Search

With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing.

Agent-based systems have been widely examined in the literature for various type of tasks. Within this examination, various strategies and modeling have been employed. Several surveys and reviews have been depicted in the literature regarding agent-based systems. However, minimal efforts have been made in the context of feature extraction and feature selection. This paper aims to review the strategies used for feature extraction and selection agentbased systems. In terms of the nature of agent communications, this paper tackles two types, centralized and decentralized. In terms of the workflow, this paper tackles three types, including coordinative, collaborative and emergent-based systems. Finally, a discussion is presented comparing the strategies and the frequent use of the strategies in the literature. Based on this review, most of feature extraction agent-based systems rely on either coordinating or emergent-based strategies, while feature selection agent-based systems rely on collaborative strategies. However, there are several aspects that we can consider to be classify agent-based strategies. This review develops a classification scheme for systems used for specific tasks, including feature extraction and feature selection.

Purpose - Malay is a major language of the Austronesian family spoken in many countries. Malay Jawi is lacking in annotated resources and tools. In addition, Part-of-speech (POS) ambiguity in Natural Language Processing (NLP) is a vague important phenomenon that needs to be solved immediately. Since POS is an important feature of the word, and is the link between the words and syntax, POS tagging (POST) needs to provide intermediate results showing superior performance to the next NLP tasks. POS ambiguity is a main problem in increasing POST performance. POST performance is often measured with accuracy and precision of a tag and it was considered critical to NLP application. Some of the standard package POS tagging provided in Natural Language ToolKit (NLTK) are Brill tagger, HMM tagger, and CRF Tagger. In this paper, POST Malay Jawi implemented NLP tools, NLTK for the state-of-the-art methods tagger; maximum entropy models. NLTK is used as the implementation tool for Jawi tagging, as syntax and semantics of the language is transparent, and it has the good functionality of NLP-operator. The tool also uses Python as the implementation language.

This paper describes the design and creation of a monolingual parallel corpus for the Malay language written in Jawi. This paper proposes a new corpus called the National University of Malaysia Word Tokenization (NUWT) corpora To the best of our knowledge, currently, there is no sufficiently comprehensive, well-designed standard corpus that is annotated and made available for the public for the Jawi script corpora. This corpus contains the Jawi-specific Buckwalter character code and can be used to evaluate the performance of word tokenization tasks, as well as further language processing. The objective of this work is to conform and standardize the corpora between similar characters in Jawi. It consists of three subcorporas with documents from different genres. The gathering and processing steps, as well as the definition of several evaluation tasks regarding the use of these corpora, are included in this paper. One of the important roles and fundamental tasks of the corpus, which is the tokenization, is also presented in this paper. The development of the Malay language tokenizer is based on the syntactic data compatibility of Malay words written in Jawi. A series of experiments were performed to validate the corpus and to fulfill the requirement of the Jawi script tokenizer with an average error rate of 0.020255. Based on this promising result, the token will be used for the disambiguation and unknown word resolution, such as out-of-vocabulary (OOV) problem in the tagging process.

Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as the two most important challenges that these techniques face to solve such problem. This study presents a comprehensive survey of the various techniques that have been proposed in this domain. As part of this survey, we include a classification of the approaches and identify their methods.

Research in Malay Part-of-Speech (POS) has increased considerably in the past few years.From the literature, POS are known as the first stage in automated text analysis and the development of language technologies can scarcely begun without this initial phase.Malay language can be written in Roman or Jawi.Three different spelling between Roman and Jawi make this study essential.In this paper, we highlighted the problem and issues related to Malay language, POS general framework, POS approaches and techniques.POS at basis was introduced to get information from Old Malay Manuscripts that contain important information in various spheres of knowledge.Promising result for the auto-tagging of Malay written in Jawi is expected.

Swarm intelligence of honey bees had motivated many bioinspired based optimisation techniques. The Bees Algorithm (BA) was created specifically by mimicking the foraging behavior of foraging bees in searching for food sources.During the searching, the original BA ignores the possibilities of the recruits being lost during the flying.The BA algorithm can become closer to the nature foraging behavior of bees by taking account of this phenomenon.This paper proposes an enhanced BA which adds a neighbourhood search parameter which we called as the Local Search Manoeuvres (LSM) recruitment factor.The parameter controls the possibilities of a bee extends its neighbourhood searching area in certain direction.The aim of LSM recruitment is to decrease the number of searching iteration in solving optimization problems that have high dimensions.The experiment results on several benchmark functions show that the BA with LSM performs better compared to the one with basic recruitment.