Displaying publications 1 - 20 of 311 in total

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  1. Periodontal treatment needs and workforce requirements: comparisons between the normative and sociodental approaches using different skill mix models
    Ab-Murat N, Sheiham A, Tsakos G, Watt R
    Community Dent Oral Epidemiol, 2015 Apr;43(2):106-15.
    PMID: 25178437 DOI: 10.1111/cdoe.12125
    Assessment of dental treatment needs has predominantly been based on the normative approach, despite its numerous limitations. The sociodental approach is a more rational method of needs assessment as it incorporates broader concepts of health and needs and behavioural propensity. This study compares estimates of periodontal dental treatment needs and workforce requirements for different skill mixes using normative and sociodental approaches among a sample of adults in Malaysia.
    Matched MeSH terms: Models, Statistical
  2. Fulltext The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis
    Abbasi GA, Tiew LY, Tang J, Goh YN, Thurasamy R
    PLoS One, 2021;16(3):e0247582.
    PMID: 33684120 DOI: 10.1371/journal.pone.0247582
    In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.
    Matched MeSH terms: Models, Statistical*
  3. A complex linear regression model
    Abdul Ghapor Hussin, Norli Anida Abdullah, Ibrahim Mohamed
    Sains Malaysiana, 2010;39.
    This paper gives a comprehensive discussion on complex regression model by extending the idea of regression model to circular variables. Various aspect have been considered such as the biasness of parameters, error assumptions and model checking. The advantage of this approach is that it allows the use of usual technique available in ordinary linear regression for the regression of circular variables. The quality of the estimates and the feasibility of the approach were illustrated via simulation. The model was then applied to the wave direction data.
    Matched MeSH terms: Models, Statistical
  4. Fulltext Optimization of milk-based medium for efficient cultivation of Bifidobacterium pseudocatenulatum G4 using face-centered central composite-response surface methodology
    Abdul Khalil K, Mustafa S, Mohammad R, Bin Ariff A, Shaari Y, Abdul Manap Y, et al.
    Biomed Res Int, 2014;2014:787989.
    PMID: 24527457 DOI: 10.1155/2014/787989
    This study was undertaken to optimize skim milk and yeast extract concentration as a cultivation medium for optimal Bifidobacteria pseudocatenulatum G4 (G4) biomass and β -galactosidase production as well as lactose and free amino nitrogen (FAN) balance after cultivation period. Optimization process in this study involved four steps: screening for significant factors using 2(3) full factorial design, steepest ascent, optimization using FCCD-RSM, and verification. From screening steps, skim milk and yeast extract showed significant influence on the biomass production and, based on the steepest ascent step, middle points of skim milk (6% wt/vol) and yeast extract (1.89% wt/vol) were obtained. A polynomial regression model in FCCD-RSM revealed that both factors were found significant and the strongest influence was given by skim milk concentration. Optimum concentrations of skim milk and yeast extract for maximum biomass G4 and β -galactosidase production meanwhile low in lactose and FAN balance after cultivation period were 5.89% (wt/vol) and 2.31% (wt/vol), respectively. The validation experiments showed that the predicted and experimental values are not significantly different, indicating that the FCCD-RSM model developed is sufficient to describe the cultivation process of G4 using skim-milk-based medium with the addition of yeast extract.
    Matched MeSH terms: Models, Statistical
  5. Fulltext Population Pharmacokinetics of Doripenem in Critically Ill Patients with Sepsis in a Malaysian Intensive Care Unit
    Abdul-Aziz MH, Abd Rahman AN, Mat-Nor MB, Sulaiman H, Wallis SC, Lipman J, et al.
    Antimicrob Agents Chemother, 2016 01;60(1):206-14.
    PMID: 26482304 DOI: 10.1128/AAC.01543-15
    Doripenem has been recently introduced in Malaysia and is used for severe infections in the intensive care unit. However, limited data currently exist to guide optimal dosing in this scenario. We aimed to describe the population pharmacokinetics of doripenem in Malaysian critically ill patients with sepsis and use Monte Carlo dosing simulations to develop clinically relevant dosing guidelines for these patients. In this pharmacokinetic study, 12 critically ill adult patients with sepsis receiving 500 mg of doripenem every 8 h as a 1-hour infusion were enrolled. Serial blood samples were collected on 2 different days, and population pharmacokinetic analysis was performed using a nonlinear mixed-effects modeling approach. A two-compartment linear model with between-subject and between-occasion variability on clearance was adequate in describing the data. The typical volume of distribution and clearance of doripenem in this cohort were 0.47 liters/kg and 0.14 liters/kg/h, respectively. Doripenem clearance was significantly influenced by patients' creatinine clearance (CL(CR)), such that a 30-ml/min increase in the estimated CL(CR) would increase doripenem CL by 52%. Monte Carlo dosing simulations suggested that, for pathogens with a MIC of 8 mg/liter, a dose of 1,000 mg every 8 h as a 4-h infusion is optimal for patients with a CL(CR) of 30 to 100 ml/min, while a dose of 2,000 mg every 8 h as a 4-h infusion is best for patients manifesting a CL(CR) of >100 ml/min. Findings from this study suggest that, for doripenem usage in Malaysian critically ill patients, an alternative dosing approach may be meritorious, particularly when multidrug resistance pathogens are involved.
    Matched MeSH terms: Models, Statistical*
  6. "Power quality system," a new system of quality management for globalization: towards innovation and competitive advantages
    Abdul-Rahman H, Berawi MA
    Qual Assur, 2001;9(1):5-30.
    PMID: 12465710
    Knowledge Management (KM) addresses the critical issues of organizational adoption, survival and competence in the face of an increasingly changing environment. KM embodies organizational processes that seek a synergistic combination of the data and information processing capabilities of information and communication technologies (ICT), and the creative and innovative capacity of human beings to improve ICT In that role, knowledge management will improve quality management and avoid or minimize losses and weakness that usually come from poor performance as well as increase the competitive level of the company and its ability to survive in the global marketplace. To achieve quality, all parties including the clients, company consultants, contractors, entrepreneurs, suppliers, and the governing bodies (i.e., all involved stake-holders) need to collaborate and commit to achieving quality. The design based organizations in major business and construction companies have to be quality driven to support healthy growth in today's competitive market. In the march towards vision 2020 and globalization (i.e., the one world community) of many companies, their design based organizations need to have superior quality management and knowledge management to anticipate changes. The implementation of a quality system such as the ISO 9000 Standards, Total Quality Management, or Quality Function Deployment (QFD) focuses the company's resources towards achieving faster and better results in the global market with less cost. To anticipate the needs of the marketplace and clients as the world and technology change, a new system, which we call Power Quality System (PQS), has been designed. PQS is a combination of information and communication technologies (ICT) and the creative and innovative capacity of human beings to meet the challenges of the new world business and to develop high quality products.
    Publication year= 2001 Jan-2002 Mar
    Matched MeSH terms: Models, Statistical
  7. Fulltext Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm
    Abdulhamid SM, Abd Latiff MS, Abdul-Salaam G, Hussain Madni SH
    PLoS One, 2016;11(7):e0158102.
    PMID: 27384239 DOI: 10.1371/journal.pone.0158102
    Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.
    Matched MeSH terms: Models, Statistical
  8. Classification of air quality using fuzzy synthetic multiplication
    Abdullah L, Khalid ND
    Environ Monit Assess, 2012 Nov;184(11):6957-65.
    PMID: 22160435 DOI: 10.1007/s10661-011-2472-1
    Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
    Matched MeSH terms: Models, Statistical
  9. Heavy metals concentration relationship with Perna viridis physical properties in Mengkabong Lagoon, Sabah, Malaysia
    Abdullah N, Tair R, Abdullah MH
    Pak J Biol Sci, 2014 Jan 01;17(1):62-7.
    PMID: 24783779
    Perna viridis (P. viridis) has been identified as a good biological indicator in identifying environmental pollution, especially when there are various types of Heavy Metals Accumulations (HMA) inside its tissue. Based on the potential of P. viridis to accumulate heavy metals and the data on its physical properties, this study proffers to determine the relationships between both properties. The similarities of the physical properties are used to mathematical model their relationships, which included the size (length, width, height) and weight (wet and dry) of P. viridis, whilst the heavy metals are focused on concentrations of Pb, Cu, Cr, Cd and Zn. The concentrations of metal elements are detected by using Flame Atomic Adsorption Spectrometry. Results show that the mean concentration of Pb, Cu, Cr, Cd, Zn, length, width, height, wet weight and dry weight are: 1.12 +/- 1.00, 2.36 +/- 1.65, 2.12 +/- 2.74, 0.44 +/- 0.41 and 16.52 +/- 10.64 mg kg(-1) (dry weight), 105.08 +/- 14.35, 41.64 +/- 4.64, 28.75 +/- 3.92 mm, 14.56 +/- 3.30 and 2.37 +/- 0.86 g, respectively. It is also found out that the relationships between the Heavy Metals Concentrations (HMA) and the physical properties can be represented using Multiple Linear Regressions (MLR) models, relating that the HMA of Zinc has affected significantly the physical growth properties of P. viridis.
    Matched MeSH terms: Models, Statistical
  10. Fulltext Determination of Cutoff Values for the Screening of Osteosarcopenia in Obese Postmenopausal Women
    Abidin NZ, Mitra SR
    Curr Gerontol Geriatr Res, 2021;2021:6634474.
    PMID: 33790963 DOI: 10.1155/2021/6634474
    Osteosarcopenic obesity (OSO) describes the concurrent presence of obesity, low bone mass, and low muscle mass in an individual. Currently, no established criteria exist to diagnose OSO. We hypothesized that obese individuals require different cut-points from standard cut-points to define low bone mass and low muscle mass due to their higher weight load. In this study, we determined cutoff values for the screening of osteosarcopenia (OS) in obese postmenopausal Malaysian women based on the measurements of quantitative ultrasound (QUS), bioelectrical impedance analysis (BIA), and functional performance test. Then, we compared the cutoff values derived by 3 different statistical modeling methods, (1) receiver operating characteristic (ROC) curve, (2) lowest quintile of the study population, and (3) 2 standard deviations (SD) below the mean value of a young reference group, and discussed the most suitable method to screen for the presence of OS in obese population. One hundred and forty-one (n = 141) postmenopausal Malaysian women participated in the study. Bone density was assessed using calcaneal quantitative ultrasound. Body composition was assessed using bioelectrical impedance analyzer. Handgrip strength was assessed using a handgrip dynamometer, and physical performance was assessed using a modified Short Physical Performance Battery test. ROC curve was determined to be the most suitable statistical modeling method to derive the cutoffs for the presence of OS in obese population. From the ROC curve method, the final model to estimate the probability of OS in obese postmenopausal women is comprised of five variables: handgrip strength (HGS, with area under the curve (AUC) = 0.698 and threshold ≤ 16.5 kg), skeletal muscle mass index (SMMI, AUC = 0.966 and threshold ≤ 8.2 kg/m2), fat-free mass index (FFMI, AUC = 0.946 and threshold ≤ 15.2 kg/m2), broadband ultrasonic attenuation (BUA, AUC = 0.987 and threshold ≤ 52.85 dB/MHz), and speed of sound (SOS, AUC = 0.991 and threshold ≤ 1492.15 m/s). Portable equipment may be used to screen for OS in obese women. Early identification of OS can help lower the risk of advanced functional impairment that can lead to physical disability in obese postmenopausal women.
    Matched MeSH terms: Models, Statistical
  11. Fulltext Optimization of physical conditions for the production of thermostable T1 lipase in Pichia guilliermondii strain SO using response surface methodology
    Abu ML, Nooh HM, Oslan SN, Salleh AB
    BMC Biotechnol, 2017 Nov 10;17(1):78.
    PMID: 29126403 DOI: 10.1186/s12896-017-0397-7
    BACKGROUND: Pichia guilliermondii was found capable of expressing the recombinant thermostable lipase without methanol under the control of methanol dependent alcohol oxidase 1 promoter (AOXp 1). In this study, statistical approaches were employed for the screening and optimisation of physical conditions for T1 lipase production in P. guilliermondii.

    RESULT: The screening of six physical conditions by Plackett-Burman Design has identified pH, inoculum size and incubation time as exerting significant effects on lipase production. These three conditions were further optimised using, Box-Behnken Design of Response Surface Methodology, which predicted an optimum medium comprising pH 6, 24 h incubation time and 2% inoculum size. T1 lipase activity of 2.0 U/mL was produced with a biomass of OD600 23.0.

    CONCLUSION: The process of using RSM for optimisation yielded a 3-fold increase of T1 lipase over medium before optimisation. Therefore, this result has proven that T1 lipase can be produced at a higher yield in P. guilliermondii.

    Matched MeSH terms: Models, Statistical
  12. The use of response surface methodology for enhanced production of a thermostable bacterial lipase in a novel yeast system
    Abu ML, Mohammad R, Oslan SN, Salleh AB
    Prep Biochem Biotechnol, 2021;51(4):350-360.
    PMID: 32940138 DOI: 10.1080/10826068.2020.1818256
    A thermostable bacterial lipase from Geobacillus zalihae was expressed in a novel yeast Pichia sp. strain SO. The preliminary expression was too low and discourages industrial production. This study sought to investigate the optimum conditions for T1 lipase production in Pichia sp. strain SO. Seven medium conditions were investigated and optimized using Response Surface Methodology (RSM). Five responding conditions namely; temperature, inoculum size, incubation time, culture volume and agitation speed observed through Plackett-Burman Design (PBD) method had a significant effect on T1 lipase production. The medium conditions were optimized using Box-Behnken Design (BBD). Investigations reveal that the optimum conditions for T1 lipase production and Biomass concentration (OD600) were; Temperature 31.76 °C, incubation time 39.33 h, culture volume 132.19 mL, inoculum size 3.64%, and agitation speed of 288.2 rpm with a 95% PI low as; 12.41 U/mL and 95% PI high of 13.65 U/mL with an OD600 of; 95% PI low as; 19.62 and 95% PI high as; 22.62 as generated by the software was also validated. These predicted parameters were investigated experimentally and the experimental result for lipase activity observed was 13.72 U/mL with an OD600 of 24.5. At these optimum conditions, there was a 3-fold increase on T1 lipase activity. This study is the first to develop a statistical model for T1 lipase production and biomass concentration in Pichia sp. Strain SO. The optimized production of T1 lipase presents a choice for its industrial application.
    Matched MeSH terms: Models, Statistical*
  13. Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review
    Aburas MM, Ahamad MSS, Omar NQ
    Environ Monit Assess, 2019 Mar 05;191(4):205.
    PMID: 30834982 DOI: 10.1007/s10661-019-7330-6
    Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.
    Matched MeSH terms: Models, Statistical
  14. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images
    Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, et al.
    Comput Biol Med, 2016 12 01;79:250-258.
    PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022
    Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
    Matched MeSH terms: Models, Statistical
  15. On discordance test for the wrapped normal data
    Adzhar Rambli, Safwati Ibrahim, Mohd Ikhwan Abdullah, Abdul Ghapor Hussin, Ibrahim Mohamed
    Sains Malaysiana, 2012;41:769-778.
    This paper focuses on detecting outliers in the circular data which follow the wrapped normal distribution. We considered four discordance tests based on M, C, D and A statistics. The cut-off points of the four tests were obtained and the performance of the detection procedures was studied via simulations. In general, we showed that the discordance test based on the A statistic outperforms the other tests in all cases. For illustration, the city of Kuantan wind direction data set was considered.
    Matched MeSH terms: Models, Statistical
  16. Objective assessment of psoriasis erythema for PASI scoring
    Ahmad Fadzil MH, Ihtatho D, Affandi AM, Hussein SH
    Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:4070-3.
    PMID: 19163606 DOI: 10.1109/IEMBS.2008.4650103
    Skin colour is vital information in dermatological diagnosis. It reflects pathological condition beneath the skin and commonly being used to indicate the extent of a disease. Psoriasis is a skin disease which is indicated by the appearance of red plaques. Although there is no cure for psoriasis, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to determine severity of psoriasis lesion. Erythema (redness) is one parameter in PASI. Commonly, the erythema is assessed visually, thus leading to subjective and inconsistent result. In this work, we proposed an objective assessment of psoriasis erythema for PASI scoring. The colour of psoriasis lesion is analyzed by DeltaL, Deltahue, and Deltachroma of CIELAB colour space. References of lesion with different scores are obtained from the selected lesions by two dermatologists. Results based on 38 lesions from 22 patients with various level of skin pigmentation show that PASI erythema score can be determined objectively and consistent with dermatology scoring.
    Matched MeSH terms: Models, Statistical
  17. Computerized segmentation and measurement of chronic wound images
    Ahmad Fauzi MF, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN
    Comput Biol Med, 2015 May;60:74-85.
    PMID: 25756704 DOI: 10.1016/j.compbiomed.2015.02.015
    An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
    Matched MeSH terms: Models, Statistical
  18. Modelling for determination of fe uptake by brassica chinensis jusl var. Parachinensi (bailey) tsen & lee (flowering white cabbage)
    Ahmad Mahir R, Arfah A, Rozaimah Z, Siti Adyani S, Khairiah J, Ismail B
    Sains Malaysiana, 2017;46:2305-2313.
    The study was conducted to determine the best model suitable for the determination of ferrum uptake in Brassica chinensis (flowering white cabbage). A nonlinear regression model was selected to determine the amount of ferrum absorbed by each part of the Brassica chinensis plant namely the leaves, stems and roots. The Levenberg-Marquardt method was used to perform the nonlinear least square fit. This method employs information on the gradients and hence requires specification of the partial derivatives. A suitable model was obtained from the exponential regression model. The polynomial model was found to be appropriate for leaves, the mono-exponential model was suitable for stems and the simple exponential model for roots. The residual plots and the normal probability plots from each of the models indicated no substantial diagnostic problems, so it can be concluded that the polynomial and exponential regression models provide adequate fit to determine data on heavy metal uptake by the flowering white cabbage.
    Matched MeSH terms: Models, Statistical
  19. Statistical analysis of vehicle theft crime in peninsular Malaysia using negative binomial regression model
    Ahmad Mahir Razali, Nurulkamal Masseran, Noriszura Ismail, Malina Zulkifli
    Sains Malaysiana, 2015;44:1363-1370.
    The aim of this paper was to identify the determinants that influence vehicle theft by applying a negative binomial regression model. The identification of these determinants is very important to policy-makers, car-makers and car owners, as they can be used to establish practical steps for preventing or at least limiting vehicle thefts. In addition, this paper also proposed a crime mapping application that allows us to identify the most risky areas for vehicle theft. The results from this study can be utilized by local authorities as well as management of internal resource planning of insurance companies in planning effective strategies to reduce vehicle theft. Indirectly, this paper has built ingenuity by combining information obtained from the database of Jabatan Perangkaan Malaysia and insurance companies to pioneer the development of location map of vehicle theft in Malaysia.
    Matched MeSH terms: Models, Statistical
  20. Power divergence statistics under quasi independence model for square contingency tables
    Akta? S
    Sains Malaysiana, 2016;45:1565-1572.
    In incomplete contingency tables, some cells may contain structural zeros. The quasi-independence model, which is a generalization of the independence model, is most commonly model used to analyze incomplete contingency tables. Goodness of fit tests of the quasi-independence model are usually based on Pearson chi square test statistic and likelihood ratio test statistic. In power divergence statistics family, the selection of power divergence parameter is of interest in multivariate discrete data. In this study, a simulation study is conducted to evaluate the performance of the power divergence statistics under quasi independence model for particular power divergence parameters in terms of power values.
    Matched MeSH terms: Models, Statistical
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