A novel approach to decision making of Mined Data using Dynamic Snapshot Pattern Recognition Algorithm (DS-PRA)

  • Mahmoud Zaki Iskandarani Al-Zaytoonah University of Jordan
Keywords: Snapshot, Pattern Recognition, classification, Data Mining, Intelligent Systems

Abstract

A new approach to pattern recognition and decision machines profiling is proposed, proved and tested. The technique adopts the Snapshot method dynamically as a function of both organization policy and the organization departments policies. Such policies are associated with  individual products and services provided by the organization with departments policies derived from general organization profile with the organization policy being a function of the various departments profiles.  It is proved through real data the ability of such algorithm to classify, detect and predict policy changes and identify differences between different organizations. Also, such algorithm combines the concepts of general Artificial intelligence through the use of knowledge bases and Neural Networks by utilizing a similar weights matrix.

Author Biography

Mahmoud Zaki Iskandarani, Al-Zaytoonah University of Jordan

B.Eng (Hons), MS.c (Neural Processors), Ph.D (Intelligent Techniques) from The University of Warwick-UK. Worked as Research Fellow at the Advanced Technology Centre at the University. I am  47 years old  British National but now works in Jordan.

 

Department of Computer Science, Faculty if Science and Information Technology. Professor Intelligent Systems & Sensors.

References

. J. Ramon, C. comendant, Open Problem: Learning Dynamic Network Models from a Static Snapshot. 25th Annual Conference on Learning Theory, JMLR: Workshop and Conference Proceedings, 2012. 23: p. 45.1- 45.3.

. Z. Mousavinasab, H. Bahadori, AN OVERVIEW ON DATA MODELS FOR KNOWLEDGE DISCOVERY FROM DATABASES. International Journal of Advanced Research in IT and Engineering, London:2013. 2(7): p.12-20.

. G. Kou, W.Wu, TAn Analytic Hierarchy Model for Classification Algorithms Selection in Credit Risk Analysis. Mathematical Problems in Engineering, 2014. 2014(297563): p. 1-7.

. S. Beniwal, J. Arora, Classification and Feature Selection Techniques in Data Mining. International Journal of Engineering Research & Technology (IJERT), 2012. 1(6): p. 1-6.

. A. Moeinian, S. Baladehi, A. Zolfagharian, Hybrid Genetic Algorithm Using the Solving Open Shop Scheduling. International Journal of Engineering Research and Technology, 2013. 5(2): p. 1-10.

. V. Vasani, R. Gawali, Classification and performance evaluation using data mining algorithms. International Journal of Innovative Research in Science, Engineering and Technology, 2014. 3(3): p. 10453-10458.

. R. Kumar, R. Verma, Classification Algorithms for Data Mining:A Survey. International Journal of Innovations in Engineering and Technology (IJIET), 2012, 1(2): p. 7-14.

. R. Lokeshkumar, P. Sengottuvelan, M. Vina, An Approach for Web Personalization using Relational Based Fuzzy Clustering Ontology Model, Biomedical Engineering, Australian Journal of Basic and Applied Sciences, 2014. 8(2): p. 18-22.

. M. Peker, B. Sen, S. Bayir. Using Artificial Intelligence Techniques for Large Scale SetPartitioning Problems. Procedia Technology, 2012 , 1: p. 44 – 49.

. N. Davuth, K. Sung-Ryul, Classification of Malicious Domain Names using Support Vector Machine and Bi-gram Method, International Journal of Security and Its Applications, 2013, 7(1): p. 51-58.

. H. Yang, S. Fong, G. Sun, R. Wong, A Very Fast Decision Tree Algorithm for Real-Time Data Mining of Imperfect Data Streams in a DistributedWireless Sensor Network. International Journal of Distributed Sensor Networks, 2012, 2012(863545): p.1-16.

. S. Dandu, B. Deekshatulu, P. Chandra, Improved Algorithm for Frequent Item sets Mining Based on Apriori and FP-TreeIEEE. Global Journal of Computer Science and Technology Software & Data Engineering, 2013. 13(2): p. 1-5.

. Y. Yang, Z. Zhi-Hua, A Framework for Modeling Positive Class Expansion with Single Snapshot. Springer-Verlag Berlin Heidelberg, 2008, p. 429-440.

. L. Zhenliang, B. Wang, X. Xiaowei, P. Hannam, Environmental emergency decision support system based on Artificial Neural Network. Safety Science, 2012. 50(2012): p. 150-163.

. S. Gupta, D. Kumar, A. Sharma, DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS. Indian Journal of Computer Science and Engineering (IJCSE), 2011. 2(2): p. 188-195.

. X. Wu, et al., Top 10 algorithms in data mining. Knowl Inf Syst, 2008, 14: p.1–37.

. S. Strohmeier, F. Piazza, Domain driven data mining in human resource management: A review of current research, Expert Systems with Applications, 2013, 40(7):p.2410-2420.

. H. Tsai, Knowledge management vs. data mining: Research trend, forecast and citation approach. Expert Systems with Applications, 2013, 40(8): p. 3160-3173.

Published
2014-07-31