Ontology has become a very vital issue to solve important issues regarding human diseases through data integration of chemical and biological data. Mining such data discovers highly important knowledge about diseases can give an important insight to arrive to new drug targets and assist in personalized medicine. In the current paper, a mining technique for diseases is developed based on integrated ontology and association rule mining algorithm. To perform mining, the semantic web, as a knowledge representation methodology is used to integrate data. In addition, an Ontology Association Rule Mining algorithm (OARM) is developed since existing algorithms cannot be applied because of the ontology nature of data containing several types of relations. To test our performance, prostate cancer data is obtained from NCI, which is related to 279 genes and 89 genes (from prostate cancer pathway).

Ontology has become a very vital issue to solve important issues regarding human diseases through data integration of chemical and biological data. Mining such data discovers highly important knowledge about diseases can give an important insight to arrive to new drug targets and assist in personalized medicine. In the current paper, a mining technique for diseases is developed based on integrated ontology and association rule mining algorithm. To perform mining, the semantic web, as a knowledge representation methodology is used to integrate data. In addition, an Ontology Association Rule Mining algorithm (OARM) is developed since existing algorithms cannot be applied because of the ontology nature of data containing several types of relations. To test our performance, prostate cancer data is obtained from NCI, which is related to 279 genes and 89 genes (from prostate cancer pathway).

Abstract. In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization(ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi- Route AODV Ant routing algorithm (LBMRAA)

Abstract. In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization(ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi- Route AODV Ant routing algorithm (LBMRAA)

Abstract. In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization(ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi- Route AODV Ant routing algorithm (LBMRAA)

Abstract. In this paper, we propose two methods to improve the Ad-Hoc On-Demand Distance-Vector (AODV) protocol. The main goal in the design of the protocol was to reduce the routing overhead, buffer overflow, end-to-end delay and increase the performance. A multi-path routing protocol is proposed which is based on AODV and Ant Colony Optimization(ACO). This protocol is refereed to Multi-Route AODV Ant routing (MRAA). Also we propose a load balancing method that uses all discovered paths simultaneously for transmitting data. In this method, data packets are balanced over discovered paths and energy consumption is distributed across many nodes through network. This protocol is refereed to Load Balanced Multi- Route AODV Ant routing algorithm (LBMRAA)

Abstract. This paper extends the Region-based Deformable Net (RbDN) technique described in [1]to extract the 3D information of all the objects in the scene from a single moving camera. The technique is used for segmenting real-time video sequences captured from a single moving camera. The deformation process tracks the changes in the location and the shape of the segments across the frames. These changes along with the camera displacement are used to estimate the 3D information. The algorithm is completely autonomous and does not require pre-knowledge, training, or assumption about the contents of the sequence. It can handle the difficult case where the motion of the camera is parallel to its optical axis. It can also estimate the distances to objects that are more than 100 m away as long as the camera displacement is over 10% of the expected distance to the objects.

Abstract. This paper introduces a new color image segmentation framework that unifies contour deformation and region-based segmentation. Instead of deforming a single or multiple contours, typically used with classical deformable contour methods, the proposed framework deforms a single planar net that represents the contours of all the objects in the image. The net consists of a group of vertices connected by edges without crossing each other. The connected edges form polygons that represent the segmented regions boundaries. During the deformation process, the algorithm changes the location and the number of vertices as well as the number of polygons to enhance the segmentation fit. The deformation forces for each polygon are generated based upon the average color of the region and the color of the pixels surrounding it. The algorithm is completely autonomous and does not require any user interference, training or preknowledge about the image contents. The experimental results demonstrate the capability of the algorithm to segment color images from arbitrary sources within reasonable time. Furthermore, the compact mathematical representation of the resulting boundaries could be of value for further image analysis.