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Fulltext A study on user’s comfort level and seat mismatch in a lecture theatre

Ahmad Kamal, S., Baba, M.D., Nor Kamaliana, K., Ahmad Rasdan, I., Abu, A.

Malaysian Journal of Public Health Medicine, 2016;16(20):5-13.
MyJurnal

The furnishings provided by the Institute of Higher Learning (IHL) are not suitable with the diverse student’s body
shapes. This could leads to lack of comfort in usage of the furnishings as well as affecting the effectiveness of the
delivery of the lectures. Therefore, the objectives of this study are to evaluate the student’s comfort level in the
current seat of the Lecture Theatre (LT) and to gather student’s anthropometry data. Two hundred and twenty one
students were involved in this study from one of the IHL in Selangor. A few similar design of LT were chosen for this
study. The Standard Nordic Questionnaire was used to identify the comfort level of students. Eleven body parts
measurements of all participated students were collected by using measurement apparatus such as callipers and
ruler. Based on the findings, it showed that the current seat in LT is uncomfortable to be used, in line with the
results from surveys and mismatch of the seat and student measurement data.
Toxicity of produced water from crude oil terminals to Photobacterium phosphoreum, Chaetoceros sp., and Donax faba

Din ZB, Abu AB

Bull Environ Contam Toxicol, 1993 Mar;50(3):413-6.
PMID: 8428121
Fulltext Plant data visualisation using network graphs

Mohamad-Matrol AA, Chang SW, Abu A

PeerJ, 2018;6:e5579.
PMID: 30186704 DOI: 10.7717/peerj.5579

Background: The amount of plant data such as taxonomical classification, morphological characteristics, ecological attributes and geological distribution in textual and image forms has increased rapidly due to emerging research and technologies. Therefore, it is crucial for experts as well as the public to discern meaningful relationships from this vast amount of data using appropriate methods. The data are often presented in lengthy texts and tables, which make gaining new insights difficult. The study proposes a visual-based representation to display data to users in a meaningful way. This method emphasises the relationships between different data sets.
Method: This study involves four main steps which translate text-based results from Extensible Markup Language (XML) serialisation format into graphs. The four steps include: (1) conversion of ontological dataset as graph model data; (2) query from graph model data; (3) transformation of text-based results in XML serialisation format into a graphical form; and (4) display of results to the user via a graphical user interface (GUI). Ontological data for plants and samples of trees and shrubs were used as the dataset to demonstrate how plant-based data could be integrated into the proposed data visualisation.
Results: A visualisation system named plant visualisation system was developed. This system provides a GUI that enables users to perform the query process, as well as a graphical viewer to display the results of the query in the form of a network graph. The efficiency of the developed visualisation system was measured by performing two types of user evaluations: a usability heuristics evaluation, and a query and visualisation evaluation.
Discussion: The relationships between the data were visualised, enabling the users to easily infer the knowledge and correlations between data. The results from the user evaluation show that the proposed visualisation system is suitable for both expert and novice users, with or without computer skills. This technique demonstrates the practicability of using a computer assisted-tool by providing cognitive analysis for understanding relationships between data. Therefore, the results benefit not only botanists, but also novice users, especially those that are interested to know more about plants.
Fulltext Classification and Morphology of Rhinocypha spp. (Odonata): A Comprehensive Taxonomic Study Within the Females

Mamat N, Abu A, Yusoff NR

Zool Stud, 2021;60:e47.
PMID: 35003341 DOI: 10.6620/ZS.2021.60-47

Studies on Odonata have gained attention worldwide as well as locally in Malaysia. Although there is a wealth of data available to be utilized for solving taxonomic problems, ecological and behavioural research areas are more favoured than taxonomy and systematics. Thus, there are confusions over how to correctly identify closely related and sympatric species, especially in female odonates. One such example is in the genus Rhinocypha. Consequently, the present study focuses on taxonomic work, employing multi-approaches in the form of morphological (morphological diagnostics, Field Emission Scanning Electron Microscope (FESEM) and geometric morphometric analysis), applying the molecular technique. Seventeen morphological characteristics were created to differentiate between the females of Rhinocypha spp. A FESEM was used on the female's ovipositor to focus on the anal appendages and sheathing valve (V3). Also, the phylogenetic patterns expressed by COI and 16S rRNA genes, and canonical variate analysis for the wing geometric morphometric revealed three clusters that supported the distinction of the Rhinocypha group. In summary, this study effectively developed an integrated approach of classic morphological and trendy molecular, combined with FESEM microscopy techniques, which provided corroborative evidence and resolved taxonomic uncertainties.
Fulltext Semantic representation of monogenean haptoral Bar image annotation

Abu A, Susan LL, Sidhu AS, Dhillon SK

BMC Bioinformatics, 2013;14:48.
PMID: 23398696 DOI: 10.1186/1471-2105-14-48

Digitised monogenean images are usually stored in file system directories in an unstructured manner. In this paper we propose a semantic representation of these images in the form of a Monogenean Haptoral Bar Image (MHBI) ontology, which are annotated with taxonomic classification, diagnostic hard part and image properties. The data we used are basically of the monogenean species found in fish, thus we built a simple Fish ontology to demonstrate how the host (fish) ontology can be linked to the MHBI ontology. This will enable linking of information from the monogenean ontology to the host species found in the fish ontology without changing the underlying schema for either of the ontologies.
Fulltext Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches

Abu A, Leow LK, Ramli R, Omar H

BMC Bioinformatics, 2016 Dec 22;17(Suppl 19):505.
PMID: 28155645 DOI: 10.1186/s12859-016-1362-5

BACKGROUND: Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs.
RESULTS: At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community.
CONCLUSIONS: This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.
Fulltext Automated craniofacial landmarks detection on 3D image using geometry characteristics information

Abu A, Ngo CG, Abu-Hassan NIA, Othman SA

BMC Bioinformatics, 2019 Feb 04;19(Suppl 13):548.
PMID: 30717658 DOI: 10.1186/s12859-018-2548-9

BACKGROUND: Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. Currently, most anthropometric studies use landmark points that are manually plotted on a 3D facial image by the examiner. This method is time-consuming and leads to human biases, which will vary from intra-examiners to inter-examiners when involving large data sets. Biased judgment also leads to a wider gap in measurement error. Thus, this work aims to automate the process of landmarks detection to help in enhancing the accuracy of measurement. In this work, automated craniofacial landmarks (ACL) on a 3D facial image system was developed using geometry characteristics information to identify the nasion (n), pronasale (prn), subnasale (sn), alare (al), labiale superius (ls), stomion (sto), labiale inferius (li), and chelion (ch). These landmarks were detected on the 3D facial image in .obj file format. The IA was also performed by manually plotting the craniofacial landmarks using Mirror software. In both methods, once all landmarks were detected, the eight linear measurements were then extracted. Paired t-test was performed to check the validity of ACL (i) between the subjects and (ii) between the two methods, by comparing the linear measurements extracted from both ACL and AI. The tests were performed on 60 subjects (30 males and 30 females).
RESULTS: The results on the validity of the ACL against IA between the subjects show accurate detection of n, sn, prn, sto, ls and li landmarks. The paired t-test showed that the seven linear measurements were statistically significant when p
Fulltext Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Pillay AB, Pathmanathan D, Dabo-Niang S, Abu A, Omar H

Sci Rep, 2024 Jul 06;14(1):15579.
PMID: 38971911 DOI: 10.1038/s41598-024-66246-z

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.
Fulltext Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Murat M, Chang SW, Abu A, Yap HJ, Yong KT

PeerJ, 2017;5:e3792.
PMID: 28924506 DOI: 10.7717/peerj.3792

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.