Journals Articles

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https://hdl.handle.net/20.500.14170/1995

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Browsing Journals Articles by Department "Universiti Malaysia Perlis"
  • Publication
    2-SAT discrete Hopfield neural networks optimization via Crow search and fuzzy dynamical clustering approach
    ( 2024)
    Caicai Feng
    ;
    Saratha Sathasivam
    ;
    Nurshazneem Roslan
    ;
    Muraly Velavan
    <abstract> <p>Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems.</p> </abstract>
  • Publication
    Analytical and numerical solutions of fuzzy differential equations
    ( 2013-02-26)
    Muhammad Zaini Ahmad
    ;
    M.K. Hasan
    ;
    B. De Baets
    In this paper, we study analytical and numerical solutions of fuzzy differential equations based on the extension principle. For linear fuzzy differential equations, we state some results on the behaviour of the solutions and study their relationship with the generalised Hukuhara derivative. In order to approximate the solutions of linear and non-linear fuzzy differential equations, we propose a new fuzzification of the classical Euler method and then incorporate an unconstrained optimisation technique. This combination offers a powerful tool to tackle uncertainty in any numerical method. An efficient computational algorithm is also provided to guarantee the convexity of fuzzy solutions on the time domain. Several illustrative examples are given.
  • Publication
    Application of hybrid cubic B-Spline collocation approach for solving a generalized nonlinear Klien-Gordon equation
    ( 2014)
    Shazalina Mat Zin
    ;
    Ahmad Abd Majid
    ;
    Ahmad Izani Md. Ismail
    ;
    Muhammad Abbas
    The generalized nonlinear Klien-Gordon equation is important in quantum mechanics and related fields. In this paper, a semi-implicit approach based on hybrid cubic B-spline is presented for the approximate solution of the nonlinear Klien-Gordon equation. The usual finite difference approach is used to discretize the time derivative while hybrid cubic B-spline is applied as an interpolating function in the space dimension. The results of applications to several test problems indicate good agreement with known solutions.
      2  5
  • Publication
    Applications of the fuzzy sumudu transform for the solution of first order fuzzy differential equations
    ( 2015)
    Norazrizal Rahman
    ;
    Muhammad Zaini Ahmad
    In this paper, we study the classical Sumudu transform in fuzzy environment, referred to as the fuzzy Sumudu transform (FST). We also propose some results on the properties of the FST, such as linearity, preserving, fuzzy derivative, shifting and convolution theorem. In order to show the capability of the FST, we provide a detailed procedure to solve fuzzy differential equations (FDEs). A numerical example is provided to illustrate the usage of the FST.
  • Publication
    Boundary layer flow and heat transfer over a permeable stretching/shrinking sheet with a convective boundary condition
    ( 2015)
    Wan Mohd Khairy Adly Wan Zaimi
    ;
    Anuar Ishak
    This paper concerns with the boundary layer flow and heat transfer over a permeable stretching/shrinking sheet in a viscous fluid, with the bottom surface of the plate is heated by convection from a hot fluid. The partial differential equations governing the flow and heat transfer are converted into ordinary differential equations using a similarity transformation, before being solved numerically. The effects of the suction, convection and stretching/shrinking parameters on the skin friction coefficient and the local Nusselt number are examined and graphically illustrated. Dual solutions are found to exist for a certain range of the suction and stretching/shrinking parameters. The numerical results also show that suction widens the range of the stretching/shrinking parameter for which the solution exists.
      3  5
  • Publication
    Conditional random k satisfiability modeling for k =1,2 (CRAN2SAT) with non-monotonic Smish activation function in discrete Hopfield neural network
    ( 2024)
    Nurshazneem Roslan
    ;
    Saratha Sathasivam
    ;
    Farah Liyana Azizan
    <abstract> <p>The current development of logic satisfiability in discrete Hopfield neural networks (DHNN)has been segregated into systematic logic and non-systematic logic. Most of the research tends to improve non-systematic logical rules to various extents, such as introducing the ratio of a negative literal and a flexible hybrid logical structure that combines systematic and non-systematic structures. However, the existing non-systematic logical rule exhibited a drawback concerning the impact of negative literal within the logical structure. Therefore, this paper presented a novel class of non-systematic logic called conditional random <italic>k</italic> satisfiability for <italic>k</italic> = 1, 2 while intentionally disregarding both positive literals in second-order clauses. The proposed logic was embedded into the discrete Hopfield neural network with the ultimate goal of minimizing the cost function. Moreover, a novel non-monotonic Smish activation function has been introduced with the aim of enhancing the quality of the final neuronal state. The performance of the proposed logic with new activation function was compared with other state of the art logical rules in conjunction with five different types of activation functions. Based on the findings, the proposed logic has obtained a lower learning error, with the highest total neuron variation <italic>TV</italic> = 857 and lowest average of Jaccard index, <italic>JSI</italic> = 0.5802. On top of that, the Smish activation function highlights its capability in the DHNN based on the result ratio of improvement <italic>Zm</italic> and <italic>TV</italic>. The ratio of improvement for Smish is consistently the highest throughout all the types of activation function, showing that Smish outperforms other types of activation functions in terms of <italic>Zm</italic> and <italic>TV.</italic> This new development of logical rule with the non-monotonic Smish activation function presents an alternative strategy to the logic mining technique. This finding will be of particular interest especially to the research areas of artificial neural network, logic satisfiability in DHNN and activation function.</p> </abstract>
  • Publication
    Flow past a permeable stretching/shrinking sheet in a nanofluid using two-phase model
    ( 2014-11)
    Wan Mohd Khairy Adly Wan Zaimi
    ;
    Anuar Ishak
    ;
    Ioan Pop
    ;
    James P. Brody
    The steady two-dimensional flow and heat transfer over a stretching/shrinking sheet in a nanofluid is investigated using Buongiorno’s nanofluid model. Different from the previously published papers, in the present study we consider the case when the nanofluid particle fraction on the boundary is passively rather than actively controlled, which make the model more physically realistic. The governing partial differential equations are transformed into nonlinear ordinary differential equations by a similarity transformation, before being solved numerically by a shooting method. The effects of some governing parameters on the fluid flow and heat transfer characteristics are graphically presented and discussed. Dual solutions are found to exist in a certain range of the suction and stretching/shrinking parameters. Results also indicate that both the skin friction coefficient and the local Nusselt number increase with increasing values of the suction parameter.
      2  6
  • Publication
    Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases
    ( 2024)
    Farah Liyana Azizan
    ;
    Saratha Sathasivam
    ;
    Nurshazneem Roslan
    ;
    Ahmad Deedat Ibrahim
    <abstract> <p>Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.</p> </abstract>
  • Publication
    Shape preserving data interpolation using rational cubic ball functions
    ( 2015)
    Ayser Nasir Hassan Tahat
    ;
    Abd Rahni Mt Piah
    ;
    Zainor Ridzuan Yahya
    A smooth curve interpolation scheme for positive, monotone, and convex data is developed.This scheme uses rational cubic Ball representation with four shape parameters in its description. Conditions of two shape parameters are derived in such a way that they preserve the shape of the data, whereas the other two parameters remain free to enable the user to modify the shape of the curve. The degree of smoothness is 𝐶1.The outputs from a number of numerical experiments are presented.
  • Publication
    The performances of mixed ewma-cusum control charts based on median-based estimators under non-normality
    ( 2023)
    Nor Fashihah Mohd Noor
    ;
    Ayu Abdul-Rahman
    ;
    Abdu Mohammed Ali Atta
    Exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts have been regularly used to monitor small process mean shifts. More recently, a mixture of EWMA and CUSUM charts known as mixed EWMA-CUSUM (MEC) control chart has been introduced for better small shift detection. However, like its predecessor, the MEC chart requires the normality assumption to ensure optimal performances. In the presence of outliers, which is the cause of non-normality, the parameters of the chart may be overestimated, leading to an unreliable monitoring process. To mitigate this problem, this paper employed median-based estimators namely, the median and modified one-step M-estimator (MOM), to control the location parameter via the MEC control chart. In this study, the performance of robust MEC charts for Phase II monitoring of location was compared with the standard MEC chart that is based on the sample mean. The performance of the robust MEC charts in terms of the average run length (ARL) on various g-and-h distributions clearly shows that a robust MEC chart based on the MOM estimator performs well regardless of the distributional shapes.