Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal

In the age of wide digital usage, text classification is one of the significant prominent attribute required in order to automatically arrange emails, articles, and other textual data in an organization. Unclassified data can lead to slower data retrieval thus a reliable method is required to effect...

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Main Author:	Fadzal, Ahmad Nazmi
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Algorithms
Online Access:	http://ir.uitm.edu.my/id/eprint/18846/ http://ir.uitm.edu.my/id/eprint/18846/1/TM_AHMAD%20NAZMI%20FADZAL%20CS%2017_5.pdf

id	uitm-18846
recordtype	eprints
spelling	uitm-188462018-02-04T02:34:19Z http://ir.uitm.edu.my/id/eprint/18846/ Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal Fadzal, Ahmad Nazmi Algorithms In the age of wide digital usage, text classification is one of the significant prominent attribute required in order to automatically arrange emails, articles, and other textual data in an organization. Unclassified data can lead to slower data retrieval thus a reliable method is required to effectively retrieve data efficiently and in systematic manner. Ant Colony Optimization (ACO) is a bio-inspired technique that was introduced to solve Non-Polynomial hard problem of high text data dimension that is similar to Traveling Salesman Problem (TSP) using probabilistic way. Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. ACO classification accuracy is compared to Genetic Algorithm classifier which also a wrapper method. On integration of the technique, ACO is proposed to work in a multicore-multithread environment to gain additional execution time advantage. In multicore-multithread environment, the adjustment aims to make artificial ants communicate across the physical core of processor. As a trade to the investment for more computing power, the execution time reduction is expected to show an improvement without compromising the original classification accuracy. The unthreaded and multicore-multithreaded version of ACO was experimented and compared in term of accuracy and execution time. It was found that the result show a positive improvement. 2017 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/18846/1/TM_AHMAD%20NAZMI%20FADZAL%20CS%2017_5.pdf Fadzal, Ahmad Nazmi (2017) Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Algorithms
spellingShingle	Algorithms Fadzal, Ahmad Nazmi Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
description	In the age of wide digital usage, text classification is one of the significant prominent attribute required in order to automatically arrange emails, articles, and other textual data in an organization. Unclassified data can lead to slower data retrieval thus a reliable method is required to effectively retrieve data efficiently and in systematic manner. Ant Colony Optimization (ACO) is a bio-inspired technique that was introduced to solve Non-Polynomial hard problem of high text data dimension that is similar to Traveling Salesman Problem (TSP) using probabilistic way. Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. ACO classification accuracy is compared to Genetic Algorithm classifier which also a wrapper method. On integration of the technique, ACO is proposed to work in a multicore-multithread environment to gain additional execution time advantage. In multicore-multithread environment, the adjustment aims to make artificial ants communicate across the physical core of processor. As a trade to the investment for more computing power, the execution time reduction is expected to show an improvement without compromising the original classification accuracy. The unthreaded and multicore-multithreaded version of ACO was experimented and compared in term of accuracy and execution time. It was found that the result show a positive improvement.
format	Thesis
author	Fadzal, Ahmad Nazmi
author_facet	Fadzal, Ahmad Nazmi
author_sort	Fadzal, Ahmad Nazmi
title	Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
title_short	Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
title_full	Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
title_fullStr	Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
title_full_unstemmed	Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
title_sort	ant colony algorithm for text classification in multicore-multithread environment / ahmad nazmi fadzal
publishDate	2017
url	http://ir.uitm.edu.my/id/eprint/18846/ http://ir.uitm.edu.my/id/eprint/18846/1/TM_AHMAD%20NAZMI%20FADZAL%20CS%2017_5.pdf
first_indexed	2023-09-18T23:01:20Z
last_indexed	2023-09-18T23:01:20Z
_version_	1777418193536548864

Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal

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