Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
IDENTIFICATION OF BOOLEAN NETWORKS USING PREMINED NETWORK TOPOLOGY INFORMATION ABSTRACT Boolean network (BN) has been a powerful tool for system biology and Boolean dynamical system. The successful applications of BN include gene regulatory networks, artificial neural network, social network, multiagent systems, and so on. These applications are based on the identification of the BNs. But it is difficult to identify the BNs directly due to the lack of tools for the logical system. EXISTING SYSTEM To solve this problem, several approaches were proposed within the framework of identifying logical relations directly. For example, a general reverse engineering algorithm used information theoretic principles to reduce the search space. Using the STP, Cheng proved that the data required could be reduced considerably when some special structure properties of BNs are known. To the best of our knowledge, the specific methodology to obtain the structural information from the observed data has not been investigated yet and still remains challenging. DIS ADVANTAGES To identify BNs large amount of data is required. PROPOSED SYSTEM In this brief, a novel approach is proposed to reduce the data requirement in the identification of BNs. Instead of removing fabricated dependence from a fully connected network, our approach mines the topology by adding each true dependence into an empty topology. First, a matching table is created to reflect the dependence relationships among nodes and to provide an approach to reduce the comparisons in the identification. Then, a dynamic locating matching pair (DLMP) approach for extracting location parameters from dynamic time series is given, which can be regarded as a dynamic extension to the matching table. Next, based on the pseudo commutative property of the STP, a position-transform mining (PTM) algorithm is put forward to improve the data utilization. With the determination of the dependence Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor, AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email: [email protected] | www.takeoffprojects. relationships among nodes, the network topology information is obtained. The premined topology information can be used to identify the BNs. ADVANTAGES Reduce the data requirement in the identification of BNs. MODULES Matching Table Dynamic Locating Matching Pairs Position-Transforms Mining SYSTEM REQUIREMENTS H/W System Configuration:Processor - Pentium –III RAM - 256 MB (min) Hard Disk - 20 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA S/W System Configuration:Operating System : Windows95/98/2000/XP Application Server : Tomcat5.0/6.X Front End : HTML, Jsp Scripts : JavaScript. Server side Script : Java Server Pages. Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor, AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email: [email protected] | www.takeoffprojects. Database : MySQL 5.0 Database Connectivity : JDBC Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor, AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email: [email protected] | www.takeoffprojects.