Volume 5, Issue 6, 2015

By | August 11, 2018

Automatic Optic Disc Localization and Segmentation using Swarm Intelligence

Thresiamma Devasia, Dept. of Computer Science, Assumption College Changanacherry, Kerala , India.
Poulose Jacob, Dept. Computer Science, Cochin University of Science and Technology, Kerala, India.
Tessamma Thomas, Dept. of Electronics , Cochin University of Science And Technology, Kerala, India.

Abstract—The optic disc may be distinguished in eye fundus images as a rounded shape having high intensity values. An efficient localization and segmentation of optic disc in colour retinal images is a significant task in an automated retinal image analysis process. It is a challenging task to detect optic disc in all types of retinal images, that is, normal, healthy images. This paper presents an automated method to locate and segment the optic disc in all types of retinal color fundus images using histogram based particle swarm optimization techniques. The technique is tested on 235 images obtained from publicly available database DRION, and certain images obtained from an ophthalmologist. The performance analysis was done using the ground truth of DRION database. From the scatter plot, it is shown that the ground truth and detected optic disc centers have a high positive correlation. The results of the new method seem promising and useful to clinical work.

Keywords- fundus image; optic disc; particle swarm optimization; retinal images.

GIS as a Job Growth Area for IT Professionals

Timur Mirzoev, Anthony Moore, Brianna Pryzbysz
Information Technology Department, Georgia Southern University, Statesboro, GA USA.
Melissa Taylor, Information Technology Department, Columbus State University, Columbus, GA USA.
John Centeno, Information Technology Department, Armstrong State University, Savannah, GA USA.

Abstract— As more companies look to capitalize on the benefits of geospatial data, Geographic Information Systems provide an area for growth in the Information Technology job sector in the United States. Careers in GIS require geography, cartography, and IT skills. As the industry grows, candidates with these types of skills that are in demand and are needed to advance the geospatial industry forward. This industry is not generally known as a growth area to many IT professionals, and due to misleading job postings, many candidates may not know their skills are in demand. This study evaluates the job postings for four positions in the Geographical Information Systems field: GIS Specialist, GIS Analyst, GIS Database Manager, and GIS Developer. Multiple postings for each of these positions were searched and reviewed using five job posting boards: Monster.com, Indeed.com, CareerBuilder.com, GJC.org, and GISJobs.com. The postings were rated based on weighting criterion and compared to similar posting on other job boards. The results varied greatly from one posting to the next within the same job posting board as well as varying from similar postings on other job board websites. This study highlighted the need for the industry to set standards for education and experience required for each position. Having a standard job description and required job duties across the field will better enable those applying for the jobs ensure they have the proper skillsets needed to better perform the job which they are seeking.

Keywords- Geographic Information Systems; Information Technology; GIS Database Administrator; GIS Developer, GIS Analyst.

The Effect of Combining Different Semantic Relations on Arabic Text Classification

Suhad A. Yousif, Islam Elkabani
Mathematics and Computer Science Department, Beirut Arab University, Lebanon.
Venus W. Samawi, Computer Information System Department, Amman Arab University, Jordan.
Rached Zantout, Electrical and Computer Engineering Department Rafik Hariri University Lebanon.

Abstract —A massive amount of documents are being posted online every minute. The task of document classification requires extensive background work on the content of documents, where keyword-based matching alone may not be sufficient. Much research has been carried out in several languages that has revealed significant results. However, Arabic documents still pose a great challenge due to the nature of Arabic language. Extracting roots or stems from the breakdown of multiple Arabic words and phrases are an important task that must be completed before applying text classification. The research at hand proposes an algorithm for classifying Arabic-Text documents using semantic relations between words based on an Arabic thesaurus, mainly synonyms, hyperonyms and hyponyms. The experiments conducted in this study evaluated the results using F1-Measure and compared them to results obtained via other existing methods, such as utilizing stemmers and part-of-speech taggers, where it indicated an increment of more than 12.6% for the novel method using semantic relation over other methods. Arabic-WordNet was utilized as a thesaurus for indicating possible relations to be examined. The obtained results indicate that the domain of the semantic web reveals a variety of options for enhancing text classifications, which are highly competitive with current methods. Future work will include identifying best relations to be utilized among the available 20 relations.

Keywords- Arabic Text classification; Stemmer; Part of Speech; Conceptual features; Semantic relations.

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