Displaying publications 1 - 20 of 75 in total

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  1. A clean approach for functionalized carbon nanotubes by deep eutectic solvents and their performance in the adsorption of methyl orange from aqueous solution
    Ibrahim RK, El-Shafie A, Hin LS, Mohd NSB, Aljumaily MM, Ibraim S, et al.
    J Environ Manage, 2019 Apr 01;235:521-534.
    PMID: 30716672 DOI: 10.1016/j.jenvman.2019.01.070
    In this study two deep eutectic solvents (DESs) were prepared using ethylene glycol (EG) and two different ammonium-based salts. The potential of these DESs as novel agents for CNTs functionalization was examined by performing a comprehensive characterization study to identify the changes developing after the functionalization process. The impact of DESs was obvious by increasing the surface area of CNTs to reach 197.8 (m2/g), and by adding new functional groups to CNTs surface without causing any damage to the unique structure of CNTs. Moreover, CNTs functionalized with DESs were applied as new adsorbents for the removal of methyl orange (MO) from water. The adsorption conditions were optimized using RSM-CCD experimental design. The kinetics and the equilibrium adsorption data were analyzed using different kinetic and isotherm models. According to the regression results, adsorption kinetics data were well described by pseudo-second order model, whereas adsorption isotherm data were best represented by Langmuir isotherm model. The highest recorded maximum adsorption capacity (qmax) value was found to be 310.2 mg/g.
  2. Fulltext A literature review of the cardiovascular risk-assessment tools: applicability among Asian population
    Liau SY, Mohamed Izham MI, Hassali MA, Shafie AA
    Heart Asia, 2010;2(1):15-8.
    PMID: 27325935 DOI: 10.1136/ha.2009.001115
    Cardiovascular diseases, the main causes of hospitalisations and death globally, have put an enormous economic burden on the healthcare system. Several risk factors are associated with the occurrence of cardiovascular events. At the heart of efficient prevention of cardiovascular disease is the concept of risk assessment. This paper aims to review the available cardiovascular risk-assessment tools and its applicability in predicting cardiovascular risk among Asian populations.
  3. Fulltext Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach
    Valizadeh N, El-Shafie A, Mirzaei M, Galavi H, Mukhlisin M, Jaafar O
    ScientificWorldJournal, 2014;2014:432976.
    PMID: 24790567 DOI: 10.1155/2014/432976
    Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.
  4. Fulltext Advanced machine learning model for better prediction accuracy of soil temperature at different depths
    Alizamir M, Kisi O, Ahmed AN, Mert C, Fai CM, Kim S, et al.
    PLoS One, 2020;15(4):e0231055.
    PMID: 32287272 DOI: 10.1371/journal.pone.0231055
    Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.
  5. Fulltext An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration
    Ehteram M, Singh VP, Ferdowsi A, Mousavi SF, Farzin S, Karami H, et al.
    PLoS One, 2019;14(5):e0217499.
    PMID: 31150443 DOI: 10.1371/journal.pone.0217499
    Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.
  6. An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models
    Jalil-Masir H, Fattahi R, Ghanbari-Adivi E, Asadi Aghbolaghi M, Ehteram M, Ahmed AN, et al.
    Environ Sci Pollut Res Int, 2022 Sep;29(44):67180-67213.
    PMID: 35522411 DOI: 10.1007/s11356-022-20472-y
    Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR-vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D50/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D50/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.
  7. Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia
    Ansari M, Othman F, Abunama T, El-Shafie A
    Environ Sci Pollut Res Int, 2018 Apr;25(12):12139-12149.
    PMID: 29455350 DOI: 10.1007/s11356-018-1438-z
    The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R2) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.
  8. Applications of the Box-Wilson design model for bio-hydrogen production using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564)
    Alalayah WM, Kalil MS, Kadhum AA, Jahim J, Zaharim A, Alauj NM, et al.
    Pak J Biol Sci, 2010 Jul 15;13(14):674-82.
    PMID: 21848059
    Box-Wilson design (BWD) model was applied to determine the optimum values of influencing parameters in anaerobic fermentation to produce hydrogen using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564). The main focus of the study was to find the optimal relationship between the hydrogen yield and three variables including initial substrate concentration, initial medium pH and reaction temperature. Microbial growth kinetic parameters for hydrogen production under anaerobic conditions were determined using the Monod model with incorporation of a substrate inhibition term. The values of micro(max) (maximum specific growth rate) and K, (saturation constant) were 0.398 h(-1) and 5.509 g L(-1), respectively, using glucose as the substrate. The experimental substrate and biomass-concentration profiles were in good agreement with those obtained by the kinetic-model predictions. By varying the conditions of the initial substrate concentration (1-40 g L(-1)), reaction temperature (25-40 degrees C) and initial medium pH (4-8), the model predicted a maximum hydrogen yield of 3.24 mol H2 (mol glucose)(-1). The experimental data collected utilising this design was successfully fitted to a second-order polynomial model. An optimum operating condition of 10 g L(-1) initial substrate concentration, 37 degrees C reaction temperature and 6.0 +/- 0.2 initial medium pH gave 80% of the predicted maximum yield of hydrogen where as the experimental yield obtained in this study was 77.75% exhibiting a close accuracy between estimated and experimental values. This is the first report to predict bio-hydrogen yield by applying Box-Wilson Design in anaerobic fermentation while optimizing the effects of environmental factors prevailing there by investigating the effects of environmental factors.
  9. Fulltext Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent
    Fiyadh SS, AlOmar MK, Binti Jaafar WZ, AlSaadi MA, Fayaed SS, Binti Koting S, et al.
    Int J Mol Sci, 2019 Aug 28;20(17).
    PMID: 31466219 DOI: 10.3390/ijms20174206
    Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.
  10. Fulltext Assessing the cost effectiveness of an anticoagulation clinic in comparison with the usual medical clinic In Kuala Lumpur Hospital [Abstract]
    Thanimalai S, Shafie AA, Ahmad HM, Sinnadurai J
    Value Health, 2014 Nov;17(7):A487.
    PMID: 27201439 DOI: 10.1016/j.jval.2014.08.1428
    Objectives: Systematic anticoagulation management clinic is now recommended to manage warfarinized atrial fibrillation (AF) patient. In Malaysia, the service is recently introduced as pharmacist managed Warfarin Medication Therapy Adherence Clinic (WMTAC). The objective of the present study was to assess the cost effectiveness of anticoagulation clinic in comparison with usual medical in Kuala Lumpur Hospital.
    Methods: A Markov model built using the provider perspective and 20 year time horizon was used to assess the cost effectiveness. The base case analysis assumed a cohort of patients with AF 57 years of age with comorbid illnesses. Data sources include a 6 month retrospective cohort analysis of the effectiveness of the clinics, the cost of drugs, cost of personnel and space of the clinics, cost of monitoring and cost of adverse events were obtained from the local source and publications. The transition probabilities of these clinics outcomes were obtained from a literature search. Future costs were discounted by 3% to convert to present values. All costs were in Ringgit Malaysia (RM) based on year 2012.
    Results: The results of a 20-year period model showed that UMC was dominated by the WMTAC in the same time period. The mean cost of the WMTAC was RM 5864 whereas the UMC cost was RM 6550. The sensitivity analysis showed that clinic treatment costs and effectiveness influenced the cost-effectiveness. If the cost of WMTAC was increased by 50% of the current cost, the WMTAC would not be a dominant intervention. WMTAC was also cost effective for a willingness to pay of RM32000.
    Conclusions: The anticoagulation management service appears to cost less and provide greater effectiveness than usual care. In conclusion, the Markov model suggests that from the provider perspective the anticoagulation clinic is a more cost effective option than the usual medical clinic in Kuala Lumpur Hospital.
    Study site: Medication Therapy Adherence Clinic, Hospital Kuala Lumpur, Malaysia
  11. Fulltext Assessing the role of urban green spaces for human well-being: a systematic review
    Jabbar M, Yusoff MM, Shafie A
    GeoJournal, 2021 Jul 20.
    PMID: 34305268 DOI: 10.1007/s10708-021-10474-7
    Human has been evolving in a natural environment over a long time; thus, he is habitual to adapt it. Green spaces are obligatory landscapes in an urban structure that provide a natural environment and accelerate other life events. In contrast, unplanned urbanization, and conversion from green to grey structures have damaged natural environmental resources. Studies through different angles have highlighted the importance of urban green spaces for human well-being but now need to identify their role according to the potential. The demands of urban green spaces may differ with the change of population size, types of grey structure, urban expansion, the altitude of the place, and geographical location. Therefore, this systematic review aims to analyse the significance of urban green spaces for human well-being. The study opted for a systematic process during the selection and organization of studies for this review. After analysing, 46 studies were finalized with the consensus of three review authors. Accordingly, literature was analysed under the central theme of "Urban Green Spaces for Human Well-being." Human Well-being was assessed under six sub-themes; physical, psychological, mental, social, subjective, and environmental well-being. The review concluded that urban green spaces are the primary pillar for a sustainable urban place and human well-being due to highly positive and positive correlations. Moreover, the study did not find any demarcation line between green spaces and grey structures according to any specific need. Therefore, the study suggested that the role of urban green spaces for human well-being should be analysed according to their potential and required optimal ratio under different communities' urban specific environments and social behaviour.
  12. Assessment of knowledge, attitude and practice of food allergies among food handlers in the state of Penang, Malaysia
    Shafie AA, Azman AW
    Public Health, 2015 Sep;129(9):1278-84.
    PMID: 25931434 DOI: 10.1016/j.puhe.2015.03.016
    Food handler's knowledge, attitude and practice regarding food allergies are important to prevent debilitating and sometimes fatal reactions. This study aimed to assess their food allergy knowledge, attitude and practice, which could help to maintain the safety and hygiene of food consumed by the public.
  13. Association among calendar packaging and medication adherence: Findings from a focus group discussion among hypertensive patients in Penang, Malaysia. [Abstract]
    Tan BY, Shafie AA, Hassali MA, Saleem F, Kumar R
    Value Health, 2015 Nov;18(7):A831.
    PMID: 26534439 DOI: 10.1016/j.jval.2015.09.317
    Objectives: Medication adherence to treatment recommendations has major impact on health outcomes. Numerous interventions to improve medication adherence among the patients have been studied in clinical trials, including calendar packaging and patient reminder letters. Therefore, this study is aimed to explore hypertensive patient’s perceptions towards calendar packaging and its impact on medication adherence.
    Methods: A qualitative method was adopted, whereby two focus group sessions were conducted among 16 conveniently sampled hypertensive patients from a community based non-governmental organisation in the state of Penang, Malaysia. A pre validated focus group guide was constructed and used for data collection. Collected data was transcribed verbatim and analysed by thematic content analysis to identify the emerging themes.
    Results: Each focus group consisted of 8 hypertensive patients. Thematic content analysis resulted into 3 major themes (knowledge and familiarity with the medicines names and their packaging; perception about the packaging and labelling of medicines; knowledge and views of calendar packaging) and each theme was further divided into 2 sub themes. Majority of the hypertensive patients were not familiar with their medication names, however they were able to identify their medications based on the appearance and packaging. Participants agreed that calendar packaging is a great intervention to increase awareness among patients about regular medicine use and increase medication adherence.
    Conclusions: The study concluded that hypertensive patients relied on the packaging and labelling on the medications to identify their medications. Thus, packaging and labelling of the medications play an important role in improving medication adherence and reduce medication errors. This finding can help to enhance the drug manufacturers to pay attention on the drug packaging in order to increase medication adherence among the patients.
  14. Fulltext Bilateral congenital renal arteriovenous malformation: A rare entity with uncommon presentation.
    Radhiana, H., Mohd Shafie, A., Mohd Ariff, M.A.
    International Medical Journal Malaysia, 2009;8(1):53-55.
    MyJurnal
    Renal arteriovenous malformation (AVM) is a rare congenital anomaly of the urinary system. We present a patient with bilateral renal AVMs who presented with back pain and microscopic hematuria. This case highlights the importance of careful diagnostic work-up in the evaluation of upper tract hematuria. Renal AVM was found to be the cause of mild back pain and persistent microscopic hematuria in a 45-year-old lady. This case highlights the importance of complete diagnostic work-up in the evaluation of microscopic hematuria in arriving at the correct diagnosis of an uncommon clinical entity.
  15. Fulltext Caesarean delivery and its correlates in Northern Region of Bangladesh: application of logistic regression and cox proportional hazard model
    Rahman M, Shariff AA, Shafie A, Saaid R, Tahir RM
    J Health Popul Nutr, 2015;33:8.
    PMID: 26825988 DOI: 10.1186/s41043-015-0020-2
    Caesarean delivery (C-section) rates have been increasing dramatically in the past decades around the world. This increase has been attributed to multiple factors such as maternal, socio-demographic and institutional factors and is a burning issue of global aspect like in many developed and developing countries. Therefore, this study examines the relationship between mode of delivery and time to event with provider characteristics (i.e., covariates) respectively.
  16. Carbon nanotubes fibres/aluminium-NiZnFe2O4 based electromagnetic transmitter for improved magnitude versus offset (MVO) in a scaled marine environment
    Yahya N, Akhtar MN, Nasir N, Shafie A, Jabeli MS, Koziol K
    J Nanosci Nanotechnol, 2012 Oct;12(10):8100-9.
    PMID: 23421185
    In seabed logging the magnitude of electromagnetic (EM) waves for the detection of a hydrocarbon reservoir in the marine environment is very important. Having a strong EM source for exploration target 4000 m below the sea floor is a very challenging task. A new carbon nanotubes (CNT) fibres/aluminium based EM transmitter is developed and NiZn ferrite as magnetic feeders was used in a scaled tank to evaluate the presence of oil. Resistive scaled tank experiments with a scale factor of 2000 were carried out. X-ray Diffraction (XRD), Raman Spectroscopy and Field Emission Scanning Electron Microscope (FESEM) were done to characterize the synthesized magnetic feeders. Single phase Ni0.76Mg0.04Zn0.2Fe2O4, obtained by the sol-gel method and sintered at 700 degrees C in air, has a [311] major peak. FESEM results show nanoparticles with average diameters of 17-45 nm. Samples which have a high Q-factor (approximately 50) was used as magnetic feeders for the EM transmitter. The magnitude of the EM waves of this new EM transmitter increases up to 400%. A curve fitting method using MATLAB software was done to evaluate the performance of the new EM transmitter. The correlation value with CNT fibres/aluminium-NiZnFe2O4 base transmitter shows a 152.5% increase of the magnetic field strength in the presence of oil. Modelling of the scale tank which replicates the marine environment was done using the Finite Element Method (FEM). In conclusion, FEM was able to delineate the presence of oil with greater magnitude of E-field (16.89%) and the B field (4.20%) due to the new EM transmitter.
  17. Correction to: Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm
    Banadkooki FB, Ehteram M, Ahmed AN, Teo FY, Ebrahimi M, Fai CM, et al.
    Environ Sci Pollut Res Int, 2020 Oct;27(30):38117-38119.
    PMID: 32705552 DOI: 10.1007/s11356-020-10139-x
    Following the publication of the article it has come to the authors' attention that the first panel of Fig. 11 has been repeated with the second panel of Fig. 11.
  18. Cost-Effectiveness of Clinical Pharmacy Education on Infection Management among Patients with Chronic Kidney Disease in an Indonesian Hospital
    Nasution A, Syed Sulaiman SA, Shafie AA
    Value Health Reg Issues, 2013 May;2(1):43-47.
    PMID: 29702851 DOI: 10.1016/j.vhri.2013.02.009
    OBJECTIVES: This study evaluated the clinical and economic impacts of clinical pharmacy education (CPE) on infection management among patients with chronic kidney disease (CKD) stages 4 and 5 in Haji Adam Malik Hospital, Indonesia.

    METHODS: A quasi-experimental economic evaluation comparing CPE impact on 6-month CKD mortality was conducted on the basis of payer perspective. The experimental group (n = 63) received care by health care providers who were given CPE on drug-related problems and dose adjustment. The control group (n = 80) was based on the historical cohort of patients who received care before the CPE. Measure of clinical outcome applied in this study was number of lives saved/100 patients treated. Cost-effectiveness ratios for CKD stages 4 and 5 patients without CPE and with CPE and incremental cost-effectiveness ratios (ICERs) for CKD stages 4 and 5 patients were analyzed.

    RESULTS: Lives saved (%) in the treatment of CKD without CPE: CKD stage 4, 78.57; CKD stage 5, 57.58. Lives saved (%) in the treatment of CKD with CPE: CKD stage 4, 88.89; CKD stage 5, 65.45. Cost-effectiveness ratios for stage 4 with and without CPEs were Rp3,348,733.27 and Rp3,519,931.009, respectively. Cost-effectiveness ratios for stage 5 with and without CPEs were Rp7,137,874.93 and Rp7,871,822.27, respectively. ICERs were Rp2,045,341.22 for CKD stage 4 and Rp1,767,585.60 for CKD stage 5.

    CONCLUSIONS: Treatment of CKD stages 4 and 5 with CPE was more effective and cost-effective compared with treatment of CKD stages 4 and 5 without CPE. The ICERs indicated that extra costs were required to increase life saved in both stages.

  19. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
    Ehteram M, Ahmed AN, Latif SD, Huang YF, Alizamir M, Kisi O, et al.
    Environ Sci Pollut Res Int, 2021 Jan;28(2):1596-1611.
    PMID: 32851519 DOI: 10.1007/s11356-020-10421-y
    There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.
  20. Determinants of Caesarean Risk Factor in Northern Region of Bangladesh: A Multivariate Analysis
    Rahman M, Ahmad Shariff A, Shafie A, Saaid R, Md Tahir R
    Iran J Public Health, 2014 Jan;43(1):16-27.
    PMID: 26060675
    Caesarean section (c-section) rates have been increasing dramatically in the past decades around the world. This increase has been attributed to multiple factors such as maternal, socio-demographic and institutional fac-tors. Therefore, this study examines the impact of maternal, socio-demographic and relevant characteristics on caesar-ean delivery in the northern region of Bangladesh.
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