Displaying publications 21 - 40 of 167 in total

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  1. Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling
    Wang W, Zhang F, Zhao Q, Liu C, Jim CY, Johnson VC, et al.
    J Environ Manage, 2023 Oct 01;343:118249.
    PMID: 37245314 DOI: 10.1016/j.jenvman.2023.118249
    Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.
    Matched MeSH terms: Least-Squares Analysis
  2. Rapid assessment of total MCPD esters in palm-based cooking oil using ATR-FTIR application and chemometric analysis
    Goh KM, Maulidiani M, Rudiyanto R, Wong YH, Ang MY, Yew WM, et al.
    Talanta, 2019 Jun 01;198:215-223.
    PMID: 30876552 DOI: 10.1016/j.talanta.2019.01.111
    The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R2), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R2 and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R2 and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the -CH=CH2 or CH=CH (990-900 cm-1) and C-Cl stretch (800-700 cm-1) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible technique for estimating the total MCPD content of refined vegetable oils.
    Matched MeSH terms: Least-Squares Analysis
  3. Fulltext Dataset on the Acceptance of e-learning System among Universities Students' under the COVID-19 Pandemic Conditions
    Al-Okaily M, Alqudah H, Matar A, Lutfi A, Taamneh A
    Data Brief, 2020 Aug 18.
    PMID: 32837976 DOI: 10.1016/j.dib.2020.106176
    The COVID-19 pandemic has produced an unprecedented change in the educational system worldwide. Besides the economic and social impacts, there is a dilemma of accepting the new educational system "e-learning" by students within educational institutions. In particular, universities students have to handle several kinds of environmental, electronic and mental struggles due to COVID-19. To catch the current circumstances of more than two hundred thousand Jordanian university student during COVID-19. 2,500 students have been randomly selected to respond on an online survey using universities' portals and websites between March and April 2020. At the end of the data gathering process, we have received 587 records. The dataset includes 1) Demographics of students; 2) students' perspectives concerning the factors influencing their intention to use e-learning system within the Jordanian universities context. Data were analyzed using Partial Least Squares - Structural Equation Modelling (PLS-SEM). Next, the result has confirmed the positive of direct effect variables (subjective norm, perceived ease of use, and perceived usefulness) on the students' intention to use e-learning system. Next, the result has also confirmed the mediating effect of perceived usefulness and perceived ease of use between subjective norm and the behavioral intention to use the e-learning system with partially supported.
    Matched MeSH terms: Least-Squares Analysis
  4. 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
  5. Fulltext Sorption of so2 and no by modified palm shell activated carbon: breakthrough curve model
    S. Bhatia, K. T. Lee, A. R. Mohamed, Sumathi, S.
    Pertanika Journal of Science & Technology, 2014;22(1):307-314.
    MyJurnal
    Simultaneous removal of SO2 and NO from simulated flue gas by cerium oxide supported over palm shell activated carbon (Ce/PSAC) was studied in a fixed bed adsorber. In this study, the adsorption breakthrough of SO2 and NO on Ce/PSAC at different reaction temperatures was manipulated to test their applicability to a model developed by Yoon and Nelson (1984) for breakthrough curves. Yoon and Nelson (1984) developed a relatively simple model addressing the adsorption and breakthrough of adsorbate vapour with respect to activated charcoal. This model was based on the assumption that the rate of decrease in the probability of adsorption for each adsorbate molecule is proportional to the probability of adsorbate adsorption and the probability of adsorbate breakthrough on the adsorbent. A regression analysis (least square method) has been used to give the model parameters of k and t1/2. The results showed that the agreement between the model and the experimental results is satisfactory. From the observation, it is concluded that the simple two-parameter model of Yoon and Nelson’s model can be applied for modelling the breakthrough curves of SO2 and NO gas adsorption over Ce/PSAC.
    Matched MeSH terms: Least-Squares Analysis
  6. Migration of antioxidants from polylactic acid films, a parameter estimation approach: Part I - A model including convective mass transfer coefficient
    Samsudin H, Auras R, Burgess G, Dolan K, Soto-Valdez H
    Food Res Int, 2018 03;105:920-929.
    PMID: 29433289 DOI: 10.1016/j.foodres.2017.11.065
    A two-step solution based on the boundary conditions of Crank's equations for mass transfer in a film was developed. Three driving factors, the diffusion (D), partition (Kp,f) and convective mass transfer coefficients (h), govern the sorption and/or desorption kinetics of migrants from polymer films. These three parameters were simultaneously estimated. They provide in-depth insight into the physics of a migration process. The first step was used to find the combination of D, Kp,f and h that minimized the sums of squared errors (SSE) between the predicted and actual results. In step 2, an ordinary least square (OLS) estimation was performed by using the proposed analytical solution containing D, Kp,f and h. Three selected migration studies of PLA/antioxidant-based films were used to demonstrate the use of this two-step solution. Additional parameter estimation approaches such as sequential and bootstrap were also performed to acquire a better knowledge about the kinetics of migration. The proposed model successfully provided the initial guesses for D, Kp,f and h. The h value was determined without performing a specific experiment for it. By determining h together with D, under or overestimation issues pertaining to a migration process can be avoided since these two parameters are correlated.
    Matched MeSH terms: Least-Squares Analysis
  7. The Influence of Organizational External Factors on Construction Risk Management among Nigerian Construction Companies
    Adeleke AQ, Bahaudin AY, Kamaruddeen AM, Bamgbade JA, Salimon MG, Khan MWA, et al.
    Saf Health Work, 2018 Mar;9(1):115-124.
    PMID: 30363069 DOI: 10.1016/j.shaw.2017.05.004
    Background: Substantial empirical research has shown conflicting results regarding the influence of organizational external factors on construction risk management, suggesting the necessity to introduce a moderator into the study. The present research confirmed whether rules and regulations matter on the relationships between organizational external factors and construction risk management.

    Methods: Based on discouragement and organizational control theory, this research examined the effects of organizational external factors and rules and regulations on construction risk management among 238 employees operating in construction companies in Abuja and Lagos, Nigeria. A personally administered questionnaire was used to acquire the data. The data were analyzed using partial least squares structural equation modeling.

    Results: A significant positive relationship between organizational external factors and construction risk management was asserted. This study also found a significant positive relationship between rules and regulations and construction risk management. As anticipated, rules and regulations were found to moderate the relationship between organizational external factors and construction risk management, with a significant positive result. Similarly, a significant interaction effect was also found between rules and regulations and organizational external factors. Implications of the research from a Nigerian point of view have also been discussed.

    Conclusion: Political, economy, and technology factors helped the construction companies to reduce the chance of risk occurrence during the construction activities. Rules and regulations also helped to lessen the rate of accidents involving construction workers as well as the duration of the projects. Similarly, the influence of the organizational external factors with rules and regulations on construction risk management has proven that most of the construction companies that implement the aforementioned factors have the chance to deliver their projects within the stipulated time, cost, and qualities, which can be used as a yardstick to measure a good project.

    Matched MeSH terms: Least-Squares Analysis
  8. Convergence in CO 2 emissions, carbon footprint and ecological footprint: evidence from OECD countries
    Solarin SA
    Environ Sci Pollut Res Int, 2019 Feb;26(6):6167-6181.
    PMID: 30617875 DOI: 10.1007/s11356-018-3993-8
    The aim of this paper is to augment the existing literature on convergence of CO2 emissions, by adding carbon footprint per capita and ecological footprint per capita to the convergence debate. We use the residual augmented least squares regression to examine the stochastic convergence of the environmental indices in 27 OECD countries. Furthermore, in contrast to the previous studies which mainly used the conventional beta-convergence approach to examine conditional convergence, we use a beta-convergence method that is capable of identifying the actual number of countries that contribute to conditional convergence. The sigma-convergence of the environmental indices is also examined. The results suggest that conditional convergence exists in 12 countries for CO2 emissions per capita, 15 countries for carbon footprint per capita and also 13 countries for ecological footprint per capita. There is evidence for sigma-convergence for all the three indicators. The policy implications of the results are discussed in the body of the paper.
    Matched MeSH terms: Least-Squares Analysis
  9. Quantitative analysis of virgin coconut oil in cream cosmetics preparations using fourier transform infrared (FTIR) spectroscopy
    Rohman A, Man YC, Sismindari
    Pak J Pharm Sci, 2009 Oct;22(4):415-20.
    PMID: 19783522
    Today, virgin coconut oil (VCO) is becoming valuable oil and is receiving an attractive topic for researchers because of its several biological activities. In cosmetics industry, VCO is excellent material which functions as a skin moisturizer and softener. Therefore, it is important to develop a quantitative analytical method offering a fast and reliable technique. Fourier transform infrared (FTIR) spectroscopy with sample handling technique of attenuated total reflectance (ATR) can be successfully used to analyze VCO quantitatively in cream cosmetic preparations. A multivariate analysis using calibration of partial least square (PLS) model revealed the good relationship between actual value and FTIR-predicted value of VCO with coefficient of determination (R2) of 0.998.
    Matched MeSH terms: Least-Squares Analysis
  10. EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms
    Ahirwal MK, Kumar A, Singh GK
    IEEE/ACM Trans Comput Biol Bioinform, 2013 Nov-Dec;10(6):1491-504.
    PMID: 24407307 DOI: 10.1109/TCBB.2013.119
    This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
    Matched MeSH terms: Least-Squares Analysis
  11. A new hybrid intelligent system for accurate detection of Parkinson's disease
    Hariharan M, Polat K, Sindhu R
    Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
    PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004
    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
    Matched MeSH terms: Least-Squares Analysis
  12. Relationship between fetal femur diaphysis length and neonatal crown-heel length: the effect of race
    Lim JM, Hong AG, Raman S, Shyamala N
    Ultrasound Obstet Gynecol, 2000 Feb;15(2):131-7.
    PMID: 10775996
    To determine whether racial differences affect the relationship between the fetal femur diaphysis length and the neonatal crown-heel length.
    Matched MeSH terms: Least-Squares Analysis
  13. Evolutionary and ecological forces influencing population diversification in Bornean montane passerines
    Chua VL, Smith BT, Burner RC, Rahman MA, Lakim M, Prawiradilaga DM, et al.
    Mol Phylogenet Evol, 2017 Aug;113:139-149.
    PMID: 28545973 DOI: 10.1016/j.ympev.2017.05.016
    The mountains of Borneo are well known for their high endemicity and historical role in preserving Southeast Asian rainforest biodiversity, but the diversification of populations inhabiting these mountains is poorly studied. Here we examine the genetic structure of 12 Bornean montane passerines by comparing complete mtDNA ND2 gene sequences of populations spanning the island. Maximum likelihood and Bayesian phylogenetic trees and haplotype networks are examined for common patterns that might signal important historical events or boundaries to dispersal. Morphological and ecological characteristics of each species are also examined using phylogenetic generalized least-squares (PGLS) for correlation with population structure. Populations in only four of the 12 species are subdivided into distinct clades or haplotype groups. Although this subdivision occurred at about the same time in each species (ca. 0.6-0.7Ma), the spatial positioning of the genetic break differs among the species. In two species, northeastern populations are genetically divergent from populations elsewhere on the island. In the other two species, populations in the main Bornean mountain chain, including the northeast, are distinct from those on two isolated peaks in northwestern Borneo. We suggest different historical forces played a role in shaping these two distributions, despite commonality in timing. PGLS analysis showed that only a single characteristic-hand-wing index-is correlated with population structure. Birds with longer wings, and hence potentially more dispersal power, have less population structure. To understand historical forces influencing montane population structure on Borneo, future studies must compare populations across the entirety of Sundaland.
    Matched MeSH terms: Least-Squares Analysis
  14. Fulltext Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications
    Sabry AH, W Hasan WZ, Ab Kadir MZA, Radzi MAM, Shafie S
    PLoS One, 2018;13(1):e0191478.
    PMID: 29351554 DOI: 10.1371/journal.pone.0191478
    The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.
    Matched MeSH terms: Least-Squares Analysis
  15. 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
  16. Induction of suppressor cells following mucosal presentation of Actinomyces viscosus in mice
    Sosroseno W, Bird PS, Gemmell E, Seymour GJ
    Oral Microbiol. Immunol., 2003 Oct;18(5):318-22.
    PMID: 12930525
    Mucosal presentation of Actinomyces viscosus results in antigen-specific systemic immune suppression, known as oral tolerance. The aim of the present study was to determine the mechanism by which this oral tolerance is induced. DBA/2 mice were gastrically immunized with A. viscosus. Serum, Peyer's patch (PP) and spleen cells were transferred to syngeneic recipients which were then systemically challenged with the sameiA. viscosus strain. To determine antigen-specificity of cells from gastrically immunized mice, recipients which received immune spleen cells were also challenged with Porphyromonas gingivalis. One week after the last systemic challenge, the delayed type hypersensitivity (DTH) response was determined by footpad swelling and the level of serum IgG, IgA and IgM antibodies to A. viscosus or P. gingivalis measured by an ELISA. No suppression of DTH response or of specific serum antibodies was found in recipients which received serum from gastrically immunized mice. Systemic immune suppression to A. viscosus was observed in recipients which had been transferred with PP cells obtained 2 days but not 4 and 6 days after gastric immunization with A. viscosus. Conversely, suppressed immune response could be seen in recipients transferred with spleen cells obtained 6 days after gastric immunization. The immune response to P. gingivalis remained unaltered in mice transferred with A. viscosus-gastrically immunized cells. The results of the present study suggest that oral tolerance induced by A. viscosus may be mediated by antigen-specific suppressor cells which originate in the PP and then migrate to the spleen.
    Matched MeSH terms: Least-Squares Analysis
  17. A transport environmental Kuznets curve analysis for Malaysia: exploring the role of corruption
    Go YH, Lau LS, Liew FM, Senadjki A
    Environ Sci Pollut Res Int, 2021 Jan;28(3):3421-3433.
    PMID: 32918263 DOI: 10.1007/s11356-020-10736-w
    Validity of the environmental Kuznets curve (EKC) hypothesis is consistently and widely debated among economists and environmentalists alike throughout time. In Malaysia, transport is one of the "dirtiest" sectors; it intensively consumes energy in powering engines by using fossil fuels and poses significant threats to environmental quality. Therefore, this study attempted an examination into the impact of corruption on transport carbon dioxide (CO2) emissions. By adopting the fully modified ordinary least squares, canonical cointegrating regression, and dynamic ordinary least squares in performing long-run estimations, the results obtained based on the annual data spanning from 1990 to 2017 yielded various notable findings. First, more corruption would be attributable towards increased transport CO2 emissions. Second, a monotonic increment of transport CO2 emission was seen with higher economic growth and thus invalidated the presence of EKC. Overall, this study suggests that Malaysia has yet to reach the level of economic growth synonymous with transport CO2 emission reduction due to the lack of high technology usage in the current system implemented. Therefore, this study could position policy recommendations of use to the Malaysian authorities in designing the appropriate economic and environmental policies, particularly for the transport sector.
    Matched MeSH terms: Least-Squares Analysis
  18. 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
  19. Fulltext A Hybridised Intelligent Technique for the diagnosis of medical diseases
    Abdu Masanawa Sagir, Saratha Sathasivam
    Pertanika Journal of Science & Technology, 2017;25(2):425-440.
    MyJurnal
    Medical diagnosis is the process of determining which disease or medical condition explains a person’s determinable signs and symptoms. Diagnosis of most diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). It incorporates hybrid learning algorithms least square estimates with Levenberg-Marquardt algorithm using analytic derivation for computation of Jacobian matrix, as well as code optimisation technique, which indexes membership functions. The goal is to investigate how certain diseases are affected by patient’s characteristics and measurement such as abnormalities or a decision about the presence or absence of a disease. In order to achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent technique was tested with Statlog heart disease and Hepatitis disease datasets obtained from the University of California at Irvine’s (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity was examined. In comparison, the proposed method was found to achieve superior
    performance when compared to some other related existing methods.
    Matched MeSH terms: Least-Squares Analysis
  20. Fulltext Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry
    Yusop Nurida M, Norfadilah D, Siti Aishah MR, Zhe Phak C, Saleh SM
    Int J Anal Chem, 2020;2020:9830685.
    PMID: 32089691 DOI: 10.1155/2020/9830685
    The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.
    Matched MeSH terms: Least-Squares Analysis
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