Published In : IJCSN Journal Volume 7, Issue 3

Date of Publication : June 2018

Pages : 166-181

Figures :28

Tables : 19

 

Anindita Das Bhattacharjee : started her career in industry as a trainee software developer for a year. She has done M. Tech in Computer Science from National Institute of Technology (NIT), Durgapur. She secured a position of First Class Second in M. Tech. Currently she is working in Swami Vivekananda Institute of Science and Technology. She has been teaching for about 10 years in Computer Science. She is an author of a book “Artificial Intelligence and Soft Computing for Beginners” published in 2013 and author of book chapters in the book “Intelligent Analysis for Multimedia Information” published by IGI Global and indexed in Scopus.

Shibashis Mitra : is pursuing B. Tech. Final year student in Computer Science and Engineering in Swami Vivekananda Institute of Science and Technology.

Asiya Amreen Zaman : is pursuing B. Tech. Final year student in Computer Science and Engineering in Swami Vivekananda Institute of Science and Technology. Secured 3rd position in International Olympiad of Science and 2nd position in Olympiad of English 2011, New Delhi.

 

Absorption Co-efficient, Adaptive Neuro- Fuzzy Inference System, Refractive Index, Regression, Semiconductor Doping, Superconductor

In order to develop a robust decision-making model, a Hybrid Model (ANFIS) which incorporates the property of parallelism in computing of a Neural Network along with inference capability of Fuzzy Logic Designer is used in the comparative analysis but FFBPN still outperforms the hybrid model ANFIS due to some of its inherent behavioural properties and also depends upon the nature of preferred input dataset.

 

[1] Abdallah M. D., Mohammed E. A. E., Ahmed PS.A.E., Elgani PR. A., “Optical Properties of Glass and Plastic Doped by Fe”, International Journal of Innovative Science, Engineering & Technology, Vol. 4, Issue 4, April 2017, pp. 174-183. [2] James G. N., Mason A. B., “Protocol to determine accurate absorption co-efficient for iron-containing transferrin”, Analytical Biochemistry, Vol. 382, 15 November,2008, pp. 150. [3] Torres J. H., Welch A. J., MS I. C., Motamedi M., “Tissue Optical Property measurements: Overestimation of absorption co-efficient with spectrophotometric techniques”, American Society for Laser, Surgery and Medicine, Vol. 14, Issue 3, 1994, pp. 249-257. [4] Coronado A. R., Valenzuela A. G., Perez C. S., Barrera R. G. Miola “Measurement of effective refractive index of a turbid colloidal suspension using light refraction”, New Journal of Physics, Vol. 7, 6 April,2005, pp. 1-22. [5] H. A. K., Suresh Y., “Multilayer feed forward neural network to predict the speed of wind”, IEEE Xplore Library, 12 December,2016, pp. 285-290. [6] Munirathinam S., Ramadoss B., “Feed Forward Backpropagation Neural Network Model to Predict Remaining Useful Life Estimation of Ion Implant Tool”, International Journal of Engineering & Technology, Vol. 15, December 2015, pp. 64-72. [7] Asteris P. G., Roussis P. C., Douvika M. G., “Feed- Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials”, MDPI, 9 June,2017, pp. 1-21. [8] Abdulshahed A. M., Longstaff A. P., Fletcher S., “The application of ANFIS prediction models for thermal error compensation on CNC machine tools”, Applied Soft Computing, Vol. 27, Issue , February 2014, pp. 158-168. [9] Boyacioglu M. A., Avci D., “An Adaptive Network- Based Fuzzy Inference System(ANFIS) for the prediction of stock market return: The case of Istanbul Stock Exchange”, Expert Systems with Applications, Vol. 37, Issue 12, December 2010, pp. 7908-7912. [10] Mafolo T., Popoola O. M., “Domestic lighting demand profile prediction using ANFIS and Neural Network”, International Conference on the Domestic Use of Energy (DUE), 2016, pp. 1-8. [11] Sahin M., Erol R., “A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games”, Mathematical and Computational Applications, Vol., Issue, 2017, pp. 22-43. [12] Mayilvaganan M. K., Naidu K. B., “Comparative Study of ANN and ANFIS for the Prediction of Groundwater Level of a Watershed”. Global Journal of Mathematical Sciences: Theory and Practical, Vol. 3, 2011, pp. 299-306. [13] Walia N., Singh H., Sharma A., “ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey”, International Journal of Computer Applications, Vol. 123, Issue 13, August 2015. [14] Reis S. T. D., Pontuschka W. M., Yang J. B., Faria D. L. A., “Properties and Structural Features of Iron Doped BABAL Glasses”, Materials Research, Vol. 6, Issue 3, 2003, pp. 389-394. [15] Mandal S., Hazra S., “Structural and physical properties of Fe2O3-doped lead vanadate glass”, Journal of Materials research, Vol. 15, Issue 1, Jan 2000, pp. 218-221. [16] Baino F., Fiume E., Miola M., Leone F., Onida B., Laviano F., Gerbaldo R., Verne E., “Fe-Doped Sol-Gel Glasses and Glass-Ceramics for Magnetic Hyperthermia”, materials, Vol., Issue, 2018, pp. 2-15. [17] Mardare D., Apostol E., “TiO2 thin films doped by Ce, Nb, Fe, deposited onto ITO/glass substrates”, JOURNAL OF OPTOELECTRONICS AND ADVANCED MATERIALS, Vol. 8, Issue 3, June 2006, pp. 914-916. [18] Hua W. M., Sum W. P., Yew E., Ibrahim Z., Hussin R., “Structural and Luminescence Properties of Borate Glass with Lithium and Strontium Modifier Doped with Transition Metal Ions”, Advanced Materials Research, Vol. 501, 2012, pp. 71-75. [19] Nassiri C., Hadri A., Chafi FZ., Hat A. E., Hassanain N., Rouchdi M., Fares B., Mzerd A., “Structural, Optical and Electrical Properties of Fe Doped SnO2 Prepared by Spray Pyrolysis”, Journal of materials and Environmental Sciences, Vol. 8, Issue 2, 2017, pp. 420- 425. [20] Choudhary B. P., Singh N. B., “Characterization of Fe3+-doped silver phosphate glasses”, Bulletin Of materials Science, Vol. 39, Issue 7, December 2016, pp. 1651–1663. [21] Bora D., Hazarika S., “Optical Properties of Malachite Green Dye Doped SiO2 Glasses: Effect of Transition Metal (Fe-I) Used as a Codopant”, International Journal of Optics, Vol. 2014, 2014, pp. 1-9. [22] Chiad S. S., “Optical Characterization of NiO Doped Fe2O3 thin Films Prepared by Spray Pyrolysis Method”, International Letters of Chemistry, Physics and Astronomy, Vol. 45, 2015, pp. 50-58. [23] Dhingra S., Man P. S., “An Adaptive Neuro Fuzzy Approach for Software Development Time Estimation”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, Issue 5, May 2013, pp. 586-590. [24] Jang J. S. R., “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Trans. Systems, Man, Cybernetics, Vol. 23, Issue (5/6), 1993, pp. 665-685. [25] Jang J. S. R., Sun C. -T., “Neuro-Fuzzy Modeling and Control”, Proceeding of the IEEE, Vol. 83, Issue 3, March 1995, pp. 378-406. [26] Bonissone, Badami, Chiang, Khedkar, Marcelle, Schutten, “Industrial Application of Fuzzy Logic at General Electric”, Proceedings of the IEEE, Vl. 83, Issue 3, March 1995, pp. 450-465. [27] Jang J. S. R., Gulley N., Natick, “The Fuzzy Logic Toolbox for use with MATLAB”, MA: The MathWorks Inc., 1995. [28] Michie, Spiegelhart, Taylor(Eds.), Machine Learning, Neural and Statistical Classification, NY: Ellis Horwood, 1994. [29] Breiman, Friedman, Oslhen, Stone, “Classification and Regression Trees”, CA: Wadsworth and Brooks, 1985. [30] Ali O. A. M., Ali A. Y., Sumait B. S., “Comparison between the Effects of Different Types of Membership Functions on Fuzzy Logic Controller Performance”, International Journal of Emerging Engineering Research and technology, Vol. 3, Issue 3, March 2015, pp. 76-83. [31] Vafakhah M., “Comparison of cokriging and adaptive neuro-fuzzy inference system models for suspended sediment load forecasting”, Arabian Journal of Geosciences, Vol. 6, Issue 8, August 2013, pp. 3003- 3018. [32] Yetilmezsoy K., Ozkaya B., Cakmaskci M., “Artificial Intelligence-Based Prediction Models for Environmental Engineering”, Neural Network world, Vol. 3, Issue 11, 18 April,2010, pp. 193-218. [33] Yetilmezsoy K., Fingas M., Fieldhouse B., “An Adaptive neuro-fuzzy approach for modeling of waterin- oil emulsion formation”, Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol. 389, Issue 1-3, 20 September,2011, pp. 50-62. [34] Azeez D., Ali M. A. M., Gan K.B., Saiboon I., “Comparison of adaptive neuro-fuzzy inference system and artificial neural networks model to categorize patients in the emergency department”, National Center for Biotechnology Information, Vol. 2, Issue 1, 29 August,2013, pp. 2-10. [35] Kumari N., Sunita, Smita, “Comparison of ANNs, Fuzzy Logic and Neuro-Fuzzy Integrated Approach for Diagnosis of Coronary Heart Disease: A Survey”, International Journal of Computer Science and Mobile Computing, Vol 2, Issue 6, June 2013, pp. 216-224.