Displaying publications 101 - 120 of 252 in total

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  1. Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
    Fu M, Le C, Fan T, Prakapovich R, Manko D, Dmytrenko O, et al.
    Environ Sci Pollut Res Int, 2021 Dec;28(45):64818-64829.
    PMID: 34318419 DOI: 10.1007/s11356-021-15574-y
    The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
    Matched MeSH terms: Forecasting
  2. Fulltext Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models
    Lim MC, Singh S, Lai CH, Gill BS, Kamarudin MK, Md Zamri ASS, et al.
    Epidemiol Health, 2023;45:e2023093.
    PMID: 37905314 DOI: 10.4178/epih.e2023093
    OBJECTIVES: This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak.

    METHODS: SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison.

    RESULTS: In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario).

    CONCLUSIONS: Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia.

    Matched MeSH terms: Forecasting
  3. Educating medical students in oral health care: current curriculum and future needs of institutions in Malaysia and Australia
    Ahmad MS, Abuzar MA, Razak IA, Rahman SA, Borromeo GL
    Eur J Dent Educ, 2017 Nov;21(4):e29-e38.
    PMID: 27273317 DOI: 10.1111/eje.12211
    Poor oral health has been associated with compromised general health and quality of life. To promote comprehensive patient management, the role of medical professionals in oral health maintenance is compelling, thus indicating the need for educational preparation in this area of practice. This study aimed to determine the extent of training in oral health in Malaysian and Australian medical schools. An audio-recorded semi-structured phone interview involving Academic Programme Directors in Malaysian (n = 9, response rate=81.8%) and Australian (n = 7, response rate = 35.0%) medical schools was conducted during the 2014/2015 and 2014 academic years, respectively. Qualitative data was analysed via thematic analysis, involving coding and grouping into emerging themes. Quantitative data were measured for frequencies. It was found that medical schools in Malaysia and Australia offered limited teaching of various oral health-related components that were mostly integrated throughout the curriculum, in the absence of structured learning objectives, teaching methodologies and assessment approaches. Barriers to providing oral health education included having insufficient expertise and overloaded curriculum. As medical educators demonstrated support for oral health education, collaboration amongst various stakeholders is integral to developing a well-structured curriculum and practice guidelines on oral health management involving medical professionals.
    Matched MeSH terms: Forecasting
  4. Fulltext Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
    Shabri A, Samsudin R
    ScientificWorldJournal, 2014;2014:854520.
    PMID: 24895666 DOI: 10.1155/2014/854520
    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
    Matched MeSH terms: Forecasting
  5. Teledentistry in practice: literature review
    Khan SA, Omar H
    Telemed J E Health, 2013 Jul;19(7):565-7.
    PMID: 23672799 DOI: 10.1089/tmj.2012.0200
    Teledentistry can be defined as the remote provision of dental care, advice, or treatment through the medium of information technology, rather than through direct personal contact with any patient(s) involved. Within dental practice, teledentistry is used extensively in disciplines like preventive dentistry, orthodontics, endodontics, oral surgery, periodontal conditions, detection of early dental caries, patient education, oral medicine, and diagnosis. Some of the key modes and methods used in teledentistry are electronic health records, electronic referral systems, digitizing images, teleconsultations, and telediagnosis. All the applications used in teledentistry aim to bring about efficiency, provide access to underserved population, improve quality of care, and reduce oral disease burden.
    Matched MeSH terms: Forecasting
  6. Fulltext Asia Cohort Consortium: challenges for collaborative research
    Song M, Rolland B, Potter JD, Kang D
    J Epidemiol, 2012;22(4):287-90.
    PMID: 22672913 DOI: 10.2188/jea.je20120024
    In this era of chronic diseases, large studies are essential in investigating genes, environment, and gene-environment interactions as disease causes, particularly when associations are important but not strong. Moreover, to allow expansion and generalization of the results, studies should be conducted in populations outside Western countries. Here, we briefly describe the Asia Cohort Consortium (ACC), a collaborative cancer cohort research project that was first proposed in 2004 and now involves more than 1 million healthy individuals across Asia. There are approximately 50 active members from Bangladesh, China, India, Japan, Korea, Malaysia, Singapore, Taiwan, Thailand, the United States, and elsewhere. To date, the work of the ACC includes 3 articles published in 2011 on the roles of body mass index, tobacco smoking, and alcohol consumption in mortality, diabetes, and cancer of the small intestine. Many challenges remain, including data harmonization, resolution of ethical and legal issues, establishment of protocols for biologic samples and transfer agreements, and funding procurement.
    Matched MeSH terms: Forecasting
  7. Future climate change driven sea-level rise: secondary consequences from human displacement for island biodiversity
    Wetzel FT, Kissling WD, Beissmann H, Penn DJ
    Glob Chang Biol, 2012 Sep;18(9):2707-19.
    PMID: 24501050 DOI: 10.1111/j.1365-2486.2012.02736.x
    Sea-level rise (SLR) due to global warming will result in the loss of many coastal areas. The direct or primary effects due to inundation and erosion from SLR are currently being assessed; however, the indirect or secondary ecological effects, such as changes caused by the displacement of human populations, have not been previously evaluated. We examined the potential ecological consequences of future SLR on >1,200 islands in the Southeast Asian and the Pacific region. Using three SLR scenarios (1, 3, and 6 m elevation, where 1 m approximates most predictions by the end of this century), we assessed the consequences of primary and secondary SLR effects from human displacement on habitat availability and distributions of selected mammal species. We estimate that between 3-32% of the coastal zone of these islands could be lost from primary effects, and consequently 8-52 million people would become SLR refugees. Assuming that inundated urban and intensive agricultural areas will be relocated with an equal area of habitat loss in the hinterland, we project that secondary SLR effects can lead to an equal or even higher percent range loss than primary effects for at least 10-18% of the sample mammals in a moderate range loss scenario and for 22-46% in a maximum range loss scenario. In addition, we found some species to be more vulnerable to secondary than primary effects. Finally, we found high spatial variation in vulnerability: species on islands in Oceania are more vulnerable to primary SLR effects, whereas species on islands in Indo-Malaysia, with potentially 7-48 million SLR refugees, are more vulnerable to secondary effects. Our findings show that primary and secondary SLR effects can have enormous consequences for human inhabitants and island biodiversity, and that both need to be incorporated into ecological risk assessment, conservation, and regional planning.
    Matched MeSH terms: Forecasting
  8. Medical education: time for a change
    Chaudhuri JD
    J Indian Med Assoc, 2010 Mar;108(3):168-9.
    PMID: 21043355
    The system of medical education has not changed much over the years. This article discusses the present method of teaching of medical students. Suggestions for change in the methods have been suggested in order to produce better doctors.
    Matched MeSH terms: Forecasting
  9. Assessment of municipal solid waste generation and recyclable materials potential in Kuala Lumpur, Malaysia
    Saeed MO, Hassan MN, Mujeebu MA
    Waste Manag, 2009 Jul;29(7):2209-13.
    PMID: 19369061 DOI: 10.1016/j.wasman.2009.02.017
    This paper presents a forecasting study of municipal solid waste generation (MSWG) rate and potential of its recyclable components in Kuala Lumpur (KL), the capital city of Malaysia. The generation rates and composition of solid wastes of various classes such as street cleansing, landscape and garden, industrial and constructional, institutional, residential and commercial are analyzed. The past and present trends are studied and extrapolated for the coming years using Microsoft office 2003 Excel spreadsheet assuming a linear behavior. The study shows that increased solid waste generation of KL is alarming. For instance, the amount of daily residential SWG is found to be about 1.62 kg/capita; with the national average at 0.8-0.9 kg/capita and is expected to be increasing linearly, reaching to 2.23 kg/capita by 2024. This figure seems reasonable for an urban developing area like KL city. It is also found that, food (organic) waste is the major recyclable component followed by mix paper and mix plastics. Along with estimated population growth and their business activities, it has been observed that the city is still lacking in terms of efficient waste treatment technology, sufficient fund, public awareness, maintaining the established norms of industrial waste treatment etc. Hence it is recommended that the concerned authority (DBKL) shall view this issue seriously.
    Matched MeSH terms: Forecasting
  10. Proteomics of Important Food Crops in the Asia Oceania Region: Current Status and Future Perspectives
    Chakraborty S, Salekdeh GH, Yang P, Woo SH, Chin CF, Gehring C, et al.
    J Proteome Res, 2015 Jul 2;14(7):2723-44.
    PMID: 26035454 DOI: 10.1021/acs.jproteome.5b00211
    In the rapidly growing economies of Asia and Oceania, food security has become a primary concern. With the rising population, growing more food at affordable prices is becoming even more important. In addition, the predicted climate change will lead to drastic changes in global surface temperature and changes in rainfall patterns that in turn will pose a serious threat to plant vegetation worldwide. As a result, understanding how plants will survive in a changing climate will be increasingly important. Such challenges require integrated approaches to increase agricultural production and cope with environmental threats. Proteomics can play a role in unraveling the underlying mechanisms for food production to address the growing demand for food. In this review, the current status of food crop proteomics is discussed, especially in regard to the Asia and Oceania regions. Furthermore, the future perspective in relation to proteomic techniques for the important food crops is highlighted.
    Matched MeSH terms: Forecasting
  11. The undergraduate ENT medical curriculum: past, present and future
    Gendeh BS
    Med J Malaysia, 2002 Dec;57 Suppl E:23-6.
    PMID: 12733188
    The strong international demand for admission into medical schools make medical education a "seller's market", and increasingly a global market. Teaching of Otorhinolaryngology-Head and Neck Surgery (ORL-HNS) has two primary goals. Firstly, a firm grasp of basic principles, recognition and treatment of common disorders, initial management of ORL-HNS emergencies and indications for specialist referral. Secondly, to provide sufficient exposure to the specialty to assist in career planning. Good communicative skills for optimal patient care are essential in the selection criteria of medical students. Proficiency in English is essential to obtain a disproportion share of opportunities in the new economy. The examination evaluation needs to be standardized between the various medical schools and the recommended lecturer-student ratio is maintained. The Joint National Evaluating Board has a very essential role to play in the maintenance of medical educational standards in Malaysia.
    Matched MeSH terms: Forecasting
  12. Water and wastewater in developing countries: present reality and strategy for the future
    Ujang Z, Buckley C
    Water Sci Technol, 2002;46(9):1-9.
    PMID: 12448446
    This paper summarises the paper presentation sessions at the Conference, as well giving insights on the issues related to developing countries. It also discusses the present status of practice and research on water and wastewater management, and projected future scenario based not only on the papers presented in the Conference, but also on other sources. The strategy is presented to overcome many problems in developing countries such as rapid urbanization, industrialization, population growth, financial and institutional problems and, depleting water resources. The strategy consists of Integrated Urban Water Management (IUWM), cleaner industrial production, waste minimisation and financial arrangements.
    Matched MeSH terms: Forecasting
  13. Immunology in Malaysia--a review
    Bosco J
    Ann Acad Med Singap, 1988 Apr;17(2):251-3.
    PMID: 3044263
    Immunology is a discipline that traverses all branches of clinical medicine. Thus since about ten years ago major hospitals in Malaysia established routine clinical immunology services particularly in the diagnosis of autoimmune/connective tissue disorders. More recently these laboratories have ventured into basic research in Dengue Haemorrhagic Fever, Leukaemia Immunology, Nasopharyngeal Cancer and Leprosy. The rationale for these projects together with early results from them are discussed.
    Matched MeSH terms: Forecasting
  14. Fulltext How Investors Attitudes Shape Stock Market Participation in the Presence of Financial Self-Efficacy
    Nadeem MA, Qamar MAJ, Nazir MS, Ahmad I, Timoshin A, Shehzad K
    Front Psychol, 2020;11:553351.
    PMID: 33192804 DOI: 10.3389/fpsyg.2020.553351
    The purpose of this study is to investigate how investor's money attitudes shape their stock market participation (SMP) decisions. This study followed the theory of planned behavior (TPB), and a survey was conducted to collect the responses from active investors. Structural equation modeling (SEM) was used for the analysis of proposed relationships among the constructs, and a confirmatory factor analysis (CFA) was conducted to check the interrelation of the variables and validity of the constructs. This research has concluded that investor's money attitudes are significant to affect their stock market participation decisions. Further, it was found that risk attitudes partially mediate the relationship between money attitudes and stock market participation. Moreover, financial knowledge and financial self-efficacy positively moderated the relationship between money attitudes and stock market participation. This research is one of the early attempts at studying the money attitudes of investors and introduces financial self-efficacy as a moderating construct between money attitudes and stock market participation. The sample size for this study was 250 respondents which can be increased in future research, and the same relationships can be tested by using a larger sample. Moreover, this study has used money attitudes as predictors of stock market participation. Still, many other variables, like personal value, can also be taken to investigate their influence on stock market participation.
    Matched MeSH terms: Forecasting
  15. Fulltext Forecasting electricity demand from daily log sheet with correlated variables
    Mohamad Syamim Hilm, Sofianita Mutalib, Sarifah Radiah Shari, Siti Nur Kamaliah Kamarudin
    ESTEEM Academic Journal, 2020;16(2):31-40.
    MyJurnal
    Electricity is one of the most important resources and fundamental infrastructure for every nation. Its milestone shows a significant contribution to world development that brought forth new technological breakthroughs throughout the centuries. Electricity demand constantly fluctuates, which affects the supply. Suppliers need to generate more electrical energy when demand is high, and less when demand is low. It is a common practice in power markets to have a reserve margin for unexpected fluctuation of demand. This research paper investigates regression techniques: multiple linear regression (MLR) and vector autoregression (VAR) to forecast demand with predictors of economic growth, population growth, and climate change as well as the demand itself. Auto-Regressive Integrated Moving Average (Auto-ARIMA) was used in benchmarking the forecasting. The results from MLR and VAR (lag-values=20) and Auto-ARIMA are monitored for five months from June to October of 2019. Using the root mean square error (RMSE) as an indicator for accuracy, Auto-ARIMA has the lowest RMSE for four months except in June 2019. VAR (lag-values=20) shows good forecasting capabilities for all five months, considering it uses the same lag values (20) for each month. Three different techniques have been successfully examined in order to find the best model for the prediction of the demand.
    Matched MeSH terms: Forecasting
  16. Fulltext Transient Behavior in Variable Geometry Industrial Gas Turbines: A Comprehensive Overview of Pertinent Modeling Techniques
    Hashmi MB, Lemma TA, Ahsan S, Rahman S
    Entropy (Basel), 2021 Feb 22;23(2).
    PMID: 33671488 DOI: 10.3390/e23020250
    Generally, industrial gas turbines (IGT) face transient behavior during start-up, load change, shutdown and variations in ambient conditions. These transient conditions shift engine thermal equilibrium from one steady state to another steady state. In turn, various aero-thermal and mechanical stresses are developed that are adverse for engine's reliability, availability, and overall health. The transient behavior needs to be accurately predicted since it is highly related to low cycle fatigue and early failures, especially in the hot regions of the gas turbine. In the present paper, several critical aspects related to transient behavior and its modeling are reviewed and studied from the point of view of identifying potential research gaps within the context of fault detection and diagnostics (FDD) under dynamic conditions. Among the considered topics are, (i) general transient regimes and pertinent model formulation techniques, (ii) control mechanism for part-load operation, (iii) developing a database of variable geometry inlet guide vanes (VIGVs) and variable bleed valves (VBVs) schedules along with selection framework, and (iv) data compilation of shaft's polar moment of inertia for different types of engine's configurations. This comprehensive literature document, considering all the aspects of transient behavior and its associated modeling techniques will serve as an anchor point for the future researchers, gas turbine operators and design engineers for effective prognostics, FDD and predictive condition monitoring for variable geometry IGT.
    Matched MeSH terms: Forecasting
  17. An improved SPEI drought forecasting approach using the long short-term memory neural network
    Dikshit A, Pradhan B, Huete A
    J Environ Manage, 2021 Apr 01;283:111979.
    PMID: 33482453 DOI: 10.1016/j.jenvman.2021.111979
    Droughts are slow-moving natural hazards that gradually spread over large areas and capable of extending to continental scales, leading to severe socio-economic damage. A key challenge is developing accurate drought forecast model and understanding a models' capability to examine different drought characteristics. Traditionally, forecasting techniques have used various time-series approaches and machine learning models. However, the use of deep learning methods have not been tested extensively despite its potential to improve our understanding of drought characteristics. The present study uses a deep learning approach, specifically the Long Short-Term Memory (LSTM) to predict a commonly used drought measure, the Standard Precipitation Evaporation Index (SPEI) at two different time scales (SPEI 1, SPEI 3). The model was compared with other common machine learning method, Random Forests, Artificial Neural Networks and applied over the New South Wales (NSW) region of Australia, using hydro-meteorological variables as predictors. The drought index and predictor data were collected from the Climatic Research Unit (CRU) dataset spanning from 1901 to 2018. We analysed the LSTM forecasted results in terms of several drought characteristics (drought intensity, drought category, or spatial variation) to better understand how drought forecasting was improved. Evaluation of the drought intensity forecasting capabilities of the model were based on three different statistical metrics, Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The model achieved R2 value of more than 0.99 for both SPEI 1 and SPEI 3 cases. The variation in drought category forecasted results were studied using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) approach. The analysis revealed an AUC value of 0.83 and 0.82 for SPEI 1 and SPEI 3 respectively. The spatial variation between observed and forecasted values were analysed for the summer months of 2016-2018. The findings from the study show an improvement relative to machine learning models for a lead time of 1 month in terms of different drought characteristics. The results from this work can be used for drought mitigation purposes and different models need to be tested to further enhance our capabilities.
    Matched MeSH terms: Forecasting
  18. Hybridomas--medical diagnosis of the future
    Pang T
    Med J Malaysia, 1979 Dec;34(2):91-4.
    PMID: 398437
    Matched MeSH terms: Forecasting
  19. Politicians must heed health effects of climate change
    Lim V, Stubbs JW, Nahar N, Amarasena N, Chaudry ZU, Weng SCK, et al.
    Lancet, 2009 Sep 19;374(9694):973.
    PMID: 19762076 DOI: 10.1016/S0140-6736(09)61641-X
    Matched MeSH terms: Forecasting
  20. Pathology--its origins, progression and future
    Cotton RE
    Malays J Pathol, 1987 Aug;9:49-55.
    PMID: 3330746
    Matched MeSH terms: Forecasting
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