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Fulltext Maintenance and physical asset management issues in project commissioning

Ahmad Fuad Ab Ghani, Azrin Ahmad, Nor Salim Muhammad, Reduan Mat Dan, Rustamreen Jenal

Malaysian Journal of Applied Sciences, 2018;2(2):10-28.
MyJurnal

This study describes the review on maintenance related issues during design and construction stage
within construction industry. The paper highlights the causes and errors made during design and
construction stage and their impact during the operation/production/occupancy stage as well as the
maintenance costs associated with it. The study identifies the mistakes in the working processes within
design and construction stage leading to the errors that affect the durability, performance, reliability,
maintainability, availability and safety of the systems. The paper presents a comprehensive review of
the published literatures, journals, technical papers in the related areas in the construction field. The
review highlights the new approaches and decision framework which link the designers and
construction personnel that could reduce the errors and defects in construction which then lead to
maintenance issues and asset management. The factors of accessibility, materials, design and
documentation standardization have been discussed thoroughly for better understanding in improving
maintenance and physical asset management in project commissioning.

Matched MeSH terms: Learning
Fulltext Massive Open Online Courses (MOOCs): Data on higher education

Al-Rahmi W, Aldraiweesh A, Yahaya N, Bin Kamin Y, Zeki AM

Data Brief, 2019 Feb;22:118-125.
PMID: 30581914 DOI: 10.1016/j.dib.2018.11.139

The data presented in this article are based on provides a systematic and organized review of 219 studies regarding using of Massive Open Online Courses (MOOCs) in higher education from 2012 to 2017. Consequently, the extant, peer-reviewed literature relating to MOOCs was methodically assessed, as a means of formulating a classification for MOOC-focused scholarly literature. The publication journal, country of origin, researchers, release data, theoretical approach, models, methodology and study participants were all factors used to assess and categorise the MOOC. These data contribute to materials required by readers who are interested in different aspects related to the literature of using Massive Open Online Courses (MOOCs) in higher education. Intention to use, interaction, engagement, motivations and satisfaction were five dynamics assessed in relation to the improvement of MOOCs. Students' academic performance can be influenced by MOOC which has the advantage of facilitating the learning process through offering materials and enabling the share of information.

Matched MeSH terms: Learning
Google classroom as a learning management system to teach biochemistry in a medical school

Dash S

Biochem Mol Biol Educ, 2019 07;47(4):404-407.
PMID: 30994974 DOI: 10.1002/bmb.21246

Medical education has adopted various e-learning technologies to its aid. Addition of Google Classroom, introduced in 2014, as a Learning Management System (LMS) has provided a basic, easy to use platform. This study tested its efficacy in teaching a biochemistry module to first year MBBS students in an Indian medical school. Better access to learning material and supplementary teaching resources, helpfulness of immediate feedback, and learning outside of class environment were reported by students. Preference of mobile phone over laptop to access this LMS was reported. Use of this free to use LMS can be made, and especially in resource limited low and middle income countries, to encourage greater access to e-learning. © 2019 International Union of Biochemistry and Molecular Biology, 47(4):404-407, 2019.

Matched MeSH terms: Learning
Fulltext Algorithm for solution of non-convex optimization problem through piece-wise convex transformation

Kerk, Lee Chang, Rohanin Ahmad

MATEMATIKA, 2018;34(2):381-392.
MyJurnal

Optimization is central to any problem involving decision making. The area
of optimization has received enormous attention for over 30 years and it is still popular
in research field to this day. In this paper, a global optimization method called Improved
Homotopy with 2-Step Predictor-corrector Method will be introduced. The method in-
troduced is able to identify all local solutions by converting non-convex optimization
problems into piece-wise convex optimization problems. A mechanism which only consid-
ers the convex part where minimizers existed on a function is applied. This mechanism
allows the method to filter out concave parts and some unrelated parts automatically.
The identified convex parts are called trusted intervals. The descent property and the
global convergence of the method was shown in this paper. 15 test problems have been
used to show the ability of the algorithm proposed in locating global minimizer.

Matched MeSH terms: Learning
Fulltext Methodological dilemma in qualitative research in education

ROHAIDA MOHD. SAAT, HIDAYAH MOHD FADZIL

Malaysian Journal of Qualitative Research, 2019;5(2):41-46.
MyJurnal

This paper discusses methodological dilemma that arise in qualitative research, specifically in education field. It outlines the broad principles that underpin good qualitative research and the aspects of practice that qualitative researchers should consider when designing, conducting, and disseminating their research. Two primary methodological dilemma are (i) lack of objectivity, and (ii) issue of generalizability in qualitative research. The aim of this paper is to argue the dilemmas and encourage researchers to examine the relevance of qualitative issues to their own research. These dilemmas could be taken as important consideration for others who wish to conduct qualitative research in education.

Matched MeSH terms: Problem-Based Learning
Fulltext 3D texture-based face recognition system using fine-tuned deep residual networks

Zheng S, Rahmat RWO, Khalid F, Nasharuddin NA

PeerJ Comput Sci, 2019;5:e236.
PMID: 33816889 DOI: 10.7717/peerj-cs.236

As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.

Matched MeSH terms: Machine Learning
Fulltext Educational intervention for informal caregiver of person with dementia: a systematic review

Nuraisyah Hani Zulkifley, Suriani Ismail, Rosliza Abdul Manaf, Lim Poh Ying

Malaysian Journal of Medicine and Health Sciences, 2020;16(1):325-331.
MyJurnal

The role of caregivers is very important in the management of person with dementia, where it is not uncommon for them to experience psychological distress. However, the level of distress can be managed and reduced through stra- tegic educational intervention. A systematic review has been conducted through searching Medline, Science direct, Cochrane library and EMBASE databases to provide a narrative synthesis that elaborate on methods and outcomes of the educational intervention among informal caregiver of person with dementia. From a total of 5125 records, eight studies were selected and included in this review, where the results show that educational intervention can be implemented either as individual or group intervention. Group intervention methods mainly focus on training pro- grams such as workshops and lectures, and also group-based discussions. While for individual intervention, most of the activities were implemented through self-learning using technology or computer-based systems. In conclusion, based on the outcome of the studies, both methods of implementations are found to be useful in reducing psycho- logical distress of the informal caregiver.

Matched MeSH terms: Learning
Fulltext Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning

Lee S, Abdullah A, Jhanjhi N, Kok S

PeerJ Comput Sci, 2021;7:e350.
PMID: 33817000 DOI: 10.7717/peerj-cs.350

The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.

Matched MeSH terms: Machine Learning
Fulltext Latent based temporal optimization approach for improving the performance of collaborative filtering

Al-Hadi IAA, Sharef NM, Sulaiman MN, Mustapha N, Nilashi M

PeerJ Comput Sci, 2020;6:e331.
PMID: 33816980 DOI: 10.7717/peerj-cs.331

Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers' ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers' drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.

Matched MeSH terms: Learning
Twelve tips for conducting team-based learning session online in synchronous setting

Malik AS, Malik RH

Med Teach, 2021 Apr 09.
PMID: 33836640 DOI: 10.1080/0142159X.2021.1910642

INTRODUCTION: COVID-19 pandemic has challenged the educators to creatively develop teaching and assessment methods that can work effectively and efficiently while maintaining the social distancing and avoiding the gatherings of the classrooms and examination halls. Online approach has emerged as an effective alternate for classroom teaching.
AIM: To equip faculty with tools to conduct TBL session online, synchronously, effectively and efficiently.
METHODS: We examined the published literature in the area of online teaching and combined it with our own experience of conducting TBL sessions online.
RESULTS: We created 12 tips to assist faculty to facilitate an effective and engaging TBL session online.
CONCLUSIONS: Applying these 12 tips while facilitating a TBL-online session will ensure the full engagement of students in the process of active learning.

Matched MeSH terms: Problem-Based Learning
Fulltext Using historical sources as history teaching and learning materials: are students ready to explore?

Kaviza, M.

Malaysian Journal of Social Sciences and Humanities, 2018;3(5):41-47.
MyJurnal

The purpose of this study is to examine the level of readiness amongstudents in terms of knowledge,
skills and attitudes in using historical resources as history teaching and learning materials in secondary
schools. The design of this study is a quantitative research that uses survey method involving a total of
521 form four students from secondary schools using simple random sampling technique. The
questionnaire are used in this study which has been verified by the content expert dan has a good
realiability value. The data were analysed using descriptive and inferential statistics such as MONOVA
and Correlation Pearson using "IBM SPSS Statistics”version 24.The findings of this study indicate that
the level of readiness amongsecondary history students in terms of knowledge, skills and attitudes in
using historical resources as teaching and learning materials are at moderate level. Beside that, school
location influences the level of readiness and there a relationship between levels of readiness with
school location among students.Implication of this study can help history teachers know the level of their student knowledge, skills and attitudes toward using historical sources before carrying out in their
lessons.

Matched MeSH terms: Learning
Fulltext Mobile phones: The effect of its presence on learning and memory

Tanil CT, Yong MH

PLoS One, 2020;15(8):e0219233.
PMID: 32790667 DOI: 10.1371/journal.pone.0219233

Our aim was to examine the effect of a smartphone's presence on learning and memory among undergraduates. A total of 119 undergraduates completed a memory task and the Smartphone Addiction Scale (SAS). As predicted, those without smartphones had higher recall accuracy compared to those with smartphones. Results showed a significant negative relationship between phone conscious thought, "how often did you think about your phone", and memory recall but not for SAS and memory recall. Phone conscious thought significantly predicted memory accuracy. We found that the presence of a smartphone and high phone conscious thought affects one's memory learning and recall, indicating the negative effect of a smartphone proximity to our learning and memory.

Matched MeSH terms: Learning
Fulltext Practical Learning and Theory-Practice as Perceived by Student Nurses in Universiti Malaysia Sabah

Hairie Aiery, Nur Izzati M. T, Ivyta D., Farah Ezora Shafine A. B., Sukhbeer K. Darsin Singh, R. Segaran

Borneo Journal of Medical Sciences, 2018;12(101):75-76.
MyJurnal

The theory-practice gap is arguably the most important issue in nursing today, given that it challenges the concept of research-based practice, which is the basis of nursing as a profession. Majority of the student nurses shared their views that some of the practical procedures that they learned during their theory sessions were different from what was practised in the wards which caused some worries among the students that it may affect their performance during their Obstructive Structured Clinical Examination.

Matched MeSH terms: Learning
Fulltext Students’ learning styles and their academic performance in accounting course

Wan Mardyatul Miza Wan Tahir, Akma Hidayu Dol @ Abdul Wahid, Amariah Hanum Hussin, Ja’izah Abdul Jabar

Journal of Academia Universiti Teknologi MARA Negeri Sembilan, 2019;7(101):10-17.
MyJurnal

Learning accounting for non accounting major students is constantly considered challengin g. Therefore, the objective of the study is to identify the relationship between the learning style adopted by non accounting students in learning accounting course and the impact on their course performance. The Kolb’s learning style survey model that was re designed by Honey and Mumford in 1986 was adopted to recognise the learning style preferred by students. The students’ academic performance in accounting course was obtained from their scores in major assessment methods including assignment, test, quiz, an d final examination result, which represented their final grade. Further, this paper identified other factors affecting students’ academic performance. The result indicated that students who adopted the Pragmatist and Theorist learning styles were more exc ellent in their academic performance in accounting course, while those who adopted the Activist learning style were poorer in their academic result. Accordingly, accounting course does not only involve number, data, and calculation but requires fact fin din g and applying critical thinking, areas in which the Activist learning style lacks. Other factors found that educators who conducted the lecture were recognised as important contributors towards the students’ achievement in accounting course. Neverthele ss, students with a higher level of anxiety performed better academically as compared to those with low anxiety. In conclusion, to succeed in accounting course, students should not rely merely on one style in the learning process.

Matched MeSH terms: Learning
Fulltext The relationship between co-curricular management and secondary school student involvement in co-curricular activities

Sheila Michael, Abdul Said Ambotang

Malaysian Journal of Social Sciences and Humanities, 2019;4(7):202-207.
MyJurnal

This concept paper aims to discuss the relationship between co-curricular management with student involvement in secondary school. Student involvement in co-curricular activities can shape the overall personality of the students. This can be highlighted through excellent co-curricular management. Cocurricular managers play a key role in the success of the engagement. Student engagement excellence is closely related to co-curricular management. The higher co-curricular management effectiveness is, the greater impact it has on student engagement. The implementation of management is based on the objectives and capabilities of the students to enhance the knowledge, skills and values learned. Therefore, co-curricular management is related to student involvement in co-curricular activities.

Matched MeSH terms: Learning
Changes in Error Patterns during N-back Training Indicate Reliance on Subvocal Rehearsal

Chooi WT, Logie R

Mem Cognit, 2020 11;48(8):1484-1503.
PMID: 32661910 DOI: 10.3758/s13421-020-01066-w

Contemporary cognitive training literature suggests that training on an adaptive task produces improvements only in the trained task or near transfer effects. No study has yet systematically explained the mechanism behind improved performance on the N-back. In this study, we first investigated how improvements in an N-back task using eight pairs of phonologically similar words as stimuli occurred by examining error distributions of the task over training sessions. Nineteen participants (non-native English speakers) trained for 20 sessions over 5 weeks. We observed a reduction in false alarms to non-target words and fewer missed target words. Though the absolute number of phonological-based errors reduced as training progressed, the proportion of this error type did not decrease over time suggesting participants increasingly relied on subvocal rehearsal in completing the N-back. In the second experiment, we evaluated if improvements developed during N-back training transferred to tasks that relied on serial order memory using simple span tasks (letter span with phonologically distinct letters, letter span with phonologically similar letters, digit span forward, and digit span backward). Twenty-nine participants trained on the N-back and 16 trained on the Operation Span (OSPAN) for 15 sessions over 4 weeks. Neither group of participants showed improvements on any of the simple span tasks. In the third experiment, 20 participants (16 native English speakers) trained on the N-back for 15 sessions over 4 weeks also showed increasing reliance on subvocal rehearsal as they progressed through training. Self-report strategy use did not predict improvements on the N-back.

Matched MeSH terms: Learning
Fulltext Teaching Epidemiology and Biostatistics: a peer-led teaching module for postgraduate psychiatry student

Pang,Nicholas Tze Ping, Koh,Eugene Boon Yau, Sandi James, Mohd Amiruddin Mohd Kassim

Borneo Epidemiology Journal, 2020;1(2):157-162.
MyJurnal

Background and Objective: Biostatistics and epidemiology have been integral subjects in any postgraduate courses, including medical specialties Master programs. Both are widely accepted as among the difficult and confusing subjects, which worsen by lack of adequate exposure and often, time constraints. Hence, peer-led learning approach was proposed as a viable option to the traditional lecturer-driven learning style
Method: The peer-led approach intends to promote targeted learning and conceptual understanding, instead of widely sweeping learning, which is rather directionless and could cause information overload
Discussion: Students were divided into two groups, namely humanities-inclined group and science inclined group. Different pedagogical methods to address the different groups were discussed.
Conclusion: This approach helps to make the learning more palatable, boosting knowledge retention and fostering camaraderie spirit among colleagues

Matched MeSH terms: Learning
Fulltext Defining Digital Game-Based Learning for Science, Technology, Engineering, and Mathematics: A New Perspective on Design and Developmental Research

Ishak SA, Din R, Hasran UA

J Med Internet Res, 2021 02 19;23(2):e20537.
PMID: 33605885 DOI: 10.2196/20537

In the modern age, digital games are widely used as informal media for Science, Technology, Engineering, and Mathematics (STEM) education and medical therapy for game-based learning. Digital games provide learners with a graphical system of interaction that enhances scientific concepts within an enjoyable environment. The vastly increasing number of digital games produced in the market affects the quality of STEM digital games while requiring multidisciplinary expertise. This paper proposes a framework for STEM digital game-based learning encompassing input-process-output stages. Several studies from the early 2000s onward were reviewed to discuss and present a new perspective on a framework for the design and development of digital games, particularly for STEM. This proposed framework consists of digital game development as input, experience as a process, and constructs as output. This simple and precise framework will generate a universal product for various types of learners. It can thus be used as a guideline for game designers, developers, and experts to develop STEM digital games and achieve better learning outcomes.

Matched MeSH terms: Learning
Fulltext MALAY SINGLE SENTENCE PARSER

MUHAMMAD IQBAL NORDIN, NOOR HAFHIZAH ABD RAHIM

UMT Journal of Undergraduate Research, 2019;1(3):87-95.
MyJurnal

Parser is aprocess of classifying sentence structuresof a language. Parser receives a sentence and breaks it up into correct phrases. The purpose of this research is to develop a Malay single sentence parser that can help primary school studentsto learn Malay language according to the correct phrases. Thisis because research in Malay sentenceparsinghasnot gottenenough attention from researchers tothe extent ofbuildingparserprototypes. This research used top-down parsing technique,and grammar chosen was context-free grammar (CFG) for Malay language. However, to parse a sentence with correct phrase was a difficult task due to lack of resourcesfor obtainingMalay lexicon. Malay lexicon is a database that storesthousands of words with their correct phrases. Therefore, this research developeda Malay lexicon based on an articlefrom Dewan Masyarakatmagazine. In conclusion, this research can providehelpto the primaryschoolstudentsto organize correct Malay single sentences.

Matched MeSH terms: Learning
Fulltext Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

Barua PD, Muhammad Gowdh NF, Rahmat K, Ramli N, Ng WL, Chan WY, et al.

Int J Environ Res Public Health, 2021 07 29;18(15).
PMID: 34360343 DOI: 10.3390/ijerph18158052

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

Matched MeSH terms: Machine Learning

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