Displaying publications 1 - 20 of 986 in total

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  1. Display of Professionalism of the Highest Standard
    Sim JH, Hassan H
    Acad Med, 2018 01;93(1):11-12.
    PMID: 29278593 DOI: 10.1097/ACM.0000000000002007
    Matched MeSH terms: Learning*
  2. Fulltext Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms
    Murti MA, Junior R, Ahmed AN, Elshafie A
    Sci Rep, 2022 Dec 08;12(1):21200.
    PMID: 36482200 DOI: 10.1038/s41598-022-25098-1
    Earthquake is one of the natural disasters that have a big impact on society. Currently, there are many studies on earthquake detection. However, the vibrations that were detected by sensors were not only vibrations caused by the earthquake, but also other vibrations. Therefore, this study proposed an earthquake multi-classification detection with machine learning algorithms that can distinguish earthquake and non-earthquake, and vandalism vibration using acceleration seismic waves. In addition, velocity and displacement as integration products of acceleration have been considered additional features to improve the performances of machine learning algorithms. Several machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) have been used to develop the best algorithm for earthquake multi-classification detection. The results of this study indicate that the ANN algorithm is the best algorithm to distinguish between earthquake and non-earthquake, and vandalism vibrations. Moreover, it's also more resistant to various input features. Furthermore, using velocity and displacement as additional features has been proven to increase the performance of every model.
    Matched MeSH terms: Machine Learning*
  3. Flipped classrooms with proper scheduling improve learning engagement
    Govindan S, Kamath MG
    Educ Health (Abingdon), 2022;35(3):113-114.
    PMID: 37313894 DOI: 10.4103/efh.EfH_57_19
    Matched MeSH terms: Learning*
  4. Personalised learning in higher education for health sciences: a scoping review protocol
    Ali M, Wahab IBA, Huri HZ, Yusoff MS
    Syst Rev, 2024 Apr 02;13(1):99.
    PMID: 38566190 DOI: 10.1186/s13643-024-02478-4
    BACKGROUND: Personalised learning, an educational approach that tailors teaching and learning to individual needs and preferences, has gained attention in recent years, particularly in higher education. Advances in educational technology have facilitated the implementation of personalised learning in various contexts. Despite its potential benefits, the literature on personalised learning in health sciences higher education remains scattered and heterogeneous. This scoping review aims to identify and map the current literature on personalised learning in health sciences higher education and its definition, implementation strategies, benefits, and limitations.

    METHODS: A comprehensive search of electronic databases, PubMed, Scopus, Google Scholar, Educational Research Complete, and Journal Storage (JSTOR), will be conducted to identify relevant articles. The search will be limited to articles published in the English language between 2000 and 2023. The search strategy will be designed and adapted for each database using a combination of keywords and subject headings related to personalised learning and health sciences higher education. Eligibility criteria will be applied to screen and select articles. Data extraction and quality assessment will be performed, and thematic synthesis will be used to analyse the extracted data.

    DISCUSSION: The results of the scoping review will present a comprehensive and coherent overview of the literature on personalised learning in health sciences higher education. Key themes and topics related to personalised learning, its definitions, models, implementation strategies, benefits, and limitations, will be identified. The geographical and temporal distribution of research on personalised learning in health sciences higher education will also be described. This scoping review will provide a structured synthesis of the available evidence on personalised learning in health sciences higher education, highlighting potential gaps and areas for future research. The findings will contribute to ongoing scholarly and policy debates on personalised learning in higher education, informing the development of best practices, guidelines, and future research agendas.

    Matched MeSH terms: Learning*
  5. Culture of reflection through case scenarios reversing the Bloom Learning Framework
    Sivakumar I, Arunachalam S, Buzayan MM, Chidambaram R
    J Dent Educ, 2022 Dec;86 Suppl 3:1742-1744.
    PMID: 35412660 DOI: 10.1002/jdd.12945
    Matched MeSH terms: Learning*
  6. Fulltext Time-sequential graph adversarial learning for brain modularity community detection
    Gong C, Xue B, Jing C, He CH, Wu GC, Lei B, et al.
    Math Biosci Eng, 2022 Sep 13;19(12):13276-13293.
    PMID: 36654046 DOI: 10.3934/mbe.2022621
    Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.
    Matched MeSH terms: Learning*
  7. An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models
    Jalil-Masir H, Fattahi R, Ghanbari-Adivi E, Asadi Aghbolaghi M, Ehteram M, Ahmed AN, et al.
    Environ Sci Pollut Res Int, 2022 Sep;29(44):67180-67213.
    PMID: 35522411 DOI: 10.1007/s11356-022-20472-y
    Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR-vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D50/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D50/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.
    Matched MeSH terms: Learning*
  8. The Eight Stages of Trust and "Amanah" in Medicine and the Dunning-Kruger effect
    Abdullah JM
    Malays J Med Sci, 2014 Jul;21(4):1-3.
    PMID: 25977614
    The Dunning-Kruger effect is a cognitive bias in which unskilled people make poor decisions and reach erroneous conclusions, but their incompetence denies them the metacognitive ability to recognise their mistakes. These unskilled people therefore suffer from illusory superiority, rating their ability as above average, much higher than it actually is, while the highly skilled underrate their own abilities, suffering from illusory inferiority.
    Matched MeSH terms: Learning
  9. Fulltext What makes a great lecture? Use of lectures in a hybrid PBL curriculum
    Azer SA
    Kaohsiung J. Med. Sci., 2009 Mar;25(3):109-15.
    PMID: 19419915 DOI: 10.1016/S1607-551X(09)70049-X
    Lectures are of great value to students. However, with the introduction of hybrid problem-based learning (PBL) curricula into most medical schools, the emphasis on lectures has decreased. This paper discusses how lectures can be used in a PBL curriculum, what makes a great lecture, and how to deliver a lecture that fits with these changes.
    Matched MeSH terms: Problem-Based Learning*
  10. Fulltext Notes for primary care teachers: Self-directed learning: What it is, and how to promote it
    Nurjahan MI
    Malays Fam Physician, 2008;3(1):45-50.
    PMID: 25606112
    Matched MeSH terms: Learning*
  11. Fulltext A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression
    Chughtai JU, Haq IU, Islam SU, Gani A
    Sensors (Basel), 2022 Dec 12;22(24).
    PMID: 36560104 DOI: 10.3390/s22249735
    Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
    Matched MeSH terms: Machine Learning*
  12. Co-design in preclinical skill-based learning: A pandemic plan for continuity and reinforcement
    Saxena K, Arunachalam S, Banavar SR, Dhanaraj S
    J Dent Educ, 2022 Dec;86 Suppl 3:1731-1733.
    PMID: 34958485 DOI: 10.1002/jdd.12872
    Matched MeSH terms: Learning*
  13. Review on machine learning-based bioprocess optimization, monitoring, and control systems
    Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, et al.
    Bioresour Technol, 2023 Feb;370:128523.
    PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523
    Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
    Matched MeSH terms: Machine Learning*
  14. Application of machine learning on understanding biomolecule interactions in cellular machinery
    Dixit R, Khambhati K, Supraja KV, Singh V, Lederer F, Show PL, et al.
    Bioresour Technol, 2023 Feb;370:128522.
    PMID: 36565819 DOI: 10.1016/j.biortech.2022.128522
    Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
    Matched MeSH terms: Machine Learning*
  15. Fulltext An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms
    Anbananthen KSM, Subbiah S, Chelliah D, Sivakumar P, Somasundaram V, Velshankar KH, et al.
    F1000Res, 2021;10:1143.
    PMID: 34987773 DOI: 10.12688/f1000research.73009.1
    Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset.  The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.
    Matched MeSH terms: Machine Learning*
  16. Fulltext Credit card fraud detection using a hierarchical behavior-knowledge space model
    Nandi AK, Randhawa KK, Chua HS, Seera M, Lim CP
    PLoS One, 2022;17(1):e0260579.
    PMID: 35051184 DOI: 10.1371/journal.pone.0260579
    With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.
    Matched MeSH terms: Machine Learning*
  17. Fulltext BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients
    Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, et al.
    Sci Rep, 2023 Dec 18;13(1):22874.
    PMID: 38129433 DOI: 10.1038/s41598-023-48486-7
    Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
    Matched MeSH terms: Machine Learning*
  18. SCL: Self-supervised contrastive learning for few-shot image classification
    Lim JY, Lim KM, Lee CP, Tan YX
    Neural Netw, 2023 Aug;165:19-30.
    PMID: 37263089 DOI: 10.1016/j.neunet.2023.05.037
    Few-shot learning aims to train a model with a limited number of base class samples to classify the novel class samples. However, to attain generalization with a limited number of samples is not a trivial task. This paper proposed a novel few-shot learning approach named Self-supervised Contrastive Learning (SCL) that enriched the model representation with multiple self-supervision objectives. Given the base class samples, the model is trained with the base class loss. Subsequently, contrastive-based self-supervision is introduced to minimize the distance between each training sample with their augmented variants to improve the sample discrimination. To recognize the distant sample, rotation-based self-supervision is proposed to enable the model to learn to recognize the rotation degree of the samples for better sample diversity. The multitask environment is introduced where each training sample is assigned with two class labels: base class label and rotation class label. Complex augmentation is put forth to help the model learn a deeper understanding of the object. The image structure of the training samples are augmented independent of the base class information. The proposed SCL is trained to minimize the base class loss, contrastive distance loss, and rotation class loss simultaneously to learn the generic features and improve the novel class performance. With the multiple self-supervision objectives, the proposed SCL outperforms state-of-the-art few-shot approaches on few-shot image classification benchmark datasets.
    Matched MeSH terms: Learning*
  19. Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning
    Alymann AA, Alymann IA, Ong SQ, Rusli MU, Ahmad AH, Salim H
    Sci Data, 2024 Apr 05;11(1):337.
    PMID: 38580692 DOI: 10.1038/s41597-024-03172-9
    Reliable sex identification in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confirmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, offering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures.
    Matched MeSH terms: Machine Learning
  20. Social influence on the expression of robbing and bartering behaviours in Balinese long-tailed macaques
    Brotcorne F, Holzner A, Jorge-Sales L, Gunst N, Hambuckers A, Wandia IN, et al.
    Anim Cogn, 2020 Mar;23(2):311-326.
    PMID: 31820148 DOI: 10.1007/s10071-019-01335-5
    Animals use social information, available from conspecifics, to learn and express novel and adaptive behaviours. Amongst social learning mechanisms, response facilitation occurs when observing a demonstrator performing a behaviour temporarily increases the probability that the observer will perform the same behaviour shortly after. We studied "robbing and bartering" (RB), two behaviours routinely displayed by free-ranging long-tailed macaques (Macaca fascicularis) at Uluwatu Temple, Bali, Indonesia. When robbing, a monkey steals an inedible object from a visitor and may use this object as a token by exchanging it for food with the temple staff (bartering). We tested whether the expression of RB-related behaviours could be explained by response facilitation and was influenced by model-based biases (i.e. dominance rank, age, experience and success of the demonstrator). We compared video-recorded focal samples of 44 witness individuals (WF) immediately after they observed an RB-related event performed by group members, and matched-control focal samples (MCF) of the same focal subjects, located at similar distance from former demonstrators (N = 43 subjects), but in the absence of any RB-related demonstrations. We found that the synchronized expression of robbing and bartering could be explained by response facilitation. Both behaviours occurred significantly more often during WF than during MCF. Following a contagion-like effect, the rate of robbing behaviour displayed by the witness increased with the cumulative rate of robbing behaviour performed by demonstrators, but this effect was not found for the bartering behaviour. The expression of RB was not influenced by model-based biases. Our results support the cultural nature of the RB practice in the Uluwatu macaques.
    Matched MeSH terms: Social Learning*; Learning
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