By Khalid Saeed, Václav Snášel (eds.)
This booklet constitutes the complaints of the thirteenth IFIP TC eight overseas convention on machine info structures and commercial administration, CISIM 2014, held in Ho Chi Minh urban, Vietnam, in November 2014. The 60 paper provided during this quantity have been rigorously reviewed and chosen from ninety eight submissions. they're geared up in topical sections named: algorithms; biometrics and biometrics purposes; information research and data retrieval; business administration and different functions; modelling and optimization; networking; development attractiveness and photograph processing; and diverse facets of machine security.
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Extra resources for Computer Information Systems and Industrial Management: 13th IFIP TC8 International Conference, CISIM 2014, Ho Chi Minh City, Vietnam, November 5-7, 2014. Proceedings
E. DSTM and ASTM with the proposed one, by characterizing soome important designing parameeters. Fig g. 3a. Case 1. T2 waits for T7 to commit A New Contention Management Technique for OFTM 19 F 3b. Case 2. T2 forces T7 to abort Fig. 5 Comparison of the Proposed OFTM with DSTM and ASTM Table 1. Analysis of DSTM, AS STM and the proposed OFTM based on some designing parametters Designing Parameters Synchronization Granularity Conflict Detection Update Strategy Read Visibility Data Organization Data Indirection 4 DSTM ASTM Obsstruction Freeedom Objject Based Eag ger Direect Inviisible Keeeps tran nsactional data and d object data in sepaarate memory stru uctures o indirections Two Obstruction Freedom Object Based Lazy Deferred Invisible Keeps transactional data and object data in separate memory structures Two indirections PROPOSED OFTM Obstruction Freedom Object Based Lazy Deferred Visible Keeps transactional datta and object data iin the same memorry structure One indirection Performance Ev valuation The efficiency and performaance improvement of the proposed method over the existting OFTMs ,  depends a lot upon the level of domination of reads in the set.
World Scientific Publishing Company (2008) 8. : A co-evolving decision tree classification method. Expert Syst. Appl. 34, 18–25 (2008) 9. : The contruction and evaluation of decision trees: A comparison of evolutionary and concept learning methods. W. ) AISB-WS 1997. LNCS, vol. 1305, pp. 147–161. Springer, Heidelberg (1997) 10. : Genetic algorithm based multiple decision tree induction. In: ICONIP 1999. ANZIIS 1999 & ANNES 1999 & ACNN 1999. 6th International Conference on Neural Information Processing.
The average utility upper-bound of itemset X: ubX=∑ ∈ Notation 16. HAUUBI is a set of high average utility upper-bound itemsets. Notation 17. HAUI is a set of high average utility itemsets. 2 The Downward Closure Properties of HAUUBI Theorem 1: If itemset X is a high average utility upper bound itemset, then all its sub-itemsets are also the high average utility upper bound itemsets. Proof: Let r-itemset X with ubX=∑ ∈ itemset of X’ (X⊂X’). Thus, | ubX= ∑ ∈ ubX’=∑ ∈ ubX or X ∈ HAUUBI. 3 | and (r+1)-itemset X’ with X is sub| | and .
Computer Information Systems and Industrial Management: 13th IFIP TC8 International Conference, CISIM 2014, Ho Chi Minh City, Vietnam, November 5-7, 2014. Proceedings by Khalid Saeed, Václav Snášel (eds.)