Displaying publications 81 - 100 of 908 in total

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  1. Ummu Atiqah Mohd Roslan
    MATEMATIKA, 2018;34(1):13-21.
    MyJurnal
    Markov map is one example of interval maps where it is a piecewise expanding
    map and obeys the Markov property. One well-known example of Markov map is the
    doubling map, a map which has two subintervals with equal partitions. In this paper, we
    are interested to investigate another type of Markov map, the so-called skewed doubling
    map. This map is a more generalized map than the doubling map. Thus, the aims of this
    paper are to find the fixed points as well as the periodic points for the skewed doubling
    map and to investigate the sensitive dependence on initial conditions of this map. The
    method considered here is the cobweb diagram. Numerical results suggest that there exist
    dense of periodic orbits for this map. The sensitivity of this map to initial conditions is
    also verified where small differences in initial conditions give different behaviour of the
    orbits in the map.
    Matched MeSH terms: Learning
  2. Zafar R, Qayyum A, Mumtaz W
    J Integr Neurosci, 2019 Sep 30;18(3):217-229.
    PMID: 31601069 DOI: 10.31083/j.jin.2019.03.164
    In the electroencephalogram recorded data are often confounded with artifacts, especially in the case of eye blinks. Different methods for artifact detection and removal are discussed in the literature, including automatic detection and removal. Here, an automatic method of eye blink detection and correction is proposed where sparse coding is used for an electroencephalogram dataset. In this method, a hybrid dictionary based on a ridgelet transformation is used to capture prominent features by analyzing independent components extracted from a different number of electroencephalogram channels. In this study, the proposed method has been tested and validated with five different datasets for artifact detection and correction. Results show that the proposed technique is promising as it successfully extracted the exact locations of eye blinking artifacts. The accuracy of the method (automatic detection) is 89.6% which represents a better estimate than that obtained by an extreme machine learning classifier.
    Matched MeSH terms: Machine Learning
  3. Yang C, Simon G, See J, Berger MO, Wang W
    Sensors (Basel), 2020 May 27;20(11).
    PMID: 32471231 DOI: 10.3390/s20113045
    Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments.
    Matched MeSH terms: Learning
  4. Almaleeh AA, Zakaria A, Kamarudin LM, Rahiman MHF, Ndzi DL, Ismail I
    Sensors (Basel), 2022 Jan 05;22(1).
    PMID: 35009947 DOI: 10.3390/s22010405
    The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors' knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos.
    Matched MeSH terms: Machine Learning
  5. Singh OP, Vallejo M, El-Badawy IM, Aysha A, Madhanagopal J, Mohd Faudzi AA
    Comput Biol Med, 2021 Sep;136:104650.
    PMID: 34329865 DOI: 10.1016/j.compbiomed.2021.104650
    Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t-test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies.
    Matched MeSH terms: Machine Learning
  6. Huqh MZU, Abdullah JY, Wong LS, Jamayet NB, Alam MK, Rashid QF, et al.
    Int J Environ Res Public Health, 2022 Aug 31;19(17).
    PMID: 36078576 DOI: 10.3390/ijerph191710860
    OBJECTIVE: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved.

    MATERIALS AND METHODS: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied.

    RESULTS: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis.

    CONCLUSIONS: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

    Matched MeSH terms: Machine Learning
  7. Nilashi M, Abumalloh RA, Yusuf SYM, Thi HH, Alsulami M, Abosaq H, et al.
    Comput Biol Chem, 2023 Feb;102:107788.
    PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788
    Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.
    Matched MeSH terms: Machine Learning
  8. Woon LS, Mohd Daud TI, Tong SF
    BMC Med Educ, 2023 Nov 09;23(1):851.
    PMID: 37946151 DOI: 10.1186/s12909-023-04834-9
    BACKGROUND: At the Faculty of Medicine of the National University of Malaysia, a virtual patient software program, DxR Clinician, was utilised for the teaching of neurocognitive disorder topics during the psychiatry posting of undergraduate medical students in a modified team-based learning (TBL) module. This study aimed to explore medical students' learning experiences with virtual patient.

    METHODS: Ten students who previously underwent the learning module were recruited through purposive sampling. The inclusion criteria were: (a) Fourth-year medical students; and (b) Completed psychiatry posting with the new module. Students who dropped out or were unable to participate in data collection were excluded. Two online focus group discussions (FGDs) with five participants each were conducted by an independent facilitator, guided by a questioning route. The data were transcribed verbatim and coded using the thematic analysis approach to identify themes.

    RESULTS: Three main themes of their learning experience were identified: (1) fulfilment of the desired pedagogy (2), realism of the clinical case, and (3) ease of use related to technical settings. The pedagogy theme was further divided into the following subthemes: level of entry for students, flexibility of presentation of content, provision of learning guidance, collaboration with peers, provision of feedback, and assessment of performance. The realism theme had two subthemes: how much the virtual patient experience mimicked an actual patient and how much the case scenario reflected real conditions in the Malaysian context. The technical setting theme entailed two subthemes: access to the software and appearance of the user interface. The study findings are considered in the light of learning formats, pedagogical and learning theories, and technological frameworks.

    CONCLUSIONS: The findings shed light on both positive and negative aspects of using virtual patients for medical students' psychiatry posting, which opens room for further improvement of their usage in undergraduate psychiatry education.

    Matched MeSH terms: Learning
  9. Kaleem S, Sohail A, Tariq MU, Babar M, Qureshi B
    PLoS One, 2023;18(10):e0292587.
    PMID: 37819992 DOI: 10.1371/journal.pone.0292587
    Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.
    Matched MeSH terms: Machine Learning
  10. Lin GSS, Foo JY, Foong CC
    BMC Med Educ, 2023 Oct 02;23(1):716.
    PMID: 37784112 DOI: 10.1186/s12909-023-04717-z
    BACKGROUND: Dental materials science is an important subject, but research on curriculum mapping in preclinical dental materials science courses is still scarce. The present study aimed to conduct a curriculum mapping in analysing elements and suggesting recommendations for an institutional dental materials science course.

    METHODS: Curriculum mapping was conducted for the Year 2 undergraduate dental materials science course (Bachelor of Dental Surgery programme) in a Malaysian dental school. Based on Harden's framework, the following steps were used to map the curriculum of the institutional dental materials science course: (1) scoping the task; (2) deciding the mapping format; (3) populating the windows, and (4) establishing the links. Two analysts reviewed the curriculum independently. Their respective analyses were compared, and discrepancies were discussed until reaching a consensus. A SWOT analysis was also conducted to evaluate the strengths, weaknesses, opportunities, and threats associated with the curriculum.

    RESULTS: Course learning outcomes, course contents, levels of cognitive and psychomotor competencies, learning opportunities, learning resources, learning locations, assessments, timetable, staff, curriculum management and students' information were successfully scoped from the institutional dental materials science course. The present curriculum's strengths included comprehensiveness, alignment with standards, adequate learning opportunities, well-defined assessment methods, and sufficient learning resources. However, the identified weaknesses were repetition in curriculum content, limited emphasis on the psychomotor domain, dependency on a single academic staff, and limited integration of technology. The SWOT analysis highlighted the opportunities for curriculum improvement, such as revising repetitive content, emphasising the psychomotor domain, and incorporating advanced teaching strategies and technology.

    CONCLUSIONS: The present dental materials science curriculum demonstrated several strengths with some areas for improvement. The findings suggested the need to revise and optimise the course content to address gaps and enhance student learning outcomes. Ongoing monitoring and evaluation are necessary to ensure the curriculum remains aligned with emerging trends and advancements in dental materials science.

    Matched MeSH terms: Learning
  11. Alabsi BA, Anbar M, Rihan SDA
    Sensors (Basel), 2023 Jun 16;23(12).
    PMID: 37420810 DOI: 10.3390/s23125644
    The increasing use of Internet of Things (IoT) devices has led to a rise in Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks on these networks. These attacks can have severe consequences, resulting in the unavailability of critical services and financial losses. In this paper, we propose an Intrusion Detection System (IDS) based on a Conditional Tabular Generative Adversarial Network (CTGAN) for detecting DDoS and DoS attacks on IoT networks. Our CGAN-based IDS utilizes a generator network to produce synthetic traffic that mimics legitimate traffic patterns, while the discriminator network learns to differentiate between legitimate and malicious traffic. The syntactic tabular data generated by CTGAN is employed to train multiple shallow machine-learning and deep-learning classifiers, enhancing their detection model performance. The proposed approach is evaluated using the Bot-IoT dataset, measuring detection accuracy, precision, recall, and F1 measure. Our experimental results demonstrate the accurate detection of DDoS and DoS attacks on IoT networks using the proposed approach. Furthermore, the results highlight the significant contribution of CTGAN in improving the performance of detection models in machine learning and deep learning classifiers.
    Matched MeSH terms: Machine Learning
  12. Letchumanan N, Wong JHD, Tan LK, Ab Mumin N, Ng WL, Chan WY, et al.
    J Digit Imaging, 2023 Aug;36(4):1533-1540.
    PMID: 37253893 DOI: 10.1007/s10278-022-00753-1
    This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine the best machine learning (ML) model to perform the tasks. Six hundred and twenty-two breast lesions from 200 retrospective patient data were segmented and analysed. Three hundred fifty radiomics features were extracted using the Standardized Environment for Radiomics Analysis (SERA) library, one of the radiomics implementations endorsed by the Image Biomarker Standardisation Initiative (IBSI). The radiomics features and selected patient characteristics were used to train selected machine learning models to classify the breast lesions. A fivefold cross-validation was used to evaluate the performance of the ML models and the top 10 most important features were identified. The random forest (RF) ensemble gave the highest accuracy (89.3%) and positive predictive value (66%) and likelihood ratio of 13.5 in categorising benign and malignant lesions. For the classification of benign lesions, the RF model again gave the highest likelihood ratio of 3.4 compared to the other models. Morphological and textural radiomics features were identified as the top 10 most important features from the random forest models. Patient age was also identified as one of the significant features in the RF model. We concluded that machine learning models trained against texture-based radiomics features and patient features give reasonable performance in differentiating benign versus malignant breast lesions. Our study also demonstrated that the radiomics-based machine learning models were able to emulate the visual assessment of mammography lesions, typically used by radiologists, leading to a better understanding of how the machine learning model arrive at their decision.
    Matched MeSH terms: Machine Learning
  13. Spooner M, Larkin J, Liew SC, Jaafar MH, McConkey S, Pawlikowska T
    BMC Med Educ, 2023 Nov 22;23(1):895.
    PMID: 37993832 DOI: 10.1186/s12909-023-04842-9
    INTRODUCTION: While feedback aims to support learning, students frequently struggle to use it. In studying feedback responses there is a gap in explaining them in relation to learning theory. This study explores how feedback experiences influence medical students' self-regulation of learning.

    METHODS: Final-year medical students across three campuses (Ireland, Bahrain and Malaysia) were invited to share experiences of feedback in individual semi-structured interviews. The data were thematically analysed and explored through the lens of self-regulatory learning theory (SRL).

    RESULTS: Feedback interacts with learners' knowledge and beliefs about themselves and about learning. They use feedback to change both their cognitive and behavioural learning strategies, but how they choose which feedback to implement is complex. They struggle to generate learning strategies and expect teachers to make sense of the "how" in addition to the "what"" in planning future learning. Even when not actioned, learners spend time with feedback and it influences future learning.

    CONCLUSION: By exploring our findings through the lens of self-regulation learning, we advance conceptual understanding of feedback responses. Learners' ability to generate "next steps" may be overestimated. When feedback causes negative emotions, energy is diverted from learning to processing distress. Perceived non-implementation of feedback should not be confused with ignoring it; feedback that is not actioned often impacts learning.

    Matched MeSH terms: Learning
  14. Sayeed S, Ahmad AF, Peng TC
    F1000Res, 2022;11:17.
    PMID: 38269303 DOI: 10.12688/f1000research.73613.1
    The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. We experimented with different missing value imputation techniques and compared machine learning (ML) model performances with different imputation methods. We propose a hybrid model for missing value imputation combining ML and sample-based statistical techniques. Furthermore, we continued with the best missing value inputted dataset, chosen based on ML model performance for feature engineering and hyperparameter tuning. We used k-means clustering and principal component analysis. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, we used K-fold cross-validation.
    Matched MeSH terms: Machine Learning
  15. Myint K, See-Ziau H, Husain R, Ismail R
    Malays J Med Sci, 2016 May;23(3):49-56.
    PMID: 27418869
    An equitable and positive learning environment fosters deep self-directed learning in students and, consequently, good practice in their profession. Although demotivating weaknesses may lead to repeated day-to-day stress with a cascade of deleterious consequences at both personal and professional levels, a possible relationship between these parameters has not been reported. This study was undertaken to determine the relationship between students' perceptions of their educational environment and their stress levels.
    Matched MeSH terms: Learning
  16. Cheah YK
    Malays J Med Sci, 2014 Nov-Dec;21(6):36-44.
    PMID: 25897281 MyJurnal
    In the context of global increases in the prevalence of non-communicable diseases, the objective of the present study is to investigate the factors affecting individuals' decisions to use health-promoting goods and services.
    Matched MeSH terms: Learning
  17. Cheng HM
    Med Teach, 2010 Jan;32(1):91-2.
    PMID: 20104662
    Matched MeSH terms: Learning*
  18. Janes G
    Nurse Educ Pract, 2006 Mar;6(2):87-97.
    PMID: 19040861 DOI: 10.1016/j.nepr.2005.09.003
    This paper analyses the experience of one individual in the development and delivery of an innovative, undergraduate leadership development module. The module is accessed by practising health care professionals in Malaysia as part of a top-up Honours Degree and is delivered solely using a virtual learning environment (VLE), in this case Blackboard. The aim of this analysis is to contribute to the current body of knowledge regarding the use of VLE technology to facilitate learning at a distance. Of particular relevance is the paper's focus on: the drivers for e-learning; widening participation and increasing access; the experience of designing and delivering learning of relevance for this contemporary student population and evaluating the VLE experience/module. The development and delivery of this module is one result of a rapidly growing area of education. As a novice teacher in her first year in the higher education sector, this experience was a significant and stimulating challenge for a number of reasons and these are explored in greater depth. This is achieved by means of personal reflection using the phases of module development and delivery as a focus.
    Matched MeSH terms: Learning; Problem-Based Learning
  19. Win NN, Nadarajah VD, Win DK
    PMID: 25961676 DOI: 10.3352/jeehp.2015.12.17
    PURPOSE: Problem-based learning (PBL) is usually conducted in small-group learning sessions with approximately eight students per facilitator. In this study, we implemented a modified version of PBL involving collaborative groups in an undergraduate chiropractic program and assessed its pedagogical effectiveness.
    METHODS: This study was conducted at the International Medical University, Kuala Lumpur, Malaysia, and involved the 2012 chiropractic student cohort. Six PBL cases were provided to chiropractic students, consisting of three PBL cases for which learning resources were provided and another three PBL cases for which learning resources were not provided. Group discussions were not continuously supervised, since only one facilitator was present. The students' perceptions of PBL in collaborative groups were assessed with a questionnaire that was divided into three domains: motivation, cognitive skills, and perceived pressure to work.
    RESULTS: Thirty of the 31 students (97%) participated in the study. PBL in collaborative groups was significantly associated with positive responses regarding students' motivation, cognitive skills, and perceived pressure to work (P<0.05). The students felt that PBL with learning resources increased motivation and cognitive skills (P<0.001).
    CONCLUSION: The new PBL implementation described in this study does not require additional instructors or any additional funding. When implemented in a classroom setting, it has pedagogical benefits equivalent to those of small-group sessions. Our findings also suggest that students rely significantly on available learning resources.
    KEYWORDS: Chiropractic; Learning; Motivation; Perception; Problem-based learning
    Matched MeSH terms: Problem-Based Learning*
  20. Poon HK, Yap WS, Tee YK, Lee WK, Goi BM
    Neural Netw, 2019 Nov;119:299-312.
    PMID: 31499354 DOI: 10.1016/j.neunet.2019.08.017
    Document classification aims to assign one or more classes to a document for ease of management by understanding the content of a document. Hierarchical attention network (HAN) has been showed effective to classify documents that are ambiguous. HAN parses information-intense documents into slices (i.e., words and sentences) such that each slice can be learned separately and in parallel before assigning the classes. However, introducing hierarchical attention approach leads to the redundancy of training parameters which is prone to overfitting. To mitigate the concern of overfitting, we propose a variant of hierarchical attention network using adversarial and virtual adversarial perturbations in 1) word representation, 2) sentence representation and 3) both word and sentence representations. The proposed variant is tested on eight publicly available datasets. The results show that the proposed variant outperforms the hierarchical attention network with and without using random perturbation. More importantly, the proposed variant achieves state-of-the-art performance on multiple benchmark datasets. Visualizations and analysis are provided to show that perturbation can effectively alleviate the overfitting issue and improve the performance of hierarchical attention network.
    Matched MeSH terms: Machine Learning*
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