Jawi character recognition using the trace transform

The Trace transform, a generalisation of the Radon transform, allows one to construct image features that are invariant to a chosen group of image transformations. In this paper, we used some features, which are invariant to affine distortion, generated by the Trace transform to discriminate between Jawi characters. The process consists of tracing an image with straight lines, along which certain functionals of the image function are calculated, in all possible orientations. For each combination of functionals we derived a function of orientation of the tracing lines that is known as an object signature. If the functionals used have some predefined properties, this signature can be used to characterise the character in an affine way. We demonstrated the usefulness of the derived signature and compared the result of character recognition with those obtained by using features based on affine moment invariants. Experiments using the Trace transform produced decent results for the printed and handwritten Jawi character recognitions that are invariant to affine distortion.Keyword: Affine moment invariant; Jawi character recognition; trace transfor

A new initialization technique in polar coordinates for Particle Swarm Optimization and Polar PSO

Particle Swarm Optimization (PSO) is one of the famous algorithms inspired by the natural behavior of a swarm (particles). However, it is used to solve n-dimensional problems in search space. One of its modified versions a Polar Particle Swarm Optimizer, was operated in polar coordinates by using an appropriate mapping function introduced based on polar coordinates. The modified algorithm faced some problems, such as generating a distorted search space, which may have been caused by the method of randomization. This paper introduces an initialization technique that operates entirely in polar coordinates. Moreover, an investigation based on standard PSO was done to test the proposed technique. The second part was to use the new initialization technique to enhance the polar PSO performance. In addition, the proposed techniques show evenly distributed points in the polar search space. Furthermore, experimental results were obtained by using various benchmark test functions on different settings of dimensions. While its shows a little enhancement in some benchmark test functions in both PSO and polar PSO, statistically there are no significant differences by using the analysis of variance (ANOVA)

Local search manoeuvres recruitment in the bees algorithm

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

Review of local binary pattern operators in image feature extraction

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

Review of different strategies for coordinative planning of multi-agent systems

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

Experimental Approach Based on Ensemble and Frequent Itemsets Mining for Image Spam Filtering

Excessive amounts of image spam cause many problems to e-mail users. Since image spam is difficult to detect using conventional text-based spam approach, various image processing techniques have been proposed. In this paper, we present an ensemble method using frequent itemset mining (FIM) for filtering image spam. Despite the fact that FIM techniques are well established in data mining, it is not commonly used in the ensemble method. In order to obtain a good filtering performance, a SIFT descriptor is used since it is widely known as effective image descriptors. K-mean clustering is applied to the SIFT keypoints which produce a visual codebook. The bag-of-word (BOW) feature vectors for each image is generated using a hard bag-of-features (HBOF) approach. FIM descriptors are obtained from the frequent itemsets of the BOW feature vectors. We combine BOW, FIM with another three different feature selections, namely Information Gain (IG), Symmetrical Uncertainty (SU) and Chi Square (CS) with a Spatial Pyramid in an ensemble method. We have performed experiments on Dredze and SpamArchive datasets. The results show that our ensemble that uses the frequent itemsets mining has significantly outperform the traditional BOW and naive approach that combines all descriptors directly in a very large single input vector

Maxenttagger for Malays Jawi POS-tags

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

NUWT: Jawi-specific Buckwalter corpus for Malays word tokenization

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

Primary and Secondary School Students Perspective on Kolb-based STEM Module and Robotic Prototype

The aim of this paper is to assess students' perceptions of their competency and interests in Science, Technology, Engineering and Mathematics (STEM) throughout Malaysia. These perceptions are obtained during and after they were engaged in using a STEM module and building a robotic prototype that was in line with the STEM teachers' specification, and was conducted at the National Science Centre, Malaysia. This activity was undertaken because the target ratio for the number of students enrolling in STEM programs is not met. The developed STEM module is based on four stages of the learning cycle in Kolb's experiential learning theory. The stages are Concrete Experience, Reflective  Observation, Abstract Conceptualization, and Active Experimentation. These stages have five key educational activities which are watching videos, reading modules, assembling robotic components, drag and drop using blockly software and lastly playing a robotic game.  The key element of the activities is the utilisation of a robotic prototype as the main component in increasing the students’ interest in STEM via games. This module was evaluated in both qualitative and quantitative case studies of students to inform teachers’ perceptions of the developed modules and robotic prototypes. Data were collected through two training events at a science exhibition at the National Science Centre and taken from two distinct groups, namely primary and secondary school students in range 11 to 15 year old. The evaluation comprised of five areas which were interaction, engagement, challenge, competency and interest. The results show that developed module and robotic prototype based n teacher’s perception received positive response from the respondents. It can efficiently raise students’ interest in STEM that meets the Malaysia Education Blueprint 2013-2025.

Exploiting Features From Triangle Geometry For Digit Recognition

Triangle is a basic geometry. There are six type of triangle, but scalene triangle was chosen to be used in this research that based on coordinates of corners generated by our proposed algorithm. In this paper, nine features are proposed where six features were derived from coordinates and sides of triangle. Another three features are angle of corners. After features are identified, image will be zoned into 25 zones. The zoning processes are based on Cartesian plan, Vertical and Horizontal zones. From the zoning, from nine features will become 225 features. The features proposed will be used to HODA, MNIST, IFHCDB and BANGLA datasets. Experiments will be conducted using supervised learning that are Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). Results from the experiments will be evaluated with different Cost (c) for the SVM and Learning Rate (LR) for the MLP. Then, the result will be compared to state of the art by other researches