Notice Board :

Call for Paper
Vol. 10 Issue 5

Submission Start Date:
May 01, 2018

Acceptence Notification Start:
May 22, 2018

Submission End:
May 25, 2018

Final MenuScript Due:
June 05, 2018

Publication Date:
June 15, 2018
                         Notice Board: Call for PaperVol. 10 Issue 5      Submission Start Date: May 01, 2018      Acceptence Notification Start: May 22, 2018      Submission End: May 25, 2018      Final MenuScript Due: June 05, 2018      Publication Date: June 15, 2018




Volume II Issue II

Author Name
N A A Latif
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 2
Abstract
Content-based image retrieval (CBIR) systems are capable to use query for visually related images by identifying similarity between a query Image and those in the image database. The CBIR Systems can be classified broadly into two classes as Low-level feature based system and High level Semantic feature based system. Image contents are plays significant role for image retrieval. The most common contents are color, texture and shape. An efficient image retrieval system must be based on well-organized image feature extraction. K-means clustering is used to group similar and dissimilar objects in an image database into k disjoint clusters whereas neural network is used as a retrieval engine to measure the overall similarity between the query and the images. Relevance feedback is a query modification technique in the field of content-based image retrieval to improve the retrieval performance.
PaperID
2010/EUSRM/02/02/1006

Author Name
M H Taniwaki
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 2
Abstract
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. “Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself.CBIR extracts low-level features which is inbuilt in the images to present the contents of images. Each image has Visual features such as classified into three main classes: color ,texture and shape features. Color is an important image feature such as used in Content-Based Image Retrieval. K-Means is a clustering method based on the optimization of an overall measure of clustering quality is known for its efficiency in producing accurate results in image retrieval. K-Means technique with all the images in the database. The number of similarity comparisons required depends on the sizes of the clusters and the number of clusters being examined.
PaperID
2010/EUSRM/02/02/1017

Paper Title
Author Name
S E Bardicy, M M El–Rabiei
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 2
Abstract
—IT has become an integral part of everyday business & private life, though new technologies give unprecedented functionality it introduces new risks and environment harder to control. Increased dependency on IT means higher impact when things go wrong. A security breach will have a major impact. All are concerned about the privacy of their information and business losses and hence information security has become a part of IT Governance and corporate governance introductiontional server. The phenomenal growth in e - commerce applications through the Internet in the past few years has led to a genuine need, as well as a sense of urgency, for both small office and home office (SO HO) and corporate users to protect their data transactions through the Internet.
PaperID
2010/EUSRM/02/02/1021

Author Name
O M Folarin
Year Of Publication
2010
Volume and Issue
Volume 2 Issue 2
Abstract
-Danger Theory is presented with particular predominance on analogies in the Artificial Immune Systems world. Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). The idea is that the artificial cells release signals describing their status, e.g. safe signals and danger signals. The various artificial cells use the signals in order to adapt their behavior. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of ‘grounding’ the immune response, i.e. linking it di rectly to the attacker. In this paper, we look at Danger Theory from the perspective of Artificial Immune System practitioners and an overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world.
PaperID
2010/EUSRM/02/02/1027