jun. p. 1–5.
- Sathe SS, Purandare SM, Pujari PD, Sawant SD. Share Market Prediction using Artificial Neural Network. International Education and Research Journal. 2016 Mar; 2(3):74–5.
- Zhang W, Li C, Ye Y, Li W, Ngai EWT. Dynamic business network analysis for correlated stock price movement prediction. IEEE Intelligent Systems. 2015 Mar-Apr; 30(2):26–33.
- Tino P, Schittenkopf C, Dorffner G. Adaptive information systems and modelling in economics and management science, Report No. :46. Vienna (Austria): Vienna University of Economics and Business Administration; 2000 Mar.
- Sajja PS. Fuzzy artificial neural network decision support system for course selection. Journal of Engineering and Technology. 2006; 19(1):99–102.
- Sajja PS. Type-2 fuzzy user interface for artificial neural network based decision support system for course selection. International Journal of Computing and ICT Research. 2008 Dec; 2(2):96–102.
- Waghmode ML, Jamsandekar. A study of expert system for career selection: Literature review. International Journal of Advanced Research in Computer Science and Software Engineering. 2015; 5(9):779–85.
- Sodhi JS, Dutta M, Aggarwal N. Development and evaluation of ANN based decision support system for selection of vocational stream of pursuit. Proceedings of National Conference on Computing, Communication and Control; India. 2009. p. 5–8.
- Bennett GK, Seashore HG, Wesman AG. Differential aptitude tests: Technical supplement. San Antonio. Tx: Harcourt Assessment; 1984.
- Ganguly D, Ghosh D, Chatterji S, Mukherjee M. An investigation into the validity of a scientific knowledge and aptitude test. Psychometric Research and Service Unit. Calcutta: ISI; 1972.
- Cybenko G. Approximation by superpositions of a sigmoidal function, Mathematical Control. Signals and Systems. 1989 Dec; 2(4):303–14.
- Hornik K, Stinchcombe M, White H. Multilayer feed forward networks are universal approximators. Neural Networks. 1989; 2(5):359–66.
- Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting. 1998 Mar; 14(1):35–62.
- Srinivasan D. Evolving neural network for short-term load forecasting. Neuocomputing. 1998 Dec; 23(1-3):265–76.
- Zhang X. Time series analysis and prediction by neural networks. Optimization Methods and Software. 1994; 4(2):151–70.
- Chester DL. Why two hidden layers are better than one. Proceedings of the International Joint Conference on Neural Network; 1990 Jan. p. 265–8.
- Klimasauskas CC. Applying neural networks. Part 3: Training a neural network. PC-AI; 1991 Jun. p. 20–4.
- Falhman SE. Faster-learning variations of back-propagation: An empirical study. Proceedings of the 1988 Connectionist Models Summer School; 1989. p. 38–51.
- Parker DB. Optimal algorithm for adaptive networks: Second order back propagation, second order direct propagation, and second order Hebbian learning. Proceedings of the IEEE International Conference on Neural Networks; 1987. p. 593–600.
- Battiti R. First- and second-order methods for learning: Between steepest descent and Newton’s method. Neural Computation. 1992 Mar; 4(2):141–66.
- Cottrell M, Girard B, Girard Y, Mangeas M, Muller C. Neural modeling for time series: A statistical stepwise method for weight elimination. IEEE Transactions on Neural Networks. 1995 Nov; 6(6):1355–64.
- Demuth H, Beale M. Neural network toolbox user′s guide. The Math Works Inc; 1998 Jan.
- Klecka WR. Discriminant analysis. Quantitative Applications in the Social Sciences. CA: Sage Publications; 1980.
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