Displaying publications 1 - 20 of 530 in total

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  1. Sim JH, Hassan H
    Acad Med, 2018 01;93(1):11-12.
    PMID: 29278593 DOI: 10.1097/ACM.0000000000002007
    Matched MeSH terms: Learning*
  2. 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
  3. 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*
  4. 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
  5. Motlagh O, Papageorgiou E, Tang S, Zamberi Jamaludin
    Sains Malaysiana, 2014;43:1781-1790.
    Soft computing is an alternative to hard and classic math models especially when it comes to uncertain and incomplete data. This includes regression and relationship modeling of highly interrelated variables with applications in curve fitting, interpolation, classification, supervised learning, generalization, unsupervised learning and forecast. Fuzzy cognitive map (FCM) is a recurrent neural structure that encompasses all possible connections including relationships among inputs, inputs to outputs and feedbacks. This article examines a new methods for nonlinear multivariate regression using fuzzy cognitive map. The main contribution is the application of nested FCM structure to define edge weights in form of meaningful functions rather than crisp values. There are example cases in this article which serve as a platform to modelling even more complex engineering systems. The obtained results, analysis and comparison with similar techniques are included to show the robustness and accuracy of the developed method in multivariate regression, along with future lines of research.
    Matched MeSH terms: Supervised Machine Learning; Unsupervised Machine Learning; Learning
  6. Ammu K. Radhakrishnan
    MyJurnal
    In the last decade or so, Medical education all over the world has been inundated with innovations in education, which include innovations in curricular design, delivery as well as assessments. There is a need to reflect on the effectives of these innovations
    on the learner. Hence the theme chosen for the 2009 International Medical Education Conference (IMEC 2009) was “Reflections on Innovations”. The Organising Committee felt that it was timely for medical educators everywhere to reflect and evaluate the effect of the many innovations adopted by their schools. (Copied from article)
    Matched MeSH terms: Learning
  7. Siti Nurma Hanim Hadie, Asma' Hassan, Zul Izhar Mohd Ismail, Mohd Asnizam Asari, Aaijaz Ahmed Khan, Fazlina Kasim, et al.
    MyJurnal
    Anatomy is an important knowledge for medical practice. Insufficient anatomy knowledge leading to errors in identification of anatomical structures during medical practices has been reported in many countries. Many medical students seem to have difficulties in learning anatomy and retaining the knowledge for future practice, thus this might reflect the possible flaws in anatomy education. In order to achieve optimum anatomy education environment and to close the gaps in education, measuring the students' perception on anatomy teaching and learning is a pre-emptive measure needed by educationists. At present, there is no valid and reliable inventory available to specifically evaluate the anatomy education environment. Therefore, this article highlights the importance of having such inventory.
    Matched MeSH terms: Learning
  8. Muhamad Saiful Bahri Yusof
    MyJurnal
    This paper describes a systematic and practical guide on manuscript writing. A step-by-step approach
    as easy as learning ABC to facilitate authors to plan their manuscript writing. Research has shown that
    experienced writers plan extensively, in which a writing plan is a road map, without it we will probably
    lose our way in circles. Generally, authors start writing a manuscript by introduction, methods, results,
    discussion and conclusion. However, this paper proposes a different approach to start writing a
    manuscript based on the ABC of manuscript writing worksheet.
    Matched MeSH terms: Learning
  9. Zurni Omar, Mohamed Suleiman
    A new method called parallel R-point explicit block method for solving a single equation of higher order ordinary differential equation directly using a constant step size is developed. This method calculates the numerical solution at R point simultaneously is parallel in nature. Computational advantages are presented by comparing the results obtained with the new method with that of the conventional 1-point method. The numerical results show that the new method reduces the total number of steps and execution time. The accuracy of the parallel block and the conventional 1-point methods is comparable particularly when finer step sizes are used.
    Matched MeSH terms: Learning
  10. Zanariah Abdul Majid, Mohamed Suleiman
    Sains Malaysiana, 2011;40:1179-1186.
    Predictor-corrector two point block methods are developed for solving first order ordinary differential equations (ODEs) using variable step size. The method will estimate the solutions of initial value problems (IVPs) at two points simultaneously. The existence multistep method involves the computations of the divided differences and integration coefficients when using the variable step size or variable step size and order. The block method developed will be presented as in the form of Adams Bashforth - Moulton type and the coefficients will be stored in the code. The efficiency of the predictor-corrector block method is compared to the standard variable step and order non block multistep method in terms of total number of steps, maximum error, total function calls and execution times.
    Matched MeSH terms: Learning
  11. Pandey P
    Sains Malaysiana, 2014;43:1105-1108.
    In this paper, we present a new method for solving nonlinear general two point boundary value problems. A method based on finite differences and rational function approximation and we call this method as rational approximation method. A rational approximation method is applied to construct the numerical solution for two point boundary value problems. The novel method is tested on three model problems. Thus the numerical results obtained for these model problems show the performance and efficiency of the developed method.
    Matched MeSH terms: Learning
  12. Salam A, Mohamad N, Siraj HH, Kamarudin MA, Yaman MN, Bujang SM
    Natl Med J India, 2014 Nov-Dec;27(6):350.
    PMID: 26133346
    Matched MeSH terms: Learning*
  13. Nitce Isa Medina Machmudi Isa, Mai Shihah Hj Abdullah
    MyJurnal
    Selaras dengan ledakan pengetahuan berasaskan komputer dan teknologi pada abad ke-21, kaedah Pembelajaran Berasaskan Projek (PBP) atau Project Based Learning (PBL) telah diperkenalkan oleh Kementerian Pelajaran Malaysia (KPM) pada tahun 2006. Pada awalnya, implementasi kaedah PBP telah dimulakan di sekolah-sekolah bestari perdana. Ini merupakan tinjauan literatur yang membincangkan takrifan dan teori yang digunapakai dalam kaedah PBP. Selain itu, perbandingan kaedah PBP dengan kaedah Pembelajaran Berasaskan Masalah (PBM) atau Problem Based Learning (PBL) turut dibincangkan memandangkan kedua-dua kaedah ini menggunakan akronim yang sama dalam bahasa Inggeris. Berdasarkan tinjauan literatur ini, didapati bahawa kaedah PBP mempunyai kelebihan dan kelemahan yang tersendiri, maka terpulang kepada budi bicara guru yang mengajar untuk mengaplikasikan kaedah ini dalam pengajaran dan pembelajaran (P dan P) bersesuaian dengan kebolehan murid.
    Matched MeSH terms: Learning; Problem-Based Learning
  14. Sinniah, Davendralingam
    MyJurnal
    Thinking is something that we do all through our lives - an activity thcit possibly antedates our very birth itself Yet our children and we are not told about thinking or taught about the thinking process that dominates our lives, possibly, because of our own limited under-standing. Consequently, children are told to be logical and are discouraged from thinking differently, because it is the only type of think-ing we know and can understand. Methods of assessing their performance based on logical thinking underestimate their true potentials. The creative potentials of these children, 40% of who are right-brained need to be harnessed by approaches to learning that utilize methods of teaching and assessment, appropriate for their style of thinking. Another group of children, who need special attention, are those with learning disabilities that have been ignored, but can be corrected with appropriate programmes that provide a comprehensive approach to regular and special education.
    Matched MeSH terms: Learning; Learning Disorders
  15. Azer, Samy A.
    Medical Health Reviews, 2008;2008(1):81-95.
    MyJurnal
    With the introduction of problem-based learning (PBL) in medical and health professionals’ undergraduate courses, self-directed learning (also known as self-regulated learning) becomes an integral component of the learning process. There may be slight variations in how educators and students perceive self-directed learnin .However, self-directed learning provides an opportunity for collaborative discussion of the new information collected and allows learners to construct new knowledge as they address their learning issues. Therefore, self-directed learning is not just about researching for new knowledge or finding answers for questions; self-directed learning is about developing competencies, skills and attitudes that foster the learning processes. Interestingly, not all learners will be able to adapt this approach of learning once they enroll in a PBL course. The process will develop gradually and require a number of actions from the learner, including: (i) Realising the need to change their learning style to suite the needs of the medical curriculum, (ii) constructing a plan that accommodates the new learning objectives, (iii) Practicing self-directed learning and sharing their experiences with peers, and (iv) Continuing evaluation of their self-directed learning approach and improving their learning style. Therefore, the aims of this manuscript are: (i) discuss the meaning of self-directed learning in the context of PBL, and review the research outcomes in this area, (ii) understand the different factors that may affect student’s self-directed learning strategies, and (iii) briefly explore the meaning of construction of knowledge and how it can enforce students’ self-directed learning, integration of knowledge and deeper understanding of topics learnt.
    Matched MeSH terms: Learning; Problem-Based Learning
  16. Albadr MAA, Tiun S, Al-Dhief FT, Sammour MAM
    PLoS ONE, 2018;13(4):e0194770.
    PMID: 29672546 DOI: 10.1371/journal.pone.0194770
    Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.
    Matched MeSH terms: Machine Learning*
  17. Yang T, Xiao Y, Zhang Z, Liang Y, Li G, Zhang M, et al.
    Sci Rep, 2018 09 28;8(1):14518.
    PMID: 30266999 DOI: 10.1038/s41598-018-32757-9
    Soft robots driven by stimuli-responsive materials have their own unique advantages over traditional rigid robots such as large actuation, light weight, good flexibility and biocompatibility. However, the large actuation of soft robots inherently co-exists with difficulty in control with high precision. This article presents a soft artificial muscle driven robot mimicking cuttlefish with a fully integrated on-board system including power supply and wireless communication system. Without any motors, the movements of the cuttlefish robot are solely actuated by dielectric elastomer which exhibits muscle-like properties including large deformation and high energy density. Reinforcement learning is used to optimize the control strategy of the cuttlefish robot instead of manual adjustment. From scratch, the swimming speed of the robot is enhanced by 91% with reinforcement learning, reaching to 21 mm/s (0.38 body length per second). The design principle behind the structure and the control of the robot can be potentially useful in guiding device designs for demanding applications such as flexible devices and soft robots.
    Matched MeSH terms: Machine Learning*
  18. Al-Khaleefa AS, Ahmad MR, Isa AAM, Esa MRM, Aljeroudi Y, Jubair MA, et al.
    Sensors (Basel), 2019 May 25;19(10).
    PMID: 31130657 DOI: 10.3390/s19102397
    Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.
    Matched MeSH terms: Machine Learning; Learning
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