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:	https://ir.uitm.edu.my/id/eprint/18846/1/18846.pdf
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id	my-uitm-ir.18846
record_format	uketd_dc
spelling	my-uitm-ir.188462022-11-14T02:46:28Z Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal 2017 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 https://ir.uitm.edu.my/id/eprint/18846/ https://ir.uitm.edu.my/id/eprint/18846/1/18846.pdf text en public mphil masters Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences Puteh, Mazidah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Puteh, Mazidah
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
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
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
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/18846/1/18846.pdf
_version_	1783733710685732864

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

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