Notice Board :

Call for Paper
Vol. 10 Issue 8

Submission Start Date:
Aug 01, 2018

Acceptence Notification Start:
Aug 10, 2018

Submission End:
Aug 15, 2018

Final MenuScript Due:
Aug 25, 2018

Publication Date:
Aug 30, 2018
                         Notice Board: Call for PaperVol. 10 Issue 8      Submission Start Date: Aug 01, 2018      Acceptence Notification Start: Aug 10, 2018      Submission End: Aug 15, 2018      Final MenuScript Due: Aug 25, 2018      Publication Date: Aug 30, 2018




Volume II Issue VI

Author Name
B Hajieghrari
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 6
Abstract
Probabilistic Neural Network approach used for mobile adhoc network is more efficient way to estimate the network security. In this paper, we are using an Adhoc On Demand Distance Vector (AODV) protocol based mobile adhoc network. In our Proposed Method we are considering the multiple characteristics of nodes. In this we use all the parameter that is necessary in AODV. For simulation purpose we use the probabilistic neural network approach that gives more efficient and accurate results as comparison to the clustering algorithm in the previous systems was used. The performance of PNN (probabilistic neural network) approach is improved for identifying the particular attack like as wormholes, black holes and selfish.
PaperID
2010/EUSRM/02/06/1005

Author Name
A Hasbullah, M A Hossain
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 6
Abstract
Security of computer network hasbecome main stream in most of networking field,during the past few years. Today, mostdiscussions on computer security is centred on the toolsor techniques used in protecting and defending networks.An Intrusion detection system (IDS) is a security system that monitorscomputer systems and network traffic and analyzes that traffic for possible argumentativeattacks originating from outside the organization and also for system misuse or attacksoriginating from inside the organization. Intrusion detection is the method of identifying unauthorizeduse, misuse, and abuse of computer systems by both systeminsiders and external attackers. Our aim is to discuss thefeasibility of monitoring the traffic of different networks, toanalyze it for providing better security. For this reason, we focuson all the components of intrusion sniffing and response systemlike host and network based IDS. Intrusion detection is theprocess used to identify intrusion. These techniques have beenusually classified into HIDS and NIDS. In thispaper we discuss main functionalities of IDS, characteristics ofIDS and discussing few detection techniques they are anomalybaseddetection, signature based, target monitoring, StealthProbes.
PaperID
2010/EUSRM/02/06/1021

Author Name
J D M Santibañez, F Mekbib
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 6
Abstract
— Intelligent personal knowledge management (IPKM) is application of artificial intelligence in field of personal knowledge management. With the development of the educational idea on the lifelong learning, the request of community to the individual's knowledge structure and knowledge level will be getting higher and higher. It became necessary to apply intelligence to personal knowledge management which leads to Intelligent Personal Knowledge Management. This paper researches on it. This report presents introduction of personal knowledge management and necessary services and need of artificial intelligence for effective and reliable working of these services. These services include search and query, classification, communication, conversion, cataloguing and browsing. This report discussed features required for IPKM tools and can be used as the guidelines for them.
PaperID
2010/EUSRM/02/06/1027

Author Name
S A Aly
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 6
Abstract
It is well known that the classic image compression techniques such as JPEG and MPEG have serious limitations at high compression rate, the decompressed image gets really fuzzy or indistinguishable. To overcome this problem, artificial neural networks ANNs techniques are used. This paper presents a neural network based technique that may be applied to data compression. This paper breaks down large images into smaller windows and eliminates redundant information. Finally, the technique uses a neural network trained by direct solution methods. Conventional techniques such as Huffman coding and the Shannon Fano method, LZ Method, Run Length Method, LZ-77 are discussed as well as more recent methods for the compression of data presents a neural network based technique that may be applied to data compression. The proposed technique and images. Intelligent methods for data compression are reviewed including the use of Back propagation and Kohonen neural networks. The proposed method includes steps to break down large images into smaller windows for Lossless image compression/ decompression processes. Results obtained with proposed technique leads to better compression ratio at the same time preserving the image quality.
PaperID
2010/EUSRM/02/06/1033