Displaying publications 1 - 20 of 167 in total

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  1. Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration
    Ong P, Jian J, Li X, Yin J, Ma G
    Spectrochim Acta A Mol Biomol Spectrosc, 2024 Jan 15;305:123477.
    PMID: 37804706 DOI: 10.1016/j.saa.2023.123477
    Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
    Matched MeSH terms: Least-Squares Analysis
  2. Variation of δ2H, δ18O & δ13C in crude palm oil from different regions in Malaysia: Potential of stable isotope signatures as a key traceability parameter
    Muhammad SA, Seow EK, Mohd Omar AK, Rodhi AM, Mat Hassan H, Lalung J, et al.
    Sci Justice, 2018 Jan;58(1):59-66.
    PMID: 29332695 DOI: 10.1016/j.scijus.2017.05.008
    A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (δ13C) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (δ2H and δ18O) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk δ2H, δ18O and δ13C of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. δ18O was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils.
    Matched MeSH terms: Least-Squares Analysis
  3. Fulltext Urine Untargeted Metabolomic Profiling Is Associated with the Dietary Pattern of Successful Aging among Malaysian Elderly
    Nik Mohd Fakhruddin NNI, Shahar S, Ismail IS, Ahmad Azam A, Rajab NF
    Nutrients, 2020 Sep 23;12(10).
    PMID: 32977370 DOI: 10.3390/nu12102900
    Food intake biomarkers (FIBs) can reflect the intake of specific foods or dietary patterns (DP). DP for successful aging (SA) has been widely studied. However, the relationship between SA and DP characterized by FIBs still needs further exploration as the candidate markers are scarce. Thus, 1H-nuclear magnetic resonance (1H-NMR)-based urine metabolomics profiling was conducted to identify potential metabolites which can act as specific markers representing DP for SA. Urine sample of nine subjects from each three aging groups, SA, usual aging (UA), and mild cognitive impairment (MCI), were analyzed using the 1H-NMR metabolomic approach. Principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were applied. The association between SA urinary metabolites and its DP was assessed using the Pearson's correlation analysis. The urine of SA subjects was characterized by the greater excretion of citrate, taurine, hypotaurine, serotonin, and melatonin as compared to UA and MCI. These urinary metabolites were associated with alteration in "taurine and hypotaurine metabolism" and "tryptophan metabolism" in SA elderly. Urinary serotonin (r = 0.48, p < 0.05) and melatonin (r = 0.47, p < 0.05) were associated with oat intake. These findings demonstrate that a metabolomic approach may be useful for correlating DP with SA urinary metabolites and for further understanding of SA development.
    Matched MeSH terms: Least-Squares Analysis
  4. Fulltext Untargeted and Targeted Metabolomic Profiling of Australian Indigenous Fruits
    Lim V, Gorji SG, Daygon VD, Fitzgerald M
    Metabolites, 2020 Mar 19;10(3).
    PMID: 32204361 DOI: 10.3390/metabo10030114
    Selected Australian native fruits such as Davidson's plum, finger lime and native pepperberry have been reported to demonstrate potent antioxidant activity. However, comprehensive metabolite profiling of these fruits is limited, therefore the compounds responsible are unknown, and further, the compounds of nutritional value in these native fruits are yet to be described. In this study, untargeted and targeted metabolomics were conducted using the three fruits, together with assays to determine their antioxidant activities. The results demonstrate that targeted free and hydrolysed protein amino acids exhibited high amounts of essential amino acids. Similarly, important minerals like potassium were detected in the fruit samples. In antioxidant activity, Davidson's plum reported the highest activity in ferric reducing power (FRAP), finger lime in antioxidant capacity (ABTS), and native pepperberry in free radical scavenging (DPPH) and phosphomolybdenum assay. The compounds responsible for the antioxidant activity were tentatively identified using untargeted GC×GC-TOFMS and UHPLC-QqQ-TOF-MS/MS metabolomics. A clear discrimination into three clusters of fruits was observed using principal component analysis (PCA) and partial least squares (PLS) analysis. The correlation study identified a number of compounds that provide the antioxidant activities. GC×GC-TOFMS detected potent aroma compounds of limonene, furfural, and 1-R-α-pinene. Based on the untargeted and targeted metabolomics, and antioxidant assays, the nutritional potential of these Australian bush fruits is considerable and supports these indigenous fruits in the nutraceutical industry as well as functional ingredients for the food industry, with such outcomes benefiting Indigenous Australian communities.
    Matched MeSH terms: Least-Squares Analysis
  5. Fulltext Understanding How Nutrition Literacy Links to Dietary Adherence in Patients Undergoing Maintenance Hemodialysis: A Theoretical Exploration Using Partial Least Squares Structural Equation Modeling
    Lim JH, Chinna K, Khosla P, Karupaiah T, Daud ZAM
    Int J Environ Res Public Health, 2020 10 14;17(20).
    PMID: 33066603 DOI: 10.3390/ijerph17207479
    Dietary non-adherence is pervasive in the hemodialysis (HD) population. Health literacy is a plausible predictor of dietary adherence in HD patients, but its putative mechanism is scarcely studied. Thus, this study aimed to establish the causal model linking nutrition literacy to dietary adherence in the HD population. This was a multi-centre, cross-sectional study, involving 218 randomly selected multi-ethnic HD patients from nine dialysis centres in Klang Valley, Malaysia. Dietary adherence and self-management skills were assessed using validated End-Stage Renal Disease Adherence Questionnaire and Perceived Kidney/Dialysis Self-Management Scale, respectively. Validated self-developed scales were used to gauge nutrition literacy, dietary knowledge and Health Belief Model constructs. Relationships between variables were examined by multiple linear regressions and partial least squares structural equation modeling. Limited nutrition literacy was evident in 46.3% of the HD patients, associated with older age, lower education level, and shorter dialysis vintage. Dietary adherence rate was at 34.9%. Nutrition literacy (β= 0.390, p < 0.001) was an independent predictor of dietary adherence, mediated by self-efficacy (SIE = 0.186, BC 95% CI 0.110-0.280) and self-management skills (SIE = 0.192, BC 95% CI 0.103-0.304). Thus, nutrition literacy-enhancing strategies targeting self-efficacy and self-management skills should be considered to enhance dietary adherence in the HD population.
    Matched MeSH terms: Least-Squares Analysis
  6. Fulltext UV-visible chemometrics approach for the determination of selected adulterants in claimed premixed coffee
    Muhammad Zul Fayyadh Azizo Rahman, Chong, Hui Wen, Lim, Vuanghao
    Malaysian Journal of Medicine and Health Sciences, 2018;14(102):1-6.
    MyJurnal
    Adulterated premixed coffees have turned into an issue in Malaysia lately and have caught the eye of the authorities due to death reports linked to these products. The major cause of this issue is reported that these premixed coffees have passed food inspection test and eventually released to the market for public consumption. These coffees were claimed to be spiked with several sexual enhancers like sildenafil, tadalafil, and vardenafill, which are common drugs used to treat erectile dysfunction. Methods: Chemometrics approach using UV-Vis spectroscopy was developed to detect the selected sexual enhancer drugs found in commercial coffees by employing SIMCA-P software for the multivariate statistical analysis. Seven brands of coffee samples were purchased from local stores, and 30 sachets each were tested, hence totalling to 210 samples. Each sample was named H, J, G, W, N, T, and K, respectively. Results: Three multivariate models were generated, namely principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and partial least squares discriminant analysis (PLS-DA). OPLS-DA was selected as the best model for the overall results as it displayed minimal discriminate. Sildenafil, tadalafil, and vardenafil were detected in sample H, while vardenafil in brand J, and none in samples G, W, N, T, and K. Conclusion: OPLS-DA analysis showed discrimination for the sexual enhancer drugs in two brands of premixed coffee. The UV-Vis spectroscopy-based chemometrics method proved to be reliable and efficient in determining the selected drugs, as well as in saving time and cost.
    Matched MeSH terms: Least-Squares Analysis
  7. Fulltext UHPLC-UV/PDA Method Validation for Simultaneous Quantification of Luteolin and Apigenin Derivatives from Elaeis guineensis Leaf Extracts: An Application for Antioxidant Herbal Preparation
    Che Zain MS, Osman MF, Lee SY, Shaari K
    Molecules, 2021 Feb 19;26(4).
    PMID: 33669484 DOI: 10.3390/molecules26041084
    Luteolin and apigenin derivatives present in oil palm (Elaeis guineensis) leaves (OPL) are reported to possess excellent antioxidant properties relating to numerous health benefits. To meet the global demand for flavonoids, OPL, which is plentifully generated as an agricultural by-product from oil palm plantations, can be further exploited as a new source of natural antioxidant compounds. However, to produce a standardized herbal preparation, validation of the quantification method for these compounds is required. Therefore, in this investigation, we developed and validated an improved and rapid analytical method, ultra-high-performance liquid chromatography equipped with ultraviolet/photodiode array (UHPLC-UV/PDA) for the quantification of 12 luteolin and apigenin derivatives, particularly focusing on flavonoid isomeric pairs: orientin/isoorientin and vitexin/isovitexin, present in various OPL extracts. Several validation parameters were assessed, resulting in the UHPLC-UV/PDA technique offering good specificity, linearity, accuracy, precision, and robustness, where the values were within acceptable limits. Subsequently, the validated method was employed to quantify luteolin and apigenin derivatives from OPL subjected to different drying treatments and extraction with various solvent systems, giving total luteolin (TLC) and apigenin content (TAC) in the range of 2.04-56.30 and 1.84-160.38 µg/mg extract, respectively. Additionally, partial least square (PLS) analysis disclosed the combination of freeze dry-aqueous methanol yielded OPL extracts with high TLC and TAC, which are strongly correlated with antioxidant activity. Therefore, we provide the first validation report of the UHPLC-UV/PDA method for quantification of luteolin and apigenin derivatives present in various OPL extracts, suggesting that this approach could be employed in standardized herbal preparations by adopting orientin, isoorientin, vitexin, and isovitexin as chemical markers.
    Matched MeSH terms: Least-Squares Analysis
  8. Fulltext Transmission lines modelling based on RLC passive and active filter design
    Ahmed Qasim Turki, Nashiren Farzilah Mailah, Ahmed H. Sabry
    Pertanika Journal of Science & Technology, 2017;25(102):235-242.
    MyJurnal
    This paper presents a transmission line (TL) modelling which is based upon vector fitting algorithm
    and RLC passive filter design. Frequency Response Analysis (FRA) is utilised for behaviour prediction and fault diagnosis. The utilities of the measured FRA data points need to be enhanced with suitable modelling category to facilitate the modelling and analysis process. This research proposes a new method for modelling the transmission line based on a rational approximation function which can be extracted through the Vector Fitting (VF) method, based on the frequency response measured data points. A set of steps needs to be implemented to achieve this by setting up an extracted partial fraction approximation, which results from a least square RMS error via VF. Active and passive filter design circuits are used to construct the model of the Transmission line. The RLC design representation was implemented for modelling the system physically while MATLAB Simulink was used to verify the results.
    Matched MeSH terms: Least-Squares Analysis
  9. The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil
    Rohman A, Che Man YB
    Food Chem, 2011 Nov 15;129(2):583-588.
    PMID: 30634271 DOI: 10.1016/j.foodchem.2011.04.070
    Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000-650cm-1 on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R2) of 0.999.
    Matched MeSH terms: Least-Squares Analysis
  10. The rural consumer adoption of sustainable energy: a PLS-SEM-ANN approach of conceptual model development and cross-country validation of Pakistan and Malaysia
    Yin YC, Ahmed J, Nee AYH, Hoe OK
    Environ Sci Pollut Res Int, 2023 Jan;30(3):5881-5902.
    PMID: 35982392 DOI: 10.1007/s11356-022-22271-x
    Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are finally making in road to provide cheap and clean energy sources to rural population. As a result, literatures on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to fulfill this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been applied to satisfy both linear and non-linear regression assumptions, respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results confirm that branding, economic, and altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality, and ease of use are also important. The results offer wide-ranging theoretical and managerial implications.
    Matched MeSH terms: Least-Squares Analysis
  11. The relationship between air pollution, fossil fuel energy consumption, and water resources in the panel of selected Asia-Pacific countries
    Rafindadi AA, Yusof Z, Zaman K, Kyophilavong P, Akhmat G
    Environ Sci Pollut Res Int, 2014 Oct;21(19):11395-400.
    PMID: 24898296 DOI: 10.1007/s11356-014-3095-1
    The objective of the study is to examine the relationship between air pollution, fossil fuel energy consumption, water resources, and natural resource rents in the panel of selected Asia-Pacific countries, over a period of 1975-2012. The study includes number of variables in the model for robust analysis. The results of cross-sectional analysis show that there is a significant relationship between air pollution, energy consumption, and water productivity in the individual countries of Asia-Pacific. However, the results of each country vary according to the time invariant shocks. For this purpose, the study employed the panel least square technique which includes the panel least square regression, panel fixed effect regression, and panel two-stage least square regression. In general, all the panel tests indicate that there is a significant and positive relationship between air pollution, energy consumption, and water resources in the region. The fossil fuel energy consumption has a major dominating impact on the changes in the air pollution in the region.
    Matched MeSH terms: Least-Squares Analysis
  12. Fulltext The performance of robust modification of breusch-godfrey test in the presence of outliers
    Lim, H. A., Midi, H.
    Pertanika Journal of Science & Technology, 2014;22(1):81-93.
    MyJurnal
    Autocorrelation problem causes unduly effects on the variance of Ordinary Least Squares (OLS) estimates. Hence, it is very essential to detect the autocorrelation problem so that appropriate remedial measures can be taken. The Breusch-Godfrey (BG) test is the most popular and commonly used test for the detection of autocorrelation. Since this test is based on the OLS estimates, which are not robust, it is easily affected by outliers. In this paper, we propose a robust Breusch-Godfrey (MBG) test which is not easily affected by outliers. The results of the study indicate that the MBG test is more powerful than the BG test in the detection of autocorrelation problem.
    Matched MeSH terms: Least-Squares Analysis
  13. The influence of hazard control and prevention toward safety behaviors and safety outcomes in coal-fired power plants using PLS-SEM
    Arifin K, Ali MXM, Abas A, Ahmad MA, Ahamad MA, Sahimi AS
    J Safety Res, 2023 Sep;86:376-389.
    PMID: 37718065 DOI: 10.1016/j.jsr.2023.07.017
    INTRODUCTION: The electrical utility industry, which plays a vital role in sustaining other sectors, contributes to high occupational accident rates in the utility industries. The high accident rate shows that there has been insufficient effort made to control unsafe actions and conditions in the workplace. This study aims to examine the influence of hazard control and prevention as leading indicators of safety behaviors and outcomes in coal-fired power plants in Malaysia.

    METHODS: This quantitative research was conducted by distributing survey questionnaires randomly to five coal-fired power plants in Peninsular Malaysia. A total of 340 respondents were involved in this research. Partial least squares structural equation modeling (PLS-SEM) analysis was performed using SmartPLS to validate and examine the relationship of the proposed model.

    RESULTS: The results validate the construct of hazard control and prevention consisting of planning, action, managing, and verifying, while the safety outcomes construct consists of occupational accidents, fatal accidents, near misses, and lost time injuries. The results indicate that hazard control and prevention significantly relate to safety compliance, safety participation, safety motivation, and safety knowledge. Moreover, safety outcomes were influenced negatively by hazard control and prevention through safety compliance.

    CONCLUSION: The model provides a better understanding of the influence of hazard control and prevention on safety behavior and outcomes.

    PRACTICAL APPLICATIONS: The model can be used as guidance for practitioners and researchers in planning and implementing hazard control and prevention to improve health and safety in the workplace.

    Matched MeSH terms: Least-Squares Analysis
  14. Fulltext The impact of task characteristics on the performance of nursing teams
    Ahmad Zawawi A, Nasurdin AM
    Int J Nurs Sci, 2017 Jul 10;4(3):285-290.
    PMID: 31406754 DOI: 10.1016/j.ijnss.2017.03.009
    Purpose: This study sought to examine the relationship between team task features and team task performance. Team task performance revolved around the team's technical knowledge and the technical core activities of the organization. On the other hand, team task characteristics include task identity, task significance, and task interdependence.

    Methods: This study involved a total of 300 nursing teams (1436 individual nurses) from seven state hospitals in Peninsular Malaysia. Data were collected using two sets of questionnaires which were initially distributed to 320 teams. One set was given to the team members and another set was given to the team leaders. Of the 320 sets sent out, 300 sets were returned. Responses were then combined and aggregated to the team level to get the team's final score. Analyses of the hypotheses were done using Partial Least Squares (PLS) through assessment of the measurement and structural model.

    Results: Results from the path analysis revealed that of the three dimensions of team task attributes, only task significance was positively and significantly related to team task performance (β = 0.076, P > 0.05), while task identity (β = 0.076, P > 0.05) and task interdependence (β = -0.037, P > 0.05) were found unrelated to team task performance.

    Conclusions: This study demonstrated that task significance is important to predict team task performance. Task significance reflects meaningfulness and nobility of tasks, thus elevate the desire to perform better in each assigned task.

    Matched MeSH terms: Least-Squares Analysis
  15. Fulltext The impact of service quality on business commitment in B2B segment of agribusiness: An exploratory study of HORECA sector in Malaysia
    Tey, Y.S., Brindal, M., Fatimah, M.A., Kusairi, M.N., Ahmad Hanis, I.A.H., Suryani, D.
    International Food Research Journal, 2014;21(3):883-889.
    MyJurnal
    In competitive markets, agribusiness firms have embarked on improving their service quality for building and maintaining a profitable relationship with their customers. However, such impact of service quality on business commitment has not been empirically investigated. To fill this gap, this study explores the relationship between service quality and commitment, using a case of supplier selection of fresh produce by hotel, restaurant, and catering (HORECA) sector in Malaysia. Using SERVQUAL as the main component of the conceptual framework, the relevant information was collected from 195 random HORECA operators and analyzed using partial least squares. The results indicate that service quality explains little of HORECA’s decision to stay with their current suppliers. While most service quality factors were insignificant, “responsiveness” in term of providing delivery service had a statistically significant positive impact on HORECA’s contractual arrangement with their current suppliers. These findings imply that quality service is being seen as a supplement; economic factors (e.g., prices and their stability, credit term) are likely to be the key drivers affecting buyer-seller relationships. If suppliers want to stay on course, they have to improve their service quality and focus more on delivery service. In addition, more research is needed in this relatively new area.
    Matched MeSH terms: Least-Squares Analysis
  16. The effects of high-fat diet and metformin on urinary metabolites in diabetes and prediabetes rat models
    Lee YF, Sim XY, Teh YH, Ismail MN, Greimel P, Murugaiyah V, et al.
    Biotechnol Appl Biochem, 2021 Oct;68(5):1014-1026.
    PMID: 32931602 DOI: 10.1002/bab.2021
    High-fat diet (HFD) interferes with the dietary plan of patients with type 2 diabetes mellitus (T2DM). However, many diabetes patients consume food with higher fat content for a better taste bud experience. In this study, we examined the effect of HFD on rats at the early onset of diabetes and prediabetes by supplementing their feed with palm olein oil to provide a fat content representing 39% of total calorie intake. Urinary profile generated from liquid chromatography-mass spectrometry analysis was used to construct the orthogonal partial least squares discriminant analysis (OPLS-DA) score plots. The data provide insights into the physiological state of an organism. Healthy rats fed with normal chow (NC) and HFD cannot be distinguished by their urinary metabolite profiles, whereas diabetic and prediabetic rats showed a clear separation in OPLS-DA profile between the two diets, indicating a change in their physiological state. Metformin treatment altered the metabolomics profiles of diabetic rats and lowered their blood sugar levels. For prediabetic rats, metformin treatment on both NC- and HFD-fed rats not only reduced their blood sugar levels to normal but also altered the urinary metabolite profile to be more like healthy rats. The use of metformin is therefore beneficial at the prediabetes stage.
    Matched MeSH terms: Least-Squares Analysis
  17. Fulltext The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set
    Mamuda M, Sathasivam S
    MATEMATIKA, 2017;33(1):11-19.
    MyJurnal
    Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive NeuroFuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.
    Matched MeSH terms: Least-Squares Analysis
  18. Fulltext The determinants of total fertility rate in selected Asian countries: an empirical study using panel data
    Asef Raiyan Hoque, Mohd Yusof Ibrahim, Mohammad Zahirul Hoque
    Malaysian Journal of Medicine and Health Sciences, 2019;15(106):18-18.
    MyJurnal
    Introduction: In recent years, the variation in total fertility rate (TFR) has sparked public interest for demographic concerns on the global population shift towards an older age structure. This study aims to investigate the determi-nants of total fertility rate among Brunei, Indonesia, Malaysia and Philippines East Asian Growth Area (BIMP-EAGA) region. Methods: Our empirical study consists of data collected from the United Nations Development Report of the UNDP, World Development Indicators (WDI) of the World Bank and World Health Organization (WHO) report 2018. We investigated the socio-economic determinants of fertility rate by analyzing a panel data set consisting of 28 years from 1990-2017 of the four countries. A statistical and econometric software EViews version 10 (HIS Global Inc., Irvine, CA, USA) were used to run a Pearson’s Correlation and a multiple regression analysis by panel least squares method. To investigate the determinants of TFR we have selected five socio- economic factors, these are- Infant mortality rate (IMR), Gross National Income Per Capita, PPP (GNI), Human Development Index (HDI), percentage of population living in urban areas (URB) and lastly Female Labor Force Participation Rate (FLP). Results:Pearson’s correlation showed that a statistically significant negative relationship exists between TFR and the 3 vari-ables- GNI, URB and HDI. A statistically strong positive relationship exists between IMR and TFR. However, our results from the empirical multiple regression model indicates that there is a statistically significant negative relation-ship exists between TFR and two of the independent variables GNI and FLP. Conclusion: The results of present study showed that an increase in the national income and female labor participation rate in the workforce could result in a decrease in total fertility rate. These findings may have implications for countries national policy for planning, development and resource allocation.
    Matched MeSH terms: Least-Squares Analysis
  19. Fulltext The control chart technique for the detection of the problem of bad data in state estimation power system
    Khan, Zahid, Radzuan Razali, Hanita Daud, Nursyarizal Mohd Nor, Fotuhi-Firuzabad, Mahmud
    Pertanika Journal of Science & Technology, 2017;25(3):825-834.
    MyJurnal
    State estimation plays a vital role in the security analysis of a power system. The weighted least squares method is one of the conventional techniques used to estimate the unknown state vector of the power system. The existence of bad data can distort the reliability of the estimated state vector. A new algorithm based on the technique of quality control charts is developed in this paper for detection of bad data. The IEEE 6-bus power system data are utilised for the implementation of the proposed algorithm. The output of the study shows that this method is practically applicable for the separation of bad data in the problem of power system state estimation.
    Matched MeSH terms: Least-Squares Analysis
  20. The battle of health with environmental evils of Asian countries: promises to keep
    Qureshi MI, Khan NU, Rasli AM, Zaman K
    Environ Sci Pollut Res Int, 2015 Aug;22(15):11708-15.
    PMID: 25854212 DOI: 10.1007/s11356-015-4440-8
    The objective of the study is to examine the relationship between environmental indicators and health expenditures in the panel of five selected Asian countries, over the period of 2000-2013. The study used panel cointegration technique for evaluating the nexus between environment and health in the region. The results show that energy demand, forest area, and GDP per unit use of energy have a significant and positive impact on increasing health expenditures in the region. These results have been confirmed by single equation panel cointegration estimators, i.e., fully modified ordinary least squares (FMOLS), dynamic OLS (DOLS), and canonical cointegrating regression (CCR) estimators. In addition, the study used robust least squares regression to confirm the generalizability of the results in Asian context. All these estimators indicate that environmental indicators escalate the health expenditures per capita in a region; therefore, Asian countries should have to upsurge health expenditures for safeguard from environmental evils in a region.
    Matched MeSH terms: Least-Squares Analysis
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