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Abstract: This paper proposes a new adaptive pulse coupled neural network simplified models. The parameters of the model are not artificially outside interference, the decision by the pixel itself. On this basis, this paper focuses on the use of pulse coupled neural network ignition matrix for the impulse noise detection and location, and clearly pointed out that the study emphasis in the future. Moreover, the noise filtering algorithms have been ported to Field Programmable Gate Array (FPGA) hardware platform for implementation compare to the implementation of software. The implementation of FPGA hardware platform possesses high-speed, reconfiguration and other advantages.
Keywords: Pulse-coupled neural network, Synchronous oscillation, Noise testing.
References:
1. Ma rui. The principle of artificial neural network. BeiJing: Machinery Industry Press, 2010.
2. Xin Wang. Research on PCNN Chaotic features and hardware implementation. Lanzhou university, 2010.
3. Brou ssa rd R P, Rogers S K, Oxley M E, et al. Physiologically motivated image fusion for object detection using a pulse-coupled neural network. IEEE Trans. on Neural Networks, 1999, 10(3): 554-563.
4. Burkitt A N. An information-theoretic analysis of the coding of a periodic synaptic input by integrate-and-fire neurous. Neurocomputing, 2002, 55-78.
5. Forgac,R. Mokris,I Linking and activation potential optimization in the pulse coupled neuron network. IEEE International conference on computational cybernetic, 2008, 1: 44-48.
6. Ma Yi-de, Lin Dong-mei, Zhang Bei-du, Liu Qing, Gu Jason. A novel algorithm of image Gaussian noise filtering based on time matrix. 2007 IEEE International Conference on Signal Processing and Communication, 2007: 1499-1502.
7. Liu Qing, Ma Yi-de. A new algorithm for noise reducing of image based on PCNN time matrix. Journal of electronics and information technology, 2008, 30(8): 1869-1873.
8. Xiao Zhi-heng, Shi Jun, Cheng Qiang, Automatic image segmentation algorithm based on PCNN and fuzzy mutual information. 9thIEEE international conference on computer and information technology. 2009,1: 24-31.
9. Ma Yi-de, Zhang Hong-jun. A new image denoising algorithm combined PCNN with Gray-Scale morphology. Journal of Beijing university of posts and telecommunications, 2008, 31(2):109-112.
10. JOHN J L, RITTER D. Observation of periodic waves in a pulse-coupled neural network. Opt. Lett., 1993, 18 (15): 1253-1255.
11. Li Yang-bo, Zhao Bu-hui. Hardware implementation of median filter based on FPGA. Modern electronics technique, 2008, 22: 99-101.
12. Fu Yu-qiang. Research and hardware design of image processing algorithms based on PFGA. NanChang: NanChang University. 2006.
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Abstract: currently, in Albania, there is an increased interest in data mining and educational system, making educational data misning a new growing research community. Data mining is a powerful tool for academic intervention. Through data mining, a university could, for example, predict which students will or will not graduate. The university can use this information to concentrate on those students that are most at risk. In this paper we attempt to use data mining process to help in enhancing the quality of the higher education system by evaluating student data. For this purpose we have collected data from a public faculty in Tirana from 2011 to 2014, covering 1300 students. The classification process is based on the decision tree as a classification method where the generated rules are studied. We aim to build a system that will facilitate the use of generated rules, which will allow students to predict the average grade of the next year in university.
Keywords: student data, data mining, decision trees, higher education, classification process
References:
1. H. Manila, “Data mining: machine learning, statistics and databases” IEEE, 1996.
2. "EducationalDataMining.org", http://www.educationaldatamining.org/ , 2010
3. C. Romero, S. Ventura, Educational Data Mining: a Survey from 1995 to 2005, Expert Systems with Applications, Elsevier, pp. 135-146. 2007
4. H. Witten et al. “Data Mining: Practical machine learning tools and techniques”, 2nd ed. Morgan Kaufmann, San Francisco, 2005.
5. D. Kalles, C. Pierrakeas, Analyzing student performance in distance learning with genetic algorithms and decision trees, Hellenic Open University, Patras, Greece, 2004.
6. S. Hongjie, “Research on Student Learning Result System based on Data Mining”, IJCSNS International Journal of Computer Science and Network Security, Vol.10, No. 4, April 2010.
7. R. S. J. D. Baker, K. Yacef. The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1. 2009
8. A. Nandeshwar, S. Chaudhari. Enrollment prediction models using data mining. 2009 http://nandeshwar.info/wpcontent/uploads/2008/11/DMWVU_Project.pdf
9. P. Cortez, A. Silva. Using data mining to predict secondary school student performance. In the Proceedings of 5th Annual Future Business Technology Conference, Porto, Portugal. 2008
10. I. Witten, E. Frank WEKA Machine Learning Algorithms in Java, Morgan Kaufmann Publishers, 2000
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