Displaying publications 61 - 80 of 390 in total

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  1. Borhani TN, Saniedanesh M, Bagheri M, Lim JS
    Water Res, 2016 07 01;98:344-53.
    PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038
    In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
    Matched MeSH terms: Linear Models
  2. Shafie AA, Chhabra IK, Wong JHY, Mohammed NS
    Eur J Health Econ, 2021 Jul;22(5):735-747.
    PMID: 33860379 DOI: 10.1007/s10198-021-01287-z
    PURPOSE: To develop a mapping algorithm for generating EQ-5D-3L utility scores from the PedsQL Generic Core Scales (PedsQL GCS) in patients with transfusion-dependent thalassemia (TDT).

    METHODS: The algorithm was developed using data from 345 TDT patients. Spearman's rank correlation was used to evaluate the conceptual overlap between the instruments. Model specifications were chosen using a stepwise regression. Both direct and response mapping methods were attempted. Six mapping estimation methods ordinary least squares (OLS), a log-transformed response using OLS, generalized linear model (GLM), two-part model (TPM), Tobit and multinomial logistic regression (MLOGIT) were tested to determine the root mean squared error (RMSE) and mean absolute error (MAE). Other criterion used were accuracy of the predicted utility score, proportions of absolute differences that was less than 0.03 and intraclass correlation coefficient. An in-sample, leave-one-out cross validation was conducted to test the generalizability of each model.

    RESULTS: The best performing model was specified with three out of the four PedsQL GCS scales-the physical, emotional and social functioning score. The best performing estimation method for direct mapping was a GLM with a RMSE of 0.1273 and MAE of 0.1016, while the best estimation method for response mapping was the MLOGIT with a RMSE of 0.1597 and MAE of 0.0826.

    CONCLUSION: The mapping algorithm developed using the GLM would facilitate the calculation of utility scores to inform economic evaluations for TDT patients when EQ-5D data is not available. However, caution should be exercised when using this algorithm in patients who have poor quality of life.

    Matched MeSH terms: Linear Models
  3. Shahid Hassan, Mohamad Najib Mat Pa, Muhamad Saiful Bahri Yusoff
    MyJurnal
    Background: Summative assessment in postgraduate examination globally employs multiple measures. A standard-setting method decides on pass or fail based on an arbitrarily defined cut-off point on a test score, which is often content expert’s subjective judgment. Contrary to this a standard-setting strategy primarily practices two approaches, a compensatory approach, which decides on overall performance as a sum of all the test scores and a conjunctive approach that requires passing performance for each instrument. However, the challenge using multiple measures is not due to number of measurement tools but due to logic by which the measures are combined to draw inferences on pass or fail in summative assessment. Conjoint University Board of Examination of Masters’ of Otolaryngology and Head-Neck Surgery (ORL-HNS) in Malaysia also uses multiple measures to reach a passing or failing decision in summative assessment. However, the standard setting strategy of assessment is loosely and variably applied to make ultimate decision on pass or fail. To collect the evidences, the summative assessment program of Masters’ of ORL-HNS in School of Medical Sciences at Universiti Sains Malaysia was analyzed for validity to evaluate the appropriateness of decisions in postgraduate medical education in Malaysia. Methodology: A retrospective study was undertaken to evaluate the validity of the conjoint summative assessment results of part II examination of USM candidates during May 2000-May 2011. The Pearson correlation and multiple linear regression tests were used to determine the discriminant and convergent validity of assessment tools. Pearson’s correlation coefficient analyzed the association between assessment tools and the multiple linear regression compared the dominant roles of factor variables in predicting outcomes. Based on outcome of the study, reforms for standard-setting strategy are also recommended towards programming the assessment in a surgical-based discipline. Results: The correlation coefficients of MCQ and essay questions were found not significant (0.16). Long and short cases were shown to have good correlations (0.53). Oral test stood as a component to show fair correlation with written (0.39-0.42) as well as clinical component (0.50-0.66). The predictive values in written tests suggested MCQ predicted by oral (B=0.34, P
    Matched MeSH terms: Linear Models
  4. Alsharif AM, Tan GH, Choo YM, Lawal A
    J Chromatogr Sci, 2017 03 01;55(3):378-391.
    PMID: 27903555 DOI: 10.1093/chromsci/bmw188
    Hollow fiber liquid-phase microextraction (HF-LPME) techniques coupled to chromatographic systems have been widely used for extraction and determination of diverse compounds. HF-LPME was able to provide better results in precision, accuracy, selectivity and enrichment factor, in addition to reduction of matrix effect and carry over. It is applicable within a wide pH range and compatible with most analytical instruments which enable the utilization of HF-LPME in a wide variety of applications. This review focused on the modified HF-LPME techniques, efficiency, comparison to other LPME methods and applications.
    Matched MeSH terms: Linear Models
  5. Shazlin Umar, Azriani Ab Rahman, Aziah Daud, Azizah Othman, Normastura Abd Rahman, Azizah Yusoff, et al.
    MyJurnal
    Objective: The objectives of this study were to determine the effect of a one and a half year educational intervention on the job dissatisfaction of teachers in 30 Community Based Rehabilitation (CBR) centres in Kelantan, Malaysia, and to identify the factors influencing changes in job dissatisfaction following the intervention. Method: Ten educational modules were administered to the teachers. A validated Malay version of Job Content Questionnaire (JCQ) was used pre intervention, mid intervention and post intervention. Result: Repeated Measure ANOVA revealed there was a statistically significant reduction in the mean of job dissatisfaction (p = 0.048). Multiple Linear Regression revealed that co- worker support (β= 0.034 (95% CI = 0.009, 0.059)), having less decision authority (β: -0.023; 95% CI: -0.036, -0.01) and being single (β: -0.107; 95% CI: -0.176,-0.038) were significantly associated with decreases in job dissatisfaction. Conclusion: The intervention program elicited improvement in job satisfaction. Efforts should be made to sustain the effect of the intervention in reducing job dissatisfaction by continuous support visits to CBR centres.
    Matched MeSH terms: Linear Models
  6. Noraishah Othman, Siti Kartom Kamarudin, Muhd Noor Md Yunus, Abd. Halim Shamsuddin, Siti Rozaimah, Zahirah Yaakob
    MyJurnal
    The production of carbon dioxide from Karas woods under argon atmosphere was investigated using a direct pyrolysis-combustion approach. Direct burning was used in this study, using argon for yrolysis and oxygen during combustion to look at the yield of carbon dioxide, produced at different parameters, such as the temperature, retention time and flow rate of argon, as the carrier gas. In this study, a new methodology, 23 response surface central composite design was successfully employed for the experimental design and analysis of results. Central composite experimental design and response surface method were utilized to determine the best operating condition for a maximum carbon dioxide production. Appropriate predictable empirical linear model was developed by incorporating interaction effects of all the variables involved. The results of the analysis revealed that linear equation models fitted well with the experimental for carbon dioxide yield. Nevertheless, the R-Squared obtained using the direct pyrolysis-combustion was 0.7118, indicating that the regression line was not at the best-fitted line.
    Matched MeSH terms: Linear Models
  7. Loke, Shuet Toh
    Malaysian Dental Journal, 2015;38(2):16-36.
    MyJurnal
    Aim: Orthodontic treatment duration is variable and associated with many factors Very few studies looks at operator changes influencing treatment duration and outcome. This study aims to evaluate the influence of operator changes on treatment time and quality.

    Methodology: This is a 4-year cross-sectional retrospective study of preadjusted Edgewise two-arch appliance cases treated by single/ multiple operators and finished/debonded by the author. 60 singleoperator (Group 1) and 82 multiple-operator (Group 2) cases were selected and the Peer Assessment Rating (PAR) Index was used to measure treatment outcome.

    Results: Group 1 (2.31 years, SD.86) had statistically significantly shorter treatment time than Group 2 (3.25 years, SD1.23). Mean % reduction in PAR scores was high (88.7%), although single operators (92%) showed a slightly higher (p=.04) reduction than multiple-operator cases (86.2%). Post-treatment PAR score was slightly higher in Group 2 (4.6, SD5.4) compared with Group 1 (2.6, SD2.9). There was no significant difference in post-treatment PAR scores with operator changes from within and outside the clinic although there was difference in treatment duration (p=.0001). Possible predictors of treatment duration included number of failed/changed appointments, extractions and pre-treatment PAR. Multiple linear regression model showed significant association of treatment time with failed/changed appointments (p=.0001) and number of operators (p=.0001) although this contributed to 57.5% of possible factors (r=.762) .

    Conclusion: Change of operator contributes to increased treatment time by 11.3 months. Although standard of treatment was high in both groups there was slightly better outcomes in single operators. The reduction in PAR score can be predicted more accurately in single operators.
    Matched MeSH terms: Linear Models
  8. Bong CH, Lau TL, Ab Ghani A, Chan NW
    Water Sci Technol, 2016 Oct;74(8):1876-1884.
    PMID: 27789888
    The understanding of how the sediment deposit thickness influences the incipient motion characteristic is still lacking in the literature. Hence, the current study aims to determine the effect of sediment deposition thickness on the critical velocity for incipient motion. An incipient motion experiment was conducted in a rigid boundary rectangular flume of 0.6 m width with varying sediment deposition thickness. Findings from the experiment revealed that the densimetric Froude number has a logarithmic relationship with both the thickness ratios ts/d and ts/y0 (ts: sediment deposit thickness; d: grain size; y0: normal flow depth). Multiple linear regression analysis was performed using the data from the current study to develop a new critical velocity equation by incorporating thickness ratios into the equation. The new equation can be used to predict critical velocity for incipient motion for both loose and rigid boundary conditions. The new critical velocity equation is an attempt toward unifying the equations for both rigid and loose boundary conditions.
    Matched MeSH terms: Linear Models
  9. Rehman IU, Munib S, Ramadas A, Khan TM
    PLoS One, 2018;13(11):e0207758.
    PMID: 30496235 DOI: 10.1371/journal.pone.0207758
    BACKGROUND: The prevalence of chronic kidney disease-associated pruritus (CKD-aP) varies from 22% to 84% among patients receiving hemodialysis. It occurs more frequently at night, and often affects patient's sleep quality. CKD-aP is often unreported by patients, and many do not receive effective treatment. There is, however, a paucity of available data on the prevalence and impact of CKD-aP on patients receiving hemodialysis in Pakistan.

    METHODS: A multicenter cross-sectional study was undertaken from July 2016 to April 2017 at a tertiary care hospitals in Pakistan.

    RESULTS: 354 patients undergoing hemodialysis were studied. 35.6% had CKD for 1-2 years, and 42.4% were receiving hemodialysis for 1-2 years. The prevalence of pruritus was 74%. The median [interquartile range] score for pruritus was 10.0 (out of possible 25) [8.0-12.0]; while the median [interquartile range] Pittsburgh Sleep Quality Index (PSQI) score was 8.0 (out of possible 21) [7.0-10.0]'. Pruritus was significantly correlated with the sleep score (r = 0.423, p<0.001). The results of the multivariate linear regression revealed a positive association between pruritus and age of patients (β = 0.031; 95% CI = 0.002-0.061; p = 0.038) and duration of CKD (β = -0.013; 95% CI = -0.023 --0.003; p = 0.014). Similarly there was a positive association between sleep score and duration of CKD (β = 0.010; 95% CI = 0.002-0.019; p = 0.012) and pruritus (β = 0.143; 95% CI = 0.056-0.230; p = 0.001).

    CONCLUSIONS: Chronic kidney disease-associated pruritus is very common in patients receiving hemodialysis in Pakistan. Pruritus is significantly associated with poor sleep quality.

    Matched MeSH terms: Linear Models
  10. Siti Zuliana Md Z, Siti Fardaniah Abdul A
    The effectiveness of training is an important aspect in the development of training. After investing a lot of money to organize a training program, the organization often wants to know about the effectiveness of training given to trainee as well as how it can gives impact to the organization. This study was conducted to evaluate the effectiveness of training tested through learning performance among trainees that undergo a transition in the Perbadanan Hal Ehwal Bekas Angkatan Tentera (PERHEBAT). In this study, personal characteristics and training program characteristics acted as the independent variables in predicting learning performance. The instrument used in this study was adapted from Trainee Characteristic Scale, Training Program Characteristic Scale and Training Effectiveness Scale by Siti Fardaniah (2013) for personal characteristics, training program characteristics and learning performance. Questionnaires to measure the dimension of training transfer for the training characteristics was adapted from the Learning Transfer System Inventory (LTSI) by Holton et al. (2000). Data obtained were analyzed using Statistical Package for Social Sciences (SPSS) version 23. The multiple linear regression analysis indicated that extrinsic orientation, self-efficacy and organizational commitment have significant influence on learning performance. Relevance of training content and learning transfer design also affecedt learning performance. Findings in this study can be used as a reference to improve training effectiveness by focusing on personal characteristics and training characteristics conducted in PERHEBAT.
    Matched MeSH terms: Linear Models
  11. Mohd Tahir Ismail, Zaidi Isa
    Sains Malaysiana, 2006;35:55-62.
    The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time series models are typically designed to accommodate these nonlinear features in the data. In this paper, we use portmanteau test and structural change test to detect nonlinear feature in three ASEAN countries exchange rates (Malaysia, Singapore and Thailand). It is found that the null hypothesis of linearity is rejected and there is evidence of structural breaks in the exchange rates series. Therefore, the decision of using regime switching model in this study is justified. Using model selection criteria (AIC, SBC, HQC), we compare the in-sample fitting between two types of regime switching model. The two regime switching models we considered were the Self-Exciting Threshold Autoregressive (SETAR) model and the Markov switching Autoregressive (MS-AR) model where these models can explain the abrupt changes in a time series but differ as how they model the movement between regimes. From the AIC, SBC and HQC values, it is found that the MS -AR model is the best fitted model for all the return series. In addition, the regime switching model also found to perform better than simple autoregressive model in in-sample fitting. This result justified that nonlinear model give better in-sample fitting than linear model.
    Matched MeSH terms: Linear Models
  12. Behrooz Gharleghi, Abu Hassan Shaari Md Nor, Tamat Sarmidi
    Sains Malaysiana, 2014;43:1609-1622.
    Linear time series models are not able to capture the behaviour of many financial time series, as in the cases of exchange rates and stock market data. Some phenomena, such as volatility and structural breaks in time series data, cannot be modelled implicitly using linear time series models. Therefore, nonlinear time series models are typically designed to accommodate for such nonlinear features. In the present study, a nonlinearity test and a structural change test are used to detect the nonlinearity and the break date in three ASEAN currencies, namely the Indonesian Rupiah (IDR), the Malaysian Ringgit (MYR) and the Thai Baht (THB). The study finds that the null hypothesis of linearity is rejected and evidence of structural breaks exist in the exchange rates series. Therefore, the decision to use the self-exciting threshold autoregressive (SETAR) model in the present study is justified. The results showed that the SETAR model, as a regime switching model, can explain abrupt changes in a time series. To evaluate the prediction performance of SETAR model, an Autoregressive Integrated Moving Average (ARIMA) model used as a benchmark. In order to increase the accuracy of prediction, both models are combined with an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model. The prediction results showed that the construct model of SETAR-EGARCH performs better than that of the ARIMA model and the combined ARIMA and EGARCH model. The results indicated that nonlinear models give better fitting than linear models.
    Matched MeSH terms: Linear Models
  13. Melek Zeng?n, Semra Sayg?n, Nazm? Polat
    Sains Malaysiana, 2015;44:657-662.
    Otoliths, which can be used for the evaluation of relationships between the environment and organisms, are structures
    consisting of calcium carbonate. The aim of this study was to realize the shape analysis. In addition, it is to detect the
    characteristics of otolith biometrics in order to determine the relationship between the fish size of Engraulis encrasicolus
    L. from the Black and Marmara Seas. The samples were obtained from the Black and Marmara Seas between December
    2013 and February 2014. The relationships between the TL (Total length) and OL (Otolith length), TL and OB (Otolith
    breadth), and TL and OW (Otolith weight) were determined using the linear regression equation. Form factor, roundness,
    circularity and rectangularity were used for shape analyses. According to the data, there was no difference between
    localities (p>0.05). Moreover, there was no difference between the left and right otoliths of the individuals sampled from
    the same locality (p>0.05). According to the regression coefficient for relationships of TL-OL, TL-OB and TL-OW, otolith
    length was identified as the best index for estimating fish length (r
    2
    >0.70). It showed that index values were statistically
    different between two populations (p<0.001).
    Matched MeSH terms: Linear Models
  14. Murphy S, Arora D, Kruijssen F, McDougall C, Kantor P
    PLoS One, 2020;15(3):e0229286.
    PMID: 32231375 DOI: 10.1371/journal.pone.0229286
    Over the last decade, Egypt's aquaculture sector has expanded rapidly, which has contributed substantially to per capita fish supply, and the growth of domestic fish markets and employment across the aquaculture value chain. Despite the growing importance of aquaculture sector in Egyptian labour force, only a few studies have explored the livelihoods of Egypt's women and men fish retailers. Even fewer studies have examined gender-based market constraints experienced by these informal fish retailers. This study uses sex-disaggregated data collected in 2013 in three governorates of Lower Egypt to examine the economic and social constraints to scale of enterprises between women (n = 162) and men informal fish retailers (n = 183). Specifically, we employ linear regression method to determine the correlates of enterprise performance. We found that both women and men retailers in the informal fish market earn low profits and face livelihood insecurities. However, women's enterprise performance is significantly lower than that of men even after controlling for individual socio-economic and retailing characteristics. Specifically, the burden of unpaid household work and lack of support therein impedes women's ability to generate higher revenues. These findings strengthen the argument for investing in understanding how gender norms and attitudes affect livelihood options and outcomes. This leads to recommendations on gender-responsive interventions that engage with both men and women and enhance the bargaining power and collective voice of fish retailers.
    Matched MeSH terms: Linear Models
  15. Jun Zhao, Feifei Wang, Yifan Lu
    Sains Malaysiana, 2017;46:2223-2229.
    Formation lithology identification is an indispensable link in oil and gas exploration. Precision of the traditional recognition method is difficult to guarantee when trying to identify lithology of particular formation with strong heterogeneity and complex structure. In order to remove this defect, multivariate membership function discrimination method is proposed, which regard to lithology identification as a linear model in the fuzzy domain and obtain aimed result with the multivariate membership function established. It is indicated by the test on lower carboniferous Bachu group bioclastic limestone section and Donghe sandstone section reservoir on T Field H area that satisfactory accuracy can be achieved in both clastic rock and carbonate formation and obvious advantages are unfold when dealing with complex formations, which shows a good application prospect and provides a new thought to solve complex problems on oilfield exploration and development with fuzzy theory.
    Matched MeSH terms: Linear Models
  16. Terence Ricky Chiu, Md Firoz Khan, Mohd Shahrul Mohd Nadzir, Haris Hafizal Abdul Hamid, Mohd Talib Latif, Mohd Shahrul Mohd Nadzir, et al.
    Sains Malaysiana, 2018;47:871-882.
    The individual compounds and sources of polycyclic aromatic hydrocarbon (PAHs) were studied in the surface sediments
    at 32 locations in the tourism area of Langkawi Island. A total of 15 PAHs were determined and quantified by gas
    chromatography coupled with mass spectrometry (GC-MS). The total PAH concentrations of surface sediments from
    Langkawi Island ranged from 228.13 to 990.25 ng/g and they were classified as being in the low to moderate pollution
    range. All sampling stations were dominated by high molecular weight PAHs with 4 rings (31.59%) and 5-6 rings (42.73%).
    The diagnostic ratio results showed that in most cases, the sampling stations have pyrogenic input. Further analysis
    using principal component analysis (PCA) combined with absolute principal component score (APCS) and multiple linear
    regression (MLR) showed that the natural gas emissions contributed to 57% of the total PAH concentration, 22% from the
    incomplete combustion and pyrolysis of fuel, 15% from pyrogenic and petrogenic sources and 6% from an undefined source.
    Matched MeSH terms: Linear Models
  17. Wong YJ, Arumugasamy SK, Chung CH, Selvarajoo A, Sethu V
    Environ Monit Assess, 2020 Jun 17;192(7):439.
    PMID: 32556670 DOI: 10.1007/s10661-020-08268-4
    Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.
    Matched MeSH terms: Linear Models
  18. Ammatawiyanon L, Tongkumchum P, Lim A, McNeil D
    Malar J, 2022 Nov 15;21(1):334.
    PMID: 36380322 DOI: 10.1186/s12936-022-04363-8
    BACKGROUND: Malaria remains a serious health problem in the southern border provinces of Thailand. The issue areas can be identified using an appropriate statistical model. This study aimed to investigate malaria for its spatial occurrence and incidence rate in the southernmost provinces of Thailand.

    METHODS: The Thai Office of Disease Prevention and Control, Ministry of Public Health, provided total hospital admissions of malaria cases from 2008 to 2020, which were classified by age, gender, and sub-district of residence. Sixty-two sub-districts were excluded since they had no malaria cases. A logistic model was used to identify spatial occurrence patterns of malaria, and a log-linear regression model was employed to model the incidence rate after eliminating records with zero cases.

    RESULTS: The overall occurrence rate was 9.8% and the overall median incidence rate was 4.3 cases per 1,000 population. Malaria occurence peaked at young adults aged 20-29, and subsequently fell with age for both sexes, whereas incidence rate increased with age for both sexes. Malaria occurrence and incidence rates fluctuated; they appeared to be on the decline. The area with the highest malaria occurrence and incidence rate was remarkably similar to the area with the highest number of malaria cases, which were mostly in Yala province's sub-districts bordering Malaysia.

    CONCLUSIONS: Malaria is a serious problem in forest-covered border areas. The correct policies and strategies should be concentrated in these areas, in order to address this condition.

    Matched MeSH terms: Linear Models
  19. Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, et al.
    J Integr Neurosci, 2017;16(3):275-289.
    PMID: 28891512 DOI: 10.3233/JIN-170016
    Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).
    Matched MeSH terms: Linear Models
  20. Oettli P, Behera SK, Yamagata T
    Sci Rep, 2018 02 02;8(1):2271.
    PMID: 29396527 DOI: 10.1038/s41598-018-20298-0
    The influence of local conditions and remote climate modes on the interannual variability of oil palm fresh fruit bunches (FFB) total yields in Malaysia and two major regions (Peninsular Malaysia and Sabah/Sarawak) is explored. On a country scale, the state of sea-surface temperatures (SST) in the tropical Pacific Ocean during the previous boreal winter is found to influence the regional climate. When El Niño occurs in the Pacific Ocean, rainfall in Malaysia reduces but air temperature increases, generating a high level of water stress for palm trees. As a result, the yearly production of FFB becomes lower than that of a normal year since the water stress during the boreal spring has an important impact on the total annual yields of FFB. Conversely, La Niña sets favorable conditions for palm trees to produce more FFB by reducing chances of water stress risk. The region of the Leeuwin current also seems to play a secondary role through the Ningaloo Niño/ Niña in the interannual variability of FFB yields. Based on these findings, a linear model is constructed and its ability to reproduce the interannual signal is assessed. This model has shown some skills in predicting the total FFB yield.
    Matched MeSH terms: Linear Models
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