Displaying publications 1 - 20 of 908 in total

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  1. De Meyer H, Tripp G, Beckers T, van der Oord S
    Res Child Adolesc Psychopathol, 2021 09;49(9):1165-1178.
    PMID: 33792820 DOI: 10.1007/s10802-021-00781-5
    When children with ADHD are presented with behavioral choices, they struggle more than Typically Developing [TD] children to take into account contextual information necessary for making adaptive choices. The challenge presented by this type of behavioral decision making can be operationalized as a Conditional Discrimination Learning [CDL] task. We previously showed that CDL is impaired in children with ADHD. The present study explores whether this impairment can be remediated by increasing reward for correct responding or by reinforcing correct conditional choice behavior with situationally specific outcomes (Differential Outcomes). An arbitrary Delayed Matching-To-Sample [aDMTS] procedure was used, in which children had to learn to select the correct response given the sample stimulus presented (CDL). We compared children with ADHD (N = 45) and TD children (N = 49) on a baseline aDMTS task and sequentially adapted the aDMTS task so that correct choice behavior was rewarded with a more potent reinforcer (reward manipulation) or with sample-specific (and hence response-specific) reinforcers (Differential Outcomes manipulation). At baseline, children with ADHD performed significantly worse than TD children. Both manipulations (reward optimization and Differential Outcomes) improved performance in the ADHD group, resulting in a similar level of performance to the TD group. Increasing the reward value or the response-specificity of reinforcement enhances Conditional Discrimination Learning in children with ADHD. These behavioral techniques may be effective in promoting the learning of adaptive behavioral choices in children with ADHD.
    Matched MeSH terms: Learning
  2. Jalina Karim, Nabishah Mohamad, John HV Gilbert, Ismail Saibon, Subhan Thamby Mohd Meerah, Hamidah Hassan, et al.
    MyJurnal
    Introduction: Teaching strategy for nursing students need to be varied for the future preparation and to increase confident level in delivering quality care to patients. Interprofessional learning (IPL) is a way to encourage collaboration among health professional teams that will drive them to collaborate with, from and about other profession and thus, it allow students to have greater knowledge. Currently, they are unable to learn together during the clinical posting due to professional boundaries. Objective: To explore nursing student knowledge and perception on interprofessional learning. Method: This paper presents a focus group discussion with a group of nursing students (n= 8). A semi structured guide was used and focused on knowledge, experiences and benefit related to IPL. Result: Data was analysed and four major themes emerged; 1. learning with, from and about other health professionals, 2. communication skills, 3. teamwork and 4. future preparation. Conclusion: This study suggested that the interprofessional learning in the teaching and learning strategy should be introduced to the nursing students as to involve them with interprofessional learning and extend their understanding on other health professionals roles. In addition, it is an opportunity for them to work collaboratively with other health professionals.
    Matched MeSH terms: Learning
  3. Rasidah Abd Wahab, Zunika Amit
    MyJurnal
    The significance of learning research methodology and performing research has been accepted by various medical schools in Malaysia as well as in other countries. The aim of integrating research into medical curriculum is to inculcate the research culture and form part of the evidence-based practice among medical professionals. Hence, the Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak has incorporated the research component into the preclinical year of the medical curriculum. A survey was conducted to gauge the second year medical students' level of knowledge of research process at the end of the course using a set of questionnaires. Seventy nine of second year medical students participated in the study. The outcome of the study shows significant improvement in the students’ knowledge on research components after completing the one year course (p
    Matched MeSH terms: Learning
  4. Albahri OS, Al-Obaidi JR, Zaidan AA, Albahri AS, Zaidan BB, Salih MM, et al.
    Comput Methods Programs Biomed, 2020 Nov;196:105617.
    PMID: 32593060 DOI: 10.1016/j.cmpb.2020.105617
    CONTEXT: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19.

    OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

    METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.

    RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.

    DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.

    Matched MeSH terms: Machine Learning
  5. Azizul Isha, Nor Azah Yusof, Musa Ahmad, Dedy Suhendra, Wan Md. Zin Wan Yunus, Zulkarnain Zainal
    MyJurnal
    An artificial neural network (ANN) was applied for the determination of V(V) based on immobilized fatty hydroxamic acid (FHA) in poly(methyl methacrylate) (PMMA). Spectra obtained from the V(V)-FHA complex at single wavelengths was used as the input data for the ANN. The V(V)-FHA complex shows a limited linear dynamic range of V(V) concentration of 10 - 100 mg/ L. After training with ANN, the linear dynamic range was extended with low calibration error. A three layer feed forward neural network using backpropagation (BP) algorithm was employed in this study. The input layer consisted of single neurons, 30 neurons in hidden a layer and one output neuron was found appropriate for the multivariate calibration used. The network were trained up to 10000 epochs with 0.003 % learning rate. This reagent also provided a good analytical pedormance with reproducibility characters of the method yielding relative standard deviation (RSD) of 9.29% and 7.09% for V(V) at concentrations of 50 mg/ L and 200 mg/ L, respectively. The limit of detection of the method was 8.4 mg/ L.
    Matched MeSH terms: Learning
  6. Sultan G, Zubair S
    Comput Biol Chem, 2024 Feb;108:107999.
    PMID: 38070457 DOI: 10.1016/j.compbiolchem.2023.107999
    Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.
    Matched MeSH terms: Machine Learning
  7. Alkhamis MA, Al Jarallah M, Attur S, Zubaid M
    Sci Rep, 2024 Jan 12;14(1):1243.
    PMID: 38216605 DOI: 10.1038/s41598-024-51604-8
    The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
    Matched MeSH terms: Machine Learning
  8. Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS
    PMID: 38394882 DOI: 10.1016/j.saa.2024.124063
    Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
    Matched MeSH terms: Machine Learning
  9. Chang KY, Riley WJ, Knox SH, Jackson RB, McNicol G, Poulter B, et al.
    Nat Commun, 2021 04 15;12(1):2266.
    PMID: 33859182 DOI: 10.1038/s41467-021-22452-1
    Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.
    Matched MeSH terms: Machine Learning
  10. Tang BH, Guan Z, Allegaert K, Wu YE, Manolis E, Leroux S, et al.
    Clin Pharmacokinet, 2021 11;60(11):1435-1448.
    PMID: 34041714 DOI: 10.1007/s40262-021-01033-x
    BACKGROUND: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.

    OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

    METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.

    RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.

    CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

    Matched MeSH terms: Machine Learning
  11. 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
  12. Jamiaton Kusrin, Mohamad Nizam Mohamed Shapie, Sharifah Aliman, Faridah Mohamad Halil, Zarrul Hayat Mohd Yusof
    Jurnal Inovasi Malaysia, 2018;2(1):105-116.
    MyJurnal
    Physical Education (PE) teachers in schools have difficulties in attracting students to participate in activities that cause students to feel bored to play during the PE class due to the short time of teaching and the number of games to be learned in various ways. This study aims to evaluate the effectiveness of teaching methods based on the TGfU model towardssports science students through the Volleyball Camp 2016 (KBT2016). KBT2016 involves the Faculty of Sports & Recreation Science (FSR), UiTM in collaboration with SMK Puncak Alam. The respondents were selected based on sampling involving 31 Sports Science students taking the Acquisition of Movement Skills (SPS465) subject. Respondents were divided into four groups: two male groups and two female groups. KBT2016 received positive feedbacks from respondents. All respondents were able to learn the basics skills of volleyball with the right techniques in exciting situations.
    Matched MeSH terms: Learning
  13. Jie Z, Roslan S, Muhamad MM, Md Khambari MN, Zaremohzzabieh Z
    Int J Environ Res Public Health, 2022 Oct 15;19(20).
    PMID: 36293911 DOI: 10.3390/ijerph192013323
    (1) Background: The influence of academic boredom and intrinsic motivation on students' learning and achievements is receiving more attention from scholars. Nevertheless, studies on how intervention decreases academic boredom and promotes intrinsic motivation during study remain unexplored. (2) Purpose: The purpose of this study is to investigate whether positive education intervention based on the PERMA model would help Chinese college students with learning-related academic boredom, class-related academic boredom, and intrinsic motivation. (3) Methods: This study is quasi-experimental research with a control group including pre-test and post-test. The study was conducted with 173 students, including 86 (n1 = 86) experimental and 87 (n2 = 87) control group students. (4) Results: Results revealed that students in the intervention condition reported significant reductions in learning-related academic boredom and class-related academic boredom, and significant increases in intrinsic motivation in comparison to their counterparts in the control group. (5) Conclusions: These findings indicate that positive education intervention for college students is a promising approach to reducing academic boredom and increasing intrinsic motivation among Chinese college students.
    Matched MeSH terms: Learning
  14. 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
  15. Zainul Ibrahim Zainuddin
    MyJurnal
    This paper presents a conceptual approach to the integration of Islamic perspectives into a Medical Imaging Curriculum to the concept of Outcome-Based Education (OBE). This work is seen within the context of harmonising Islamic principles to a currently accepted concept in education. Although there have been discussions that question the concept of OBE, this paper contends that the integration can benefit from the practicality aspect of OBE. This can reduce the complexities and fatigue in addressing the integration using an educational approach that is different to that being applied to the human sciences. This paper features the main elements in OBE in the form of Islamic programme educational objectives, Islamic programme outcomes, and Islamic domain learning outcomes. The justification to use domain learning outcomes instead of course learning outcome is given. The teaching and learning strategies, as well as the assessment, are examined through a lens that serves to provide a desirable, practical and holistic model of Islamic integration. It is felt that the currently accepted teaching and assessment methodologies can be adapted for the integration exercise. This work also highlights two often overlooked elements of OBE; teacher and student characteristics. The various terminologies that describe the Islamic teacher characteristics and the differences in student learning styles and preferences are presented. Furthermore, suggestions are made to align the assessment of the integration to various taxonomies of learning, with the aim in evaluating the internalisation of the Islamic essences. This work contents that a holistic approach towards integration of Islamic perspectives into Medical Imaging curriculum can be realised.
    Matched MeSH terms: Learning
  16. Abdulrauf Sharifai G, Zainol Z
    Genes (Basel), 2020 06 27;11(7).
    PMID: 32605144 DOI: 10.3390/genes11070717
    The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.
    Matched MeSH terms: Machine Learning*
  17. Syed,A,B,S,S,, Rahim,M, I,, Zainal,A,Dz,
    Jurnal Inovasi Malaysia, 2019;1(1):33-42.
    MyJurnal
    ABSTRACT
    Many language learners face difficulties in applying and using the parts
    of speech in a given situation. It has become a challenge for teachers to
    navigate a grammar lesson in a creative way that can help learners to
    understand and apply the concepts most appropriately. Thus, POS-UP
    designed as a supplementary teaching tool to allow language learners to
    practice using the most suitable parts of speech in a sentence. Targeted at
    English language learners, POS-UP is a game which provides players with
    the opportunity to learn details of a lesson using poems written by local
    poets. In this game, language learners must complete a poem by using
    words with the correct parts of speech. In making sure that this game reaches
    its objectives and fulfils the needs of the students, a survey was carried out
    to find out the perceptions of English language learners towards POS-UP.
    The result shows that learners who play this game find it enjoyable and
    helpful in enhancing their understanding of parts of speech. It is hoped
    that POS-UP will be a useful supplementary teaching tool for teachers in
    the classroom. It is also expected that students will build an appreciation
    towards poems written by local poets.
    Matched MeSH terms: Learning
  18. Siow, LR, Naresh G, Nik Ritza Kosai, Harunarashid H, Sutton, PA, Zainal AA
    MyJurnal
    The incidence of varicose veins and the need for treatment has shown a tremendous increase over the years. Debilitating venous ulcers and dragging edemas of the lower limb with overall improvement in cosmetic results and availability of endovenous procedures has brought many patients forward for treatment. Continuous-wave handheld Doppler usage is limited by its diagnostic capabilities, thus the need to determine its real effectiveness. Benefits of using hand-held dopplers lies in its rapidity in assessment of patients, it's low running cost and short learning curve. This is important as duplex ultrasounds are not readily available in district hospitals. This study aims to determine the clinical effectiveness of hand-held continuous wave dopplers in the local setting especially in primary uncomplicated varicose articles veins. All electively referred patients with primary uncomplicated varicose veins who were referred to the Varicose Vein Clinic were evaluated with continuous-wave handheld Doppler (CWD) and duplex ultrasound (DUS) examination. The study duration was from the 1st of July to 31st of August 2013 (2 months). All patients in the study were independently evaluated with CWD and DUS in the clinic on the same day after adequate rest time. DUS was taken as the gold standard for evaluation of CWD specificity and sensitivity. The Chi-square and T-test was used to test for statistical significance. A total of 41 patients were evaluated in this study. The specificity of CWD when compared to DUS for diagnosing Sapheno-femoral junction (SFJ) was 100% and at the Sapheno-popliteal junction (SPJ) was 87%. Meanwhile sensitivity of CWD for SFJ was 75% and SPJ was 60%. The examination time with CWD was significantly faster than when compared with DUS examination with significant faster tracing times that can be achieved with CWD. CWD also significantly shorter reflux times when compared to DUS. Continuous-wave handheld doppler proves to be an indispensable clinical tool in the evaluation of SFJ and SPJ reflux in varicose veins. CWD assessment in this study was shown to be equal if not better for evaluating reflux when compared to DUS assessment for SFJ reflux. Main advantages for CWD also lie in its low running cost, rapidity in assessment and short learning curve when compared to duplex ultrasound examinations.
    Matched MeSH terms: Learning Curve
  19. Mohd Tahir Ismail, Zaidi Isa
    Sains Malaysiana, 2006;35:55-62.
    The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time series models are typically designed to accommodate these nonlinear features in the data. In this paper, we use portmanteau test and structural change test to detect nonlinear feature in three ASEAN countries exchange rates (Malaysia, Singapore and Thailand). It is found that the null hypothesis of linearity is rejected and there is evidence of structural breaks in the exchange rates series. Therefore, the decision of using regime switching model in this study is justified. Using model selection criteria (AIC, SBC, HQC), we compare the in-sample fitting between two types of regime switching model. The two regime switching models we considered were the Self-Exciting Threshold Autoregressive (SETAR) model and the Markov switching Autoregressive (MS-AR) model where these models can explain the abrupt changes in a time series but differ as how they model the movement between regimes. From the AIC, SBC and HQC values, it is found that the MS -AR model is the best fitted model for all the return series. In addition, the regime switching model also found to perform better than simple autoregressive model in in-sample fitting. This result justified that nonlinear model give better in-sample fitting than linear model.
    Matched MeSH terms: Learning
  20. Zabidi Azhar Mohd. Hussin
    MyJurnal
    Learning disability occurs in 10-15% of children. It is manifested by an imperfect ability to listen, think, speak, read, write, spell, calculate or interact. It may be specific as in dyslexia, dyscalculia, dysgraphia or nonspecific learning disability. In the latter group, there may be under-achievement despite average or above-average-intelligence, slow learners and mental retardation. Factors that may cause learning disability include genetic abnormalities, antenatal and perinatal insults, abnormal growth and malnutrition in early childhood, parental mode of upbringing, poor opportunity for learning, physical illness and emotional and social problems. Meticulous history taking and physical examination is important to arrive at a proper diagnosis so that the most appropriate management is given, often involving professionals working as a team.
    Matched MeSH terms: Learning; Learning Disorders
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