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Page Rank Algorithm


Posted by: summer project pal
Created at: Sunday 23rd of January 2011 10:06:22 AM
Last Edited Or Replied at :Tuesday 30th of October 2012 03:34:50 AM
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3 Understanding the PageRank algorithm 4
4 Implementation 7
4.1 Dangling node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.2 Dangling node fix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.3 Damping factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
5 Page Rank Calculation 10
5.1 Iterative method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.2 Algebraic method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6 Advantages and Disadvantages 12
6.1 Advantages . . . . ...................[:=> Show Contents <=:]



rsa algorithm full report


Posted by: computer science technology
Created at: Sunday 24th of January 2010 10:41:31 AM
Last Edited Or Replied at :Friday 23rd of November 2012 12:02:15 AM
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each iteration of Euclidâ„¢s algorithm, A is set equal to the previous value of B and B is set equal to the value of A mod B.
similarly, at each step of extended Euclidâ„¢s algorithm,A3 is set equal to the previous value of B3,and B3 is set equal to the previous value of A3 minus the integer quotient of A3/B3 multiplied by B3.This latter value is simply the remainder of A3 divided by B3, which is A3mod B3.
Note also that if gcd (m, b) =1, then on the final step we would have B3=0 and A3 =1. Therefore, on the preceding step, B3 =1. But B3 =1, then we can say the following:

mB1+b..................[:=> Show Contents <=:]



Application of Genetic Algorithm to Economic Load Dispatch full report


Posted by: computer science technology
Created at: Sunday 24th of January 2010 10:16:38 AM
Last Edited Or Replied at :Thursday 26th of January 2012 11:15:05 PM
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is used. The data used in this paper are obtained from Sheble and Britting are as follows:
F1 = 0.00156 X P12 + 7.92 X P1 + 561 Rs/hr
F2 = 0.00194 X P22 + 7.85 X P2 + 310 Rs/hr
F3 = 0.00482 X P32 + 7.97 X P3 + 78 Rs/hr
0.0000050 0.000005 0.0000075
Bmn = 0.0000050 0.000015 0.0000100
0.0000075 0.000010 0.0000450
The total operating ranges for this example are
100 MW < P1 < 600 MW
100 MW < P2 < 400 MW
50 MW < P3 < 200 MW
The parameters used in GA are as follows
Population size 10 Chromosome length 36 Sub-Chromosome lengths 13,12,11 Crossover probability 0.5
M..................[:=> Show Contents <=:]



Fast Redundant Binary Partial Product Generators for Booth Multiplication


Posted by: electronics seminars
Created at: Saturday 09th of January 2010 06:15:05 AM
Last Edited Or Replied at :Saturday 09th of January 2010 06:15:05 AM
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re A* A and a = 1, when A is negative and
A* A and a = 0, when A is positive.
The sum of two NB partial products A and B can now be
expressed as
A B (A*a) (B*b)
A* B * a b
(A* -B *) -1a b
Using the RB Encoding 2 shown in Table I, the above
equation can be expressed as
A B (A*,B*) -1a b (6)
For different positive and negative numbers A and B, the
values of a and b will be chosen according to Table II. It can
be observed that a and b are nothing but the sign bits of A
and B respectively.
If Z = a + b - 1, Equation 6 can be modified as
A B (A*,B*) Z (7)
where Z can be coded accord..................[:=> Show Contents <=:]



Particle Swarm Optimization Algorithm and Its Application in Engineering Design Optim


Posted by: computer science crazy
Created at: Friday 23rd of October 2009 06:14:34 AM
Last Edited Or Replied at :Thursday 02nd of May 2013 11:58:46 PM
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PSO) method was originally proposed by J.Kennedy as a simulation of social behavior, and it was insitially introduced as an optimization method in 1995 (Eberhart and Kennedy, 1995). The PSO is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms looking for the most fertile feeding location. PSO's foundation is based on the principle that each solution can be represented as a particle (agent) in a swarm. Each agent has a position and velocity vector and each position coordinate represents a parameter value. The underlying rules of cooperation ..................[:=> Show Contents <=:]



Recent Researches on Web Page Ranking


Posted by: computer science crazy
Created at: Friday 23rd of October 2009 05:49:47 AM
Last Edited Or Replied at :Tuesday 30th of October 2012 03:34:45 AM
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dering of search results. Among the existing page ranking algorithms the most important algorithms are Kleinberg's HITS algorithm and Brin & Page's PageRank algorithm. Most of the other algorithms are directly or indirectly based on these two algorithms. The present seminars will give a comprehensive discussion on HITS and PageRank algorithm. The focus will be on recent researches on web page ranking algorithms to compensate the drawbacks of HITS and Pagerank algorithm. Some of the algorithms to be covered include CLEVER, Companion Algorithm, Weighted Page Rank, Confidence based page ranking e..................[:=> Show Contents <=:]



computer science project ideas list


Posted by: computer science crazy
Created at: Tuesday 20th of October 2009 05:10:10 AM
Last Edited Or Replied at :Monday 01st of March 2010 07:27:56 PM
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etection /VC++
Homogenous Network Control and Implementation /Java
XML Data Stores: Emerging Practices /Java
XTC: A Practical Topology Control Algorithm for Ad-Hoc Networks /Java
A near-optimal multicast scheme for mobile ad hoc networks using a hybrid genetic algorithm /Java
Mobile Agents In Distributed Multimedia Database Systems /Java
Wireless Traffic Viewer Using JME JME
JME Mail Client Server JME
Monitoring And Managing The Clusters Using JMX JME
Peer-To-Peer Messaging JME
Mobile Information Provider JME
Mobile Bank WAP
Video Steganography Using Mobile Simulation JME
Network ..................[:=> Show Contents <=:]



ODAM An Optimized Distributed Association Rule Mining Algorithm


Posted by: computer science crazy
Created at: Thursday 17th of September 2009 11:47:48 AM
Last Edited Or Replied at :Monday 23rd of January 2012 12:26:21 AM
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lgorithms must reduce communication costs so that generating global association rules costs less than combining the participating sites' data sets into a centralized site. However, most DARM algorithms don't have an efficient message optimization technique, so they exchange numerous messages during the mining process. We have developed a distributed algorithm, called Optimized Distributed Association Mining, for geographically distributed data sets. ODAM generates support counts of candidate item sets quicker than other DARM algorithms and reduces the size of average transactions, data sets, a..................[:=> Show Contents <=:]



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