Open Access Subscription Access
Open Access Subscription Access
Fusion Model for forecasting the Major Indicator of Leading Industries
Imaging plays a central role in the diagnosis and treatment planning of brain neurodegenerative disorders such as Alzheimer's disease, Parkinson and more. Currently, Positron Emission Tomography (PET) is an important tool to identify the functional activities of brain metabolism. An accurate segmentation is critical, especially when the brain functional changes are difficult to assess by clinical examination. Basically, in clinical environment segmentation is done by manually. However, segmentation in PET brain images has many challenges with regards to characteristics of an image, which is solved by preprocessing step. Here, segmentation is performed using the process of clustering. Clustering means the homogeneous intensity particles are grouped together. The intensity value is compared with threshold value. This paper presents a cluster based performance analysis of PET scan image for Alzheimer's disease. Here, Euclidean distance and Chebyshev distance measure is compared to identify the suitable K value for clustering. It has demonstrated its effectiveness by testing it for real world patient data sets. The experiment was compiled in MATLAB environment and an experimental result supports the comparative study.
Earnings per Share, Forecasting, Auto Regressive Moving Average, Adaptive Neuro Fuzzy Inference System
- A. Seetharaman, “Emergence of convertible debentures in Malaysia,” Akauntan national journal Malaysia Inst. Accountants, Sep 1995.
- J. S. Abarbanell and B. J. Bushee, “Fundamental analysis, future earnings and stock prices,” Journal of Accounting Research, vol. 35, no.1, pp. 1-24, 1997.
- E. Zeytinoglu, “The impact of market-based ratio on stock returns: The evidence from insurance sector in turkemore,” International Journal of Finance and Economics, issue 84, pp. 41-48, 2012.
- H. L. Chang, Y. S. Chen, C. W. Su and Y. W. Chang, “The relationship between stock price and EPS: Evidence based on Taiwan panel data,” Economics Bulletin, vol. 30, issue 3, pp. 1-12, 2008.
- J. Jarret, “Forecasting seasonal time series of corporate earnings: A note,” Decision Sciences, vol. 24. no. 4, pp. 888-896, 1990.
- P. T. Elgers, M. H. Lo and D. Murray, “Note on adjustments to analysts’ earnings forecasts based upon systematic cross-sectional components of priorperiod errors,” Management Science, vol. 41, no. 8, pp. 1392-1396, 1995.
- T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116-132, 1985.
- H. Vernieuwe, O.Georgiera, B. D. Baets, V. R. N. Pauwels, N. E. C. Verhoest and F. P. D. Troch, “Comparison of data-driven Takagi-Sugeno models of rainfall-discharge dynamics,” Journal of Hydrology, vol. 302, no. 4, pp. 173-186, 2005.
- J. L. Callen, C. C. Y. Kwan, P. C. Y. Yip and Y. Yuan, “Neural network forecasting of quarterly accounting earnings,” International Journal of Forecasting, vol. 12, no. 4, pp. 475-482, 1996.
- Rono and C.Philip, “Empirical determination of an appropriate earnings per share forecasting model for listed companies: A case of financial institutions in Kenya,” MST-Accounting and Finance, 1998.
- J. Chen and P. Lin, “An intelligent financial ratio selection mechanism for earning forecast,” Journal of the Operations Research, vol. 45, no. 4, pp. 373384, Dec 2002.
- S. Lai and H. Li, “The predictive power of quarterly earnings per share based on time series and artificial intelligence model,” Applied Financial Economic, vol. 16, issue 18, pp. 1375-1388, 2006.
- R. D. Banker and L. Chen, “Predicting earnings using a model based on cost variability and cost stickiness,” The Accounting Review, vol. 81, no. 2, pp. 285-307, 2006.
- G. J. Lobo and R. D. Nair, “Combining Judgmental and Statistical Forecasts: An Application to Earnings Forecasts,” Decision Sciences, vol. 21, issue 22, pp.446-460, 2007.
- A. Rodriguez and J. Trigueros, “Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming,” Estudios de Administration, vol. 15, no. 2, pp. 47-63, 2008.
- C. H. Cheng, J. W. Hsu and S. F. Huang, “Forecasting electronic industry EPS using an Integrated ANFIS model,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3467-3472, 2009.
- Z. L. Chen, C. C. Chiu and C. J. Lai, “ Grey group model forecasting of quarterly accounting earnings,” WSEAS Transactions on Information Science and Applications, vol. 6, issue 8, pp. 1269-1278, Aug 2009.
- Q. Cao and Q. Gan, “Assessing model efficacy in forecasting EPS of Chinese firms using fundamental accounting variables: a comparative study,” Internal Journal Society Systems Science, vol. 2, no. 3, pp. 207-225, 2010.
- L. Y. Wei, C. H. Cheng and H. H. Wu, “ Fusion ANFIS model based on AR for forecasting EPS of leading Industries,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 9, pp. 5445-5458, Sep 2011.
- Ramon Lawrence, “Using Neural Network to Forecast Stock Market Prices,” Course Project, December 1997.
- Amit Choudhary and Rahul Rishi, “Improving the Character Recognition Efficiency of Feed Forward BP Neural Network,” International Journal of Computer Science & Information Technology, vol. 3 , no. 1, pp. 85-96, Feb 2011.
- X. S. Zhou and M. Dong, “Can fuzzy logic make technical analysis 20/20?” Financial Analyst Journal 60, pp. 54-73, 2004.
Abstract Views: 400
PDF Views: 2