By Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, Shaahin Hessabi
This booklet constitutes the revised chosen papers of the thirteenth foreign CSI machine convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four standard papers offered including sixty eight poster displays have been conscientiously reviewed and chosen from a complete of 426 submissions.
The papers are prepared in topical sections on learning/soft computing, set of rules concept, SoC and NoC, wireless/sensor networks, video processing and comparable subject matters, processor structure, AI/robotics/control, scientific photograph processing, p2p/cluster/grid platforms, cellular advert hoc networks, net, sign processing/speech processing, misc, safeguard, picture processing purposes in addition to VLSI.
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Extra resources for Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers
Table 1. 1 Summery Using neural network model we combine co-occurrence and pattern-based measures. 4 explains this combination in more detail. We present a method to extract patterns that ‘is-a’ relation can be deduced from them automatically from web documents. After extracting the patterns, they must be ranked somehow, so that the best pattern receives the value 1 as its rank. Finding ‘is-a’ relations can be turn into a clasand , our algorithm must detersification problem. For a pair of words such as mine whether a ‘is-a’ relation can be between them or not.
Starting with an empty structure and tracing the node list in order, one adds a parent to the node that increases the score. The number of parents to be added to a single variable may be limited to a predetermined constant for fast inference . K2 finds structures quickly if given a reasonable ordering α. If the number of parents per variable is constrained to a constant upper bound, K2 has worst-case polynomial running time in the number n of variables. , undo the addition of an arc) or consider the joint effects of adding multiple arcs (parents).
1) ACO with a Genetic Restart Approach toward Global Optimization Where τ is pheromone, η , is heuristic function and is equal to , 11 the inverse of the difference between city i and j, N is the set of cities that haven’t been visited by ant k, α and β are parameters which shows the relative importance of pheromone versus heuristic or exploitation versus exploration. Equation (1) shows that ants prefer paths with shorter length and higher amount of pheromone, so they independently generate tours by pre-knowledge of the problem and cooperative informative communication.