Displaying publications 1 - 20 of 427 in total

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  1. Muhamad Noor Mohamed, Azrul Hisham Azhar
    Movement Health & Exercise, 2012;1(1):49-60.
    MyJurnal
    Postural movements potentially affect aiming stability in archery, thus contributing to chances of inconsistent hits. According to the expertisenovice paradigm, the factor that sets winners apart from ordinary athletes is the former’s ability to control minute changes in their performance. The
    present study seeks to determine the relationship between postural sway and shooting performance amongst Malaysian skilled recurve archers. Twenty one skilled Malaysian archers participated in this study, where performance level was measured by rank tournaments International Archery Federation (FITA) score. Postural sway was assessed in terms of anterior deviation (positive value) and posterior deviation (negative value) using ZEPHYR Bio-Harness. Postural sway was analysed at the following three phases; (i) setup, (ii) aiming, and (iii) release. Participants shot 12 arrows to a 30-meter target. Data yielded a significant relationship between postural sway and shooting performance. The correlation coefficients between shooting performance and postural sway value for skilled archers ranged between (r = -0.021 to 0.248) with the highest correlation recorded at the release phase, with the lowest at the aiming phase. The setup phase showed the only anterior deviation throughout the test. During the setup and release phases, correlation between postural sway with shooting performance was significantly noted (p < 0.001). Multiple regression analysis showed that postural sway during the setup and release phases were the significant indicators for shooting performance, accounting approximately 17% and 24% of the variances respectively. In sum, the results indicate that reducing postural sway
    during the release phase can increase shooting performance of skilled archery athletes, thus establishing a significant relationship between the postural sway value with shooting performance of skilled archers.
    Matched MeSH terms: Multivariate Analysis
  2. Nur Atikah Arbain, Mohd Sanusi Azmi, Sharifah Sakinah Syed Ahmad, Azah Kamilah Muda, Intan Ermahami A. Jalil, King Ming Tiang
    MyJurnal
    In recent years, many classification models have been developed and applied to increase their accuracy. The concept of distance between two samples or two variables is a fundamental concept in multivariate analysis. This paper proposed a tool that used different similarity distance approaches with ranking method based on Mean Average Precision (MAP). In this study, several similarity distance methods were used, such as Euclidean, Manhattan, Chebyshev, Sorenson and Cosine. The most suitable distance measure was based on the smallest value of distance between the samples. However, the real solution showed that the results were not accurate as and thus, MAP was considered the best approach to overcome current limitations.
    Matched MeSH terms: Multivariate Analysis
  3. Silalahi DD, Midi H, Arasan J, Mustafa MS, Caliman JP
    Heliyon, 2020 Jan;6(1):e03176.
    PMID: 32042959 DOI: 10.1016/j.heliyon.2020.e03176
    In practice, the collected spectra are very often composes of complex overtone and many overlapping peaks which may lead to misinterpretation because of its significant nonlinear characteristics. Using linear solution might not be appropriate. In addition, with a high-dimension of dataset due to large number of observations and data points the classical multiple regressions will neglect to fit. These complexities commonly will impact to multicollinearity problem, furthermore the risk of contamination of multiple outliers and high leverage points also increases. To address these problems, a new method called Kernel Partial Diagnostic Robust Potential (KPDRGP) is introduced. The method allows the nonlinear solution which maps nonlinearly the original input

    X

    matrix into higher dimensional feature mapping with corresponds to the Reproducing Kernel Hilbert Spaces (RKHS). In dimensional reduction, the method replaces the dot products calculation of elements in the mapped data to a nonlinear function in the original input space. To prevent the contamination of the multiple outlier and high leverage points the robust procedure using Diagnostic Robust Generalized Potentials (DRGP) algorithm was used. The results verified that using the simulation and real data, the proposed KPDRGP method was superior to the methods in the class of non-kernel and some other robust methods with kernel solution.
    Matched MeSH terms: Multivariate Analysis
  4. Kong, K.W., Emmy, H.K.I., Azizah, O., Amin, I., Tan, C.P.
    MyJurnal
    Lycopene and total phenolics of pink guava puree industry by-products (refiner, siever and decanter)
    were evaluated after steam blanching at selected temperatures and times. Lycopene content was in the order of decanter > siever > refiner (7.3, 6.3 and 1.5 mg/100 g, respectively), and the content of total phenolics was in the order of refiner > siever > decanter (4434.1, 2881.3 and 1529.3 mg GAE/100 g, respectively). Regression coefficients for temperatures (x1) and times (x2) from multiple linear regression models of siever and decanter showed significant (p
    Matched MeSH terms: Multivariate Analysis
  5. Siah, W. M., Aminah, A., Ishak, A.
    MyJurnal
    The effects of soaking conditions on the quality characteristics of seaweed paste of Kappaphycus alverazii species were studied. Response Surface Methodology (RSM) with a 2-factor, 5-level central composite design (CCD) was conducted to determine the optimum soaking conditions. The interactive effect of dry seaweed: soaking water ratio (X1 = 1: 15-50) and soaking duration (X2 = 30-120 min) on the gel strength (g), whiteness, expansion (%), moisture content (%) and protein content (g/100 g) of the paste were determined. Results showed that the experimental data could be adequately fitted into a second-order polynomial model with multiple regression coefficients (R2) of 0.8141, 0.9245, 0.9118, 0.9113 and 0.9271 for the gel strength, whiteness, expansion, moisture content and protein content, respectively. The gel strength, whiteness, expansion, moisture content and protein content of seaweed paste were dependent on the ratio of dry seaweed to soaking water and also soaking duration. The proposed optimum soaking conditions for the production of seaweed paste is at a ratio of 1:15 (dry seaweed : soaking water) and soaking duration of 117.06 min. Based on the result obtained, the RSM demonstrated a suitable approach for the processing optimization of Kappaphycus alverazii paste.
    Matched MeSH terms: Multivariate Analysis
  6. Wan Nor Arifin
    MyJurnal
    Multivariate analyses depend on multivariate normality assumption. Although the analyses are available in SPSS, it is not possible to assess the assumption from the basic package. Statistical assessment of the normality is available in a specialized package, SPSS Amos, in form of Mardia's multivariate kurtosis. However, graphical assessment of the normality by chi-square versus Mahalanobis distance plot is not available in both of the packages. The aim of this article is to present the steps to construct the plot in SPSS in a point-and-click manner as expected by most SPSS users.
    Matched MeSH terms: Multivariate Analysis
  7. Okomoda TV, Koh ICC, Hassan A, Amornsakun T, Shahreza SM
    Sci Rep, 2018 02 28;8(1):3827.
    PMID: 29491444 DOI: 10.1038/s41598-018-22149-4
    Twenty-five traditional and thirty-four geometric morphometric comparisons were carried out on pure and reciprocal crosses of Pangasianodon hypophthalmus (Sauvage, 1878) and Clarias gariepinus (Burchell, 1822). Thirty fish samples each of the C. gariepinus (CH), P. hypophthalmus (PH), Pangapinus (♀PH × ♂CG) and the two distinct morphotypes of the Clariothalmus (♀CG × ♂PH) (Clarias-like and Panga-like) between the ages of four and six months were used for this study. Phenotypically, the Clarias-like Clariothalmus and the Pangapinus progenies were indistinguishable from their maternal parents while the Panga-like Clariothalmus was a phenotypic intermediary of the putative parents but looks more closely to the paternal parent. Hence, both univariate proportion and multivariate analysis of the collected data successfully separated the different fishes into three multivariate spaces. The analysis of the dendrogram with complete linkage and Euclidean distance further showed the close relationship of the isolated Panga-like Clariothalmus progenies to the paternal parent, however, Clarias-like Clariothalmus and the Pangapinus were completely intermingled with their maternal parents. The most important index of discrimination of these fishes into different multivariate spaces was the fin characteristic which showed 100% exclusive ranges for the individual groups in many cases.
    Matched MeSH terms: Multivariate Analysis
  8. Habshah Midi, Bagheri A, Rahmatullah Imon A
    Sains Malaysiana, 2011;40:1437-1447.
    Outliers in the X-direction or high leverage points are the latest known source of multicollinearity. Multicollinearity is a nonorthogonality of two or more explanatory variables in multiple regression models, which may have important influential impacts on interpreting a fitted regression model. In this paper, we performed Monte Carlo simulation studies to achieve two main objectives. The first objective was to study the effect of certain magnitude and percentage of high leverage points, which are two important issues in tending the high leverage points to be collinearity-enhancing observations, on the multicollinarity pattern of the data. The second objective was to investigate in which situations these points do make different degrees of multicollinearity, such as moderate or severe. According to the simulation results, high leverage points should be in large magnitude for at least two explanatory variables to guarantee that they are the cause of multicollinearity problems. We also proposed some practical Lower Bound (LB) and Upper Bound (UB) for High Leverage Collinearity Influential Measure (HLCIM) which is an essential measure in detecting the degree of multicollinearity. A well-known example is used to confirm the simulation results.
    Matched MeSH terms: Multivariate Analysis
  9. Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono Suhartono, Abdul Ghapor Hussin, Yong Zulina Zubairi
    Sains Malaysiana, 2018;47:419-426.
    Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
    data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
    forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
    relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
    model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
    combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
    of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
    error graphically. From the result obtained this model gives a better forecast compare to the other two models.
    Matched MeSH terms: Multivariate Analysis
  10. Siavash NK, Ghobadian B, Najafi G, Rohani A, Tavakoli T, Mahmoodi E, et al.
    Environ Res, 2020 Nov 06.
    PMID: 33166537 DOI: 10.1016/j.envres.2020.110434
    Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it is logical that a small increment in wind speed will result in significant growth in generated power. Shrouding a wind turbine is an ordinary way to exceed the Betz limit, which accelerates the wind flow through the rotor plane. Several layouts of shrouds are developed by researchers. Recently an innovative controllable duct is developed by the authors of this work that can vary the shrouding angle, so its performance is different in each opening angle. As a wind tunnel investigation is heavily time-consuming and has a high cost, therefore just four different opening angles have been assessed. In this work, the performance of the turbine was predicted using multiple linear regression and an artificial neural network in a wide range of duct opening angles. For the turbine power generation and its rotor angular speed in different wind velocities and duct opening angles, regression and an ANN are suggested. The developed neural network model is found to possess better performance than the regression model for both turbine power curve and rotor speed estimation. This work revealed that in higher ranges of wind velocity, the turbine performance intensively will be a function of shrouding angle. This model can be used as a lookup table in controlling the turbines equipped with the proposed mechanism.
    Matched MeSH terms: Multivariate Analysis
  11. Nabi FG, Sundaraj K, Lam CK
    J Pak Med Assoc, 2021 Jan;71(1(A)):41-46.
    PMID: 33484516 DOI: 10.47391/JPMA.156
    OBJECTIVE: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study was to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis was extended to observe behaviour of wheeze sounds in different datasets.

    METHODS: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) were calculated from normalized power spectrum. Subsequently, multivariate analysis was performed.

    RESULTS: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level Ʌ = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ɳ2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples Ʌ = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ɳ2 = 0.386-0.568.

    CONCLUSIONS: The results demonstrated dthat severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics.

    Matched MeSH terms: Multivariate Analysis
  12. Masoumi HR, Kassim A, Basri M, Abdullah DK, Haron MJ
    Molecules, 2011 Jun 29;16(7):5538-49.
    PMID: 21716175 DOI: 10.3390/molecules16075538
    An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods.
    Matched MeSH terms: Multivariate Analysis*
  13. Liew, C.Y., Lau, C.Y.
    MyJurnal
    Studies have been carried out to determine the chemical (soluble solid content) and physical (firmness) parameters of locally grown Cavendish banana by near infrared (NIR) spectroscopy. NIR spectra in the wavelength region of 680-2500 nm were obtained from a total of 408 Cavendish bananas of different ripeness indices. Chemometrics using multiple linear regression (MLR) was applied to develop calibration models for prediction of firmness and soluble solid content (SSC) of Cavendish banana. Results showed that NIR spectroscopy had the feasibility for non-destructive determination of the quality of Cavendish banana. The coefficient of determination (R2) for firmness and SSC calibration models at different ripeness indices ranged from 0.78 to 0.86 and 0.75 to 0.96, respectively. The calibration models were validated using independent sets of data and prediction models developed with the root mean square error of prediction (RMSEP) ranged from 0.01 to 0.26 kgf and 0.039 to 0.788 Brix for firmness and SSC, respectively. The multi-index models showed considerable robustness but higher prediction error with RMSEP of 0.336 kgf for firmness and 0.937% Brix for SSC compared to index specific model.
    Matched MeSH terms: Multivariate Analysis
  14. Nur Hanim Mohd Salleh, Husna Hasan
    MyJurnal
    Annual air temperature data obtained from twenty-two meteorological stations across Malaysia are modeled using multiple regression. A correlation test was conducted to find statistical relationship between each of the dependent variables: annual maximum and annual average air temperature and predictor variables: longitude, latitude, elevation and wind speed. Regression models using least square estimation method were developed relating the dependent variables to independent variables and the adequacy of the models is determined by the coefficient of determination. The result shows that the longitude and wind speed factors have a significant influence on the annual air temperature in Malaysia.
    Matched MeSH terms: Multivariate Analysis
  15. Yap SL, Tan XB, Tan CS
    Previous studies have found that luminance contrast may enhance attention and attention is positively correlated with memory. However, little attention has been given to understand the impact of luminance contrast on memory. The present study attempts to address this gap by examining the effect of luminance contrast on attention and memory. A total of 159 undergraduates were randomly assigned to three luminance contrast conditions (high vs. moderate vs. low) and were administered a modified d2 test and modified words memory test. Multivariate analysis of variance showed significant effect of luminance contrast on memory performance. Participants in the high and moderate luminance contrast groups recalled more words than counterparts in the low contrast group. However, the effect of luminance contrast on attention was not significant, though planned comparison found that high contrast group scored higher than low contrast group. The findings not only shed light on improvement of memory but also have implication for design and marketing and consumer behaviours study.
    Matched MeSH terms: Multivariate Analysis
  16. Biswash MR, Sharmin M, Rahman NMF, Farhat T, Siddique MA
    Sains Malaysiana, 2016;45:706-716.
    A field experiment was conducted from June to December, 2013 to study the genetic diversity of 15 modern T. Aman rice
    varieties of Bangladesh (Oryza sativa L.) with a view to assess the superior genotype in future hybridization program
    for developing new rice varieties that is suitable for the target environment. Analysis of variance for each trait showed
    significant differences among the varieties. High heritability associated with high genetic advance in percent of mean
    was observed for plant height and thousand seed weight which indicated that selection for these characters would be
    effective. Hence, thrust has to be given for these characters in future breeding program to improve the yield trait in rice.
    Multivariate analysis based on 10 agronomic characters indicated that the 15 varieties were grouped into four distant
    clusters. The inter cluster distance was maximum between cluster II and cluster IV. The highest intra-cluster distance was
    found in cluster IV. Based on positive value of vector 1 and vector 2, plant height and 1000-seed weight had maximum
    contribution towards genetic divergence. From the results, it can be concluded that the varieties BRRI dhan40, BRRI
    dhan44, BRRI dhan46, BRRI dhan49 and BINA dhan7 may be selected for future hybridization program.
    Matched MeSH terms: Multivariate Analysis
  17. Hamli H, Hamed NA, Azmai SHS, Idris MH
    Trop Life Sci Res, 2020 Jul;31(2):145-158.
    PMID: 32922672 DOI: 10.21315/tlsr2020.31.2.7
    Pachychilidae is one of the freshwater gastropod family which was previously known under the Potamididae and Thiaridae families. Studies on freshwater gastropods especially on conchcology examinantions are still inadequate compared to marine gastropods. Morphological and morphometric studies of gastropods are practically used to identify and differentiate between species and necessary to complement molecular studies due to its low cost and tolerable resolving power of discrimination. The aim of the current study is to provide information on morphological and morphometric characteristics of Pachychilidae in Bintulu, Sarawak stream. A total of 20 individuals from each species of Sulcospira testudinaria, Sulcospira schmidti, Brotia siamensis, and Tylomelania sp. from Pachychilidae familiy were collected at three different sites from a small stream within the Bintulu area. Fourteen measurement of shell morphometrics were converted into proportioned ratios and analysed for univariate and multivariate analysis. Three shell morphometric (Aperture width, AW; Whorl width, WW2; and, Interior anterior length, AINL) of Pachychilidae indicated significant differences (P < 0.05) between species. However, multivariate analysis revealed that these shell morphometrics are pre-eminent factors to discriminate genus Sulcospira, Brotia and Tylomelania, as well as between Sulcospira species. This current study also suggests that these three characteristics are unique to Sulcospira species due to strong distinction among species. Findings on these three characteristics are significant for Sulcospira spp. as this study is the first shell morphometric report on the Pachychilidae species in Sarawak.
    Matched MeSH terms: Multivariate Analysis
  18. Sim SF, Ting W
    Talanta, 2012 Jan 15;88:537-43.
    PMID: 22265538 DOI: 10.1016/j.talanta.2011.11.030
    This paper reports a computational approach for analysis of FTIR spectra where peaks are detected, assigned and matched across samples to produce a peak table with rows corresponding to samples and columns to variables. The algorithm is applied on a dataset of 103 spectra of a broad range of edible oils for exploratory analysis and variable selection using Self Organising Maps (SOMs) and t-statistics, respectively. Analysis on the resultant peak table allows the underlying patterns and the discriminatory variables to be revealed. The algorithm is user-friendly; it involves a minimal number of tunable parameters and would be useful for analysis of a large and complicated FTIR dataset.
    Matched MeSH terms: Multivariate Analysis
  19. Md Ghani NA, Liong CY, Jemain AA
    Forensic Sci Int, 2010 May 20;198(1-3):143-9.
    PMID: 20211535 DOI: 10.1016/j.forsciint.2010.02.011
    The task of identifying firearms from forensic ballistics specimens is exacting in crime investigation since the last two decades. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint'. These fingerprints transfer when a firearm is fired to the fired bullet and cartridge case. The components that are involved in producing these unique characteristics are the firing chamber, breech face, firing pin, ejector, extractor and the rifling of the barrel. These unique characteristics are the critical features in identifying firearms. It allows investigators to decide on which particular firearm that has fired the bullet. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. Therefore, the main objective of this study is the extraction and identification of suitable features from firing pin impression of cartridge case images for firearm recognition. Some previous studies have shown that firing pin impression of cartridge case is one of the most important characteristics used for identifying an individual firearm. In this study, data are gathered using 747 cartridge case images captured from five different pistols of type 9mm Parabellum Vektor SP1, made in South Africa. All the images of the cartridge cases are then segmented into three regions, forming three different set of images, i.e. firing pin impression image, centre of firing pin impression image and ring of firing pin impression image. Then geometric moments up to the sixth order were generated from each part of the images to form a set of numerical features. These 48 features were found to be significantly different using the MANOVA test. This high dimension of features is then reduced into only 11 significant features using correlation analysis. Classification results using cross-validation under discriminant analysis show that 96.7% of the images were classified correctly. These results demonstrate the value of geometric moments technique for producing a set of numerical features, based on which the identification of firearms are made.
    Matched MeSH terms: Multivariate Analysis
  20. Muhammad SA, Frew RD, Hayman AR
    Front Chem, 2015;3:12.
    PMID: 25774366 DOI: 10.3389/fchem.2015.00012
    Compound-specific isotope analysis (CSIA) offers great potential as a tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large data set is analyzed and the isotopic differences between samples are subtle. In the present study, discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate carbon and hydrogen isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis were put into context by comparing the data with the δ(13)C and δ(2)H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical analysis provide a defensible means to differentiate and source-apportion qualitatively similar oils at the molecular level. This approach was able to overcome confounding challenges posed by the near single-point source of origin, i.e., the very subtle differences in isotopic values between the samples.
    Matched MeSH terms: Multivariate Analysis
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