Conference Publications

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https://hdl.handle.net/20.500.14170/2004

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  • Publication
    Numerical simulation of non-newtonian blood flow through a tapered stenosed artery using the cross model
    (IOP Publishing Ltd., 2020)
    T Majekodunmi Joshua
    ;
    K Anwar
    ;
    Nooraihan Abdullah
    A Non-Newtonian model is used to examine the effect of tapering on the flow of blood along a stenosed artery which is caused by the pulsating nature the heart. The constitutive equation of the Cross model is used to capture the rheology of the streaming blood which accounts for the shear thinning behaviour of blood. The flow is considered to be laminar, incompressible, and axisymmetric. The finite- difference scheme was adopted to solve the non-linear equations describing the fluid motion in an unsteady two-dimensional case. The computation is presented in terms of the axial and radial velocities, volumetric flow rate, resistance to flow and the wall shear stress. The result from the numerical simulation clearly indicates that vessel tapering has considerable effect on the flow pattern of blood: as the tapering angle increases the flow rate and the axial velocity increases proportionately while the radial velocity, wall shear stress decreases and resistance to flow.
      17  13
  • Publication
    Numerical study of Magnetohydrodynamic blood flow through an artery with multiple stenosis
    (IOP Publishing Ltd., 2020)
    T Majekodunmi Joshua
    ;
    Khairul Anwar Mohamad Khazali
    ;
    Nooraihan Abdullah
    The study theoretically accounts for the impact of Magnetohydrodynamics on streaming blood through an artery having multiple stenosis regions using the non-Newtonian Cross-rheological model. It is regarded that the streaming blood is unsteady and pulsative. The use of appropriate conditions is predicated on the assumption that the flow is laminar and axisymmetric which makes the problem two-dimensional. The geometry of stenosis was immobilized into a rectangular grid using the radial coordinate transformation. The finite difference scheme was employed for the numerical simulations. Specifically, magnetic field (Hartmann number), Reynolds number and severity of stenosis were varied over the entire arterial length. The results obtained predicted that increase in the Hartmann number and stenosis severity reduces the magnitude of the flow velocity, flow rate but the reverse is the case when the Reynolds number is increased. However, the wall shear stress and the resistance to flow are aided by increasing the Hartman number and the stenosis severity but reduces with increase in the Reynolds number. Hence, it is germane to apply the appropriate magnetic field in treatments otherwise, such patient may be vulnerable.
      3  23
  • Publication
    Numerical study of Fiber Reinforced Polymer Reinforced Normal Strength Concrete (FRPNSC) under hydrocarbon fire
    (IOP Publishing, 2020)
    N I M Shukeri
    ;
    Nursalasawati Rusli
    Main safety requirements in concrete structural are the fire resistance requirements. One of the structural components is Fiber Reinforced Polymer Reinforced Normal Strength Concrete (FRPNSC). FRP reinforcement has been used as the replacement for conventional steel due to anti-corrosion and lightweight characteristics. Severe degradation on chemical bond properties for FRP will be effected when the temperature is rises. It is important to understand the minimum concrete cover thickness and concrete aggregates types to achieve fire resistance requirements. Standard fire equations are commonly used for fire simulations study. However, studies with hydrocarbon (HC) fire equations which fire ignited from petrochemical are limited. Therefore, in order to study the fire resistance of FRPNSC under hydrocarbon fire, temperature at the reinforcement needs to be predicted. In this study, explicit finite difference method (EFDM) used to solve the heat transfer model. The numerical algorithm of EFDM heat transfer model was constructed and used to analyse the concrete thickness and aggregates to achieve fire resistance requirement. The temperature result obtained by the EFDM model successfully validated with test data. FRPNSC under HC for carbonate aggregates give significant effect on the fire resistance compared to standard fire. The carbonate aggregates types also shows better fire performances compared to lightweight aggregates.
      9  1
  • Publication
    Assessing the effect of different covariates distributions on parameter estimates for Multinomial Logistic Regression (MLR)
    (IOP Publishing, 2020)
    Hamzah Abdul Hamid
    ;
    Siti Raudhah Ismail
    ;
    Sahimel Azwal Sulaiman
    ;
    Nor Azrita Mohd Amin
    n fitting a multinomial logistic regression model, one of the most important part is estimating the parameter. In Multinomial Logistic Regression (MLR), Maximum Likelihood Estimation (MLE) method is used to estimate the parameters. MLE is the suitable method to be applied to the problems associated with categorical response variables since it has several benefits such as sufficiency, consistency, efficiency and parameterization invariance. This study investigates the different type of continuous distributions (normal, negatively skewed, positively skewed) on parameter estimation via Monte Carlo simulation. From the simulation result, it shows that as the sample size increases, the effect of covariate distribution reduces. The estimated parameter also less affected for model with normal covariate distribution. At sample size 300 and above, the estimated parameter with normal covariate distribution is considered as close to the true parameter value. Interestingly, for the positively skewed, the estimated parameter also obtained unbiased parameter at sample size 300 and above. However, for negatively skewed, it requires a larger sample size to get closer to the true parameter value. The estimated parameters deviate too far from the true parameter at small sample size. As expected, as sample size increases the parameter estimates for all distributions are getting close to the true parameter value. Lastly, the distribution for MLR with more than one covariate give the same effect as the MLR model with only one covariate on parameter estimations.
      10  2
  • Publication
    Slip effect on unsteady hybrid nanofluid flow over a stretching/shrinking surface
    (AIP Publishing, 2023)
    Najwa Najib
    ;
    Nor Ashikin Abu Bakar
    ;
    Nor Fadhilah Dzulkifli
    The focus on this paper is to investigate the effect of slip in hybrid nanofluid past a stretching/shrinking surface by depending on time. The partial differential equations of governing equations are transformed to ordinary differential equations by employing appropriate similarity transformation. The equations are then solved numerically using bvp4c function in MATLAB software. The results of skin frictions coefficient and heat transfer rate are depicted in tables and graphs. It poses dual solutions for a certain domain of each solution.
  • Publication
    Development of green-Naghdi level I equation
    ( 2024)
    Siti Maryam Hafiza Mohd Kanafiah
    ;
    Nursalasawati Rusli
    ;
    Mohd Ridza Mohd Haniffah
  • Publication
      1  16
  • Publication
    Analyzing performance of activation functions in logic satisfiability hopfield neural network
    (AIP Publishing, 2024)
    Nurshazneem Roslan
    ;
    Saratha Sathasivam
    This research presents a performances analysis between different activation functions in solving non-systematic logical rule, specifically Random 2 Satisfiability (RAN2SAT) in Discrete Hopfield Neural Network. In this study, a new activation function called the Smish activation function will be integrated into the Discrete Hopfield Neural Network. The activation function plays a vital role in transforming the local field of the network into its final neuron state. In addition, the effectiveness of logic satisfiability in obtaining final neuron state depends on the type of activation function. The proposed new activation function will be compared with the conventional activation function, which is Hyperbolic Tangent activation function (HTAF) through computer simulations. The simulation of the different activation functions in doing logic satisfiability Discrete Hopfield Neural Network is done by DEV C++ version 15. Hence, the evaluation based on the different activation functions were made according to the error analysis, energy analysis, global minimum solution and total neuron variation. The output from the computer simulation shows that the logical rule of RAN2SAT with Smish activation function can retrieve the optimal solution. The finding of this research can give a benchmark for future research on non-systematic logical rule in doing logic satisfiability Discrete Hopfield Neural Network.
  • Publication
    Numerical analysis of 25-year-old male and female voices via mechanical model of vocal cord using cubic B-spline collocation method
    (AIP Publishing, 2024)
    Shazalina Mat Zin
    ;
    Syatirah Mat Zin
    Vocal cords play an important role in human speech production. The development of mechanical models of vocal cords has increased the understanding of their role and functionality. Numerous numerical studies have been explored to investigate the properties of vocal cord. In this work, one-mass mechanical model of vocal cord has been identified to be solved numerically using B-spline collocation method. Parameters of the model have been extracted from 25 years old male and female voices. Displacement of vocal cord at time, t, have been generated explore for each voice. The number of phases and highest displacement have also been discovered in the finding. Duration of first phase of each voice has also been explored.
  • Publication
    The comparison of extreme rainfall prediction for Northern region of Peninsular Malaysia based on GEV and GPD models
    (AIP Publishing, 2024)
    Mohd Khaidir Mohamed Salleh
    ;
    Nor Azrita Mohd Amin
    ;
    Noor Fadhilah Ahmad Radi
    ;
    Siti Aisyah Zakaria
    ;
    Muhamad Hafiz Masran
    ;
    Wan Nor Munirah Ariffin
    Extreme rainfall prediction is a critical aspect in hydrological and climate research fields to estimate the probability of extreme events, such as heavy rainfall or floods. These extreme events occur all over the world and have a tremendous impact on human health, injury and illness, and the imbalance of the ecosystem. This paper aims to compare the prediction of extreme rainfall between generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) for 10 years return period. The daily rainfall data of northern region in Peninsular Malaysia were obtained from Department of Irrigation and Drainage Malaysia (DID) for 29 stations for the period 1999 to 2019 is used. The findings will be beneficial for hydrologists to improve understanding of the difference between the analysis of the standard data modeling with extreme data modeling as well as to understand the difference between two main approaches in extreme data analysis. Both models show Klinik Bkt. Bendera station will encounter the highest 10 years return level compared to the other stations. The maximum corresponding 10-years return value for GPD is 147.26mm while for GEV is 142.39mm. These values are reaching the very heavy category of rainfall intensity index in Malaysia.
  • Publication
    Teaching statistics with excel: a hands-on approach for engineering students to promote thinking skills
    (AIP Publishing, 2024)
    Aishah Mohd Noor
    ;
    Siti Aisyah Zakaria
    ;
    Safwati Ibrahim
    Statistics education has become increasingly important in today's data-driven world, as the ability to analyze and interpret data is critical in many disciplines. However, introductory statistics courses traditionally emphasize rote calculations and procedural knowledge, which can result in passive learning and disengagement from students who may not see the relevance of statistics to their engineering field. To address these challenges, this paper proposes using Excel worksheets as student learning materials in an introductory statistics course to shift from traditional to experiential learning. Excel worksheets provide a hands-on approach to learning that gives students the experience of the actual process of doing statistics. The Excel worksheet facilitates quick and accurate calculations, allows more time for students to interpret statistical results, and encourages active learning. The Excel worksheet allows for real-world data analysis and what-if analyses, making abstract concepts more accessible. In addition, the Excel worksheets are designed to promote 21st-century thinking and collaboration skills, which are increasingly important in today's workforce. This paper presents several examples of Excel worksheet designs for teaching descriptive statistics, developed using the framework of substitution, augmentation, modification, and redefinition (SAMR) model. Excel worksheets promote deep learning and facilitate students' understanding of statistical ideas, concepts, and methods through learning by doing. The paper concludes that Excel worksheets offer a valuable tool for teaching introductory statistics to engineering students, enhancing their thinking skills, and preparing them for the data-driven demands of their field.
  • Publication
    Fuzzy Runge–Kutta Cash–Karp method under the interpretation of Hukuhara derivative
    (AIP Publishing, 2024)
    Nurain Zulaikha Husin
    ;
    Muhammad Zaini Ahmad
    ;
    Mohd Kamalrulzaman Md Akhir
    These days, researchers are interested in the study of fuzzy differential equations. Modeling can be applied to this fuzzy differential equation to find solutions related with real-world issues. Yet, it is challenging to solve this fuzzy differential equation analytically. Thus, the purpose of this study is to present the fuzzy Runge-Kutta Cash-Karp method to solve first-order fuzzy differential equation under the interpretation of Hukuhara derivative. By applying the fuzzy Runge-Kutta Cash-Karp method, the approximate solution is obtained and compared with the analytical solution. The results indicated that the approximate solutions of the proposed method are comparable to the analytical solutions in terms of accuracy.
  • Publication
    Outlier detection method in multiple circular regression model via robust circular distance
    (AIP Publishing, 2024-08-19)
    Safwati Ibrahim
    ;
    Mohd Irwan Yusoff
    ;
    Farah Adibah Adnan
    ;
    Aishah Mohd Noor
    ;
    Leow Wai Zhe
    The method of outlier detection with regards to circular regression have been widely developed nowadays. Several diagrammatical plots, numerical presentation as well as hypothesis testing have been recommended in detecting the outliers. As we know, the presence of outliers in dataset significantly impacts the parameter estimation and inference of the statistics. The outlier detection that exists in multiple circular regression model (MCRM) also attracting the interest of statisticians and researchers to do the research in depth. This paper presents the outlier detection method in MCRM using circular distance as well as circular error. The proposed method has been investigated through simulation study and the 5% upper percentiles is considered in obtaining the cut-off point as well as the performance power. Here, the procedure successfully identifies two outliers detected in the data set.
  • Publication
    Unsteady stagnation-point flow and heat transfer over an exponential stretching/shrinking sheet in hybrid nanofluid exhibiting slip effect
    (AIP Publishing, 2024-08-19)
    Nor Fadhilah Dzulkifli
    ;
    Najwa Najib
    ;
    Nor Ashikin Abu Bakar
    This study focuses on the investigation of unsteady stagnation-point flow and heat transfer over an exponential stretching/shrinking sheet immersed in a hybrid nanofluid. Hybrid nanofluid is an engineered fluid and can enhance thermal conductivity and heat transfer efficiency and stagnation-point flow is important in designing heat exchangers. Hence, the heat exchange process such as in power generation, and refrigeration becomes more effective. This mathematical model applied the Tiwari and Das model where Al2O3 - Cu hybrid nanofluid is considered. The base fluid is water, and the shape of the nanoparticle is considered in sphere shape. The ordinary differential equations are solved using the bvp4c function in the Matlab program to obtain the skin friction coefficient, heat transfer rate as well as velocity and temperature profiles. This study provides some tables of the skin-fiction coefficients and heat transfer rate values for the validation with the previous study and new values for the future study. This study reveals that dual solutions exist for suction s > sc. The increase of copper nanoparticles expands the solution and increases the skin friction coefficient at the surface. Meanwhile, by considering the higher effect of the slip parameter, the findings show an increment in both skin friction coefficient and heat transfer rate at the surface. The heat transfer rate is seen increasing by considering the same value of nanoparticle Volume fraction for copper and alumina compared to the different values.
  • Publication
    Exploring the efficacy of a supervised learning approach in 3 satisfiability reverse analysis method
    (AIP Publishing, 2024-08-27)
    Nur' Afifah Rusdi
    ;
    Nurul Atiqah Romli
    ;
    Gaeithry Manoharam
    ;
    Nurshazneem Roslan
    The conventional Discrete Hopfield Neural Network encounters a notable challenge in generating an output representation that is interpretable by the user. In response to this challenge, a symbolic rule has been introduced to govern the information embedded in the network. This approach has proven successful, leading us to develop a logic mining model that utilizes the logical rule of 3 Satisfiability in Discrete Hopfield Neural Network to represent attributes for repository datasets. Nevertheless, the existing 3 Satisfiability Reverse Analysis model faces two primary issues: random attribute selection and predetermined attribute arrangement. These issues can significantly impact the ability of the model to retrieve the optimal induced logic. In response, a solution that involves a supervised attribute selection benchmark using correlation analysis is proposed. Additionally, a permutation operator to allow for various attribute arrangements was implemented, thereby expanding the search space and increasing the likelihood of finding an optimal solution. Furthermore, a novel objective function for determining the best logic, which considers both true positives and true negatives is also introduced. This differs from the conventional 3 Satisfiability Reverse Analysis method, which relies solely on true positives. Three performance metrics, including accuracy, precision, and Matthews Correlation Coefficient (MCC), and tested on 13 real-life datasets to validate the efficiency of our proposed model. The results clearly demonstrated that our proposed model consistently outperforms the conventional 3 Satisfiability Reverse Analysis method, achieving the highest values for all performance metrics.
  • Publication
    Logic mining model in 3-satisfiability reverse analysis into discrete hopfield neural network
    (AIP Publishing, 2024-08-27)
    Gaeithry Manoharam
    ;
    Nurul Atiqah Romli
    ;
    Suad Abdeen
    ;
    Nur' Afifah Rusdi
    Logic mining is a powerful tool for organizations seeking to derive insights from large datasets. By analyzing data and identifying trends, logic mining can help solve problems, prevent losses, and uncover opportunities. In this study, we introduce an innovative approach that uses 3-Satisfiability logical rules and integrates them into the Hopfield neural network to better understand specific datasets. Our primary objective is to develop a robust statistical method called log-linear analysis, which can extract the most relevant attributes and insights. To accomplish this, we employ the 3-Satisfiability Reverse Analysis Method to extract attributes as logical rules from carefully selected 15 datasets. This method serves as a standalone logic mining paradigm, which we seamlessly integrate with the 3-Satisfiability logic within the Hopfield Neural network. Our proposed method assesses and trains datasets generated by standard algorithms. We then compare the performance of the 3-Satisfiability Reverse Analysis results with existing logic mining models, and our proposed method achieves superior accuracy, sensitivity, and Matthews Correlation Coefficient.
  • Publication
    Higher order Taylor series-based method for solving nonlinear equations
    (AIP Publishing, 2024-09)
    Herniza Md Tahir
    ;
    Mohd Agos Salim Nasir
    ;
    Sumarni Abu Bakar
    ;
    Hafizudin Mohamad Nor
    This paper introduced a new iterative method for solving nonlinear equations using the derivative estimation technique for the third-order Taylor’s approximation. A comparative analysis of the new iterative technique to existing methods that can also be derived from Taylor’s approximation, such as Newton’s method (NM), Halley’s method (HM), and Gemechu’s method (GM), is also provided. A few examples of nonlinear equations have been solved where the results presented that the new iterative method has fourth-order convergence and is more accurate as compared to the previous methods.
  • Publication
    Numerical analysis of one-mass mechanical model of vocal cord using normal and pathological voices through cubic B-spline collocation method
    (AIP Publishing, 2024-09-13)
    Shazalina Mat Zin
    ;
    Syatirah Mat Zin
    Vocal cords play a crucial role in human speech production. The development of mechanical models of vocal cords has increased the understanding of their role and functionality. Numerous numerical studies have been explored to investigate the properties of vocal cord. In this work, one-mass mechanical model of vocal cord has been identified to be solved numerically using B-spline collocation method. Parameters of the model have been extracted from real voices data classified as normal and pathological voices. New results, displacement of vocal cord at time, t, have been generated for each voice. The findings indicated that each voice produced a different value of displacement due to the damping and subglottal pressure of each voice. The number of phases and highest peaks displacement have also been discovered in the finding.