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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

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. </abstract>

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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.

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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.

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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.

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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

Decay properties of 253, 255Rf using the relativistic Mean-Field framework within the preformed Cluster-Decay model

(Pleiades Publishing Ltd., 2023)

Joshua T. Majekodunmi

;

Nishu Jain

;

Khairul Anwar Mohamad Khazali

;

Nooraihan Abdullah

;

Raj Kumar

;

Muruthujaya Bhuyan

Most neutron-deficient α emitters are known to be of great relevance to the astrophysical rapid neutron capture process in superheavy nuclei. Therefore, in the present work, the decay properties of newly observed superheavy nuclei with Z = 102, i.e., 249No isotope from the α-decay of 253Rf is theoretically investigated using the relativistic mean field (RMF) framework and the NL3* parameter set within the preformed cluster-decay model (PCM). The α-decay chain of 255f is also considered. The RMF densities are folded with the R3Y nucleon-nucleon (NN) potential to deduce the nuclear interaction potential between the decaying fragments. A complete understanding of the penetration of an α-particle across the nuclear Coulomb barrier gives outstanding credence to the assumptions of quantum mechanics. The presence of shell/sub-shell closure is indicated by the formation of peaks along the decay chain and was found to alter the conventional scaling factor observed earlier in the results of the PCM. The calculated half-lives are in close agreement with recent experimental measurements.

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Publication

Dual solutions of boundary layer flow and heat transfer in hybrid nanofluid over a stretching/shrinking cylinder

(Semarak Ilmu Publishing, 2023)

Nor Ashikin Abu Bakar

;

Rohana Abdul Hamid

;

Najwa Najib

The boundary layer flow over a stretching/shrinking cylinder in hybrid nanofluid with the effects of suction, partial slip and convective boundary condition is studied. Hybrid nanoparticles Al2O3 and TiO2 with water as based fluid are considered in the study. The partial differential equations are transformed to ordinary differential equations by employing the similarity variables. The numerical results are obtained using the bvp4c solver in MATLAB software. The influence of nanoparticles volume fraction (Al2O3-TiO2 in water-based fluid), curvature parameter, suction parameter, partial slip parameter and Biot number on the velocity profile, temperature profile, skin friction coefficient and heat transfer rate are discussed. The numerical results indicate that for shrinking surface case, the dual solutions exist for a certain range of curvature parameter and suction parameter.

Publication

Effect of extraction conditions on the phytochemical properties of Clinacanthus nutans using Pressurized Hot Water Extraction (PHWE) technique

(IOP Publishing Ltd, 2020)

M.K. Abduljabbar

;

Khairul Farihan Kasim

;

Akmal Hadi Ma’Radzi

;

N.S. Sofian-Seng

Clinacanthus nutan is an important herb species from the family Acanthaceae and is commonly found in Southeast Asia. It is also known as snake grass, or 'belalai gajah' in malay. The leaves of C. nutan can be used to make a refreshing juice or tea, or even consumed raw as 'ulam', a traditional malay salad. In this work, the leaves, stems, and a mixture of both leaves and stems of C. nutan are first dried in an oven at 50 C for 24 hours and then grounded into a powder. Then, pressurized hot water extraction (PHWE) is used to extract phytochemicals from the powdered samples at 120 C for 20 minutes. This work investigates the effects of three important parameters for extraction; the sample particle size (<63 to 500μm), solventto-sample ratio (10:2 to 50:2 v/w), and sample weight (0.5 to 3.0g) on the extracted phytochemicals' total phenolic compounds (TPC), total flavonoid compounds (TFC), and 2,2-dipheny1-1picrylhydrazyl (DPPH) scavenging activity. The experiments are carried out in triplicate and the results are analyzed using Minitab. Phytochemicals extracted from 2g leaf powder samples of particle size <63μm using a solvent-tosample ratio of 50:2 (v/w) resulted in the most favorable results for TPC, TFC, and DPPH scavenging activity.

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Publication

Engineering students' performances in mathematics through project-based learning

(Horizon Research Publishing, 2020)

N. Razali

;

N. A. Zainuri

;

H. Othman

;

Z. M. Nopiah

;

Nor Ashikin Abu Bakar

Project-based learning is an example of active learning and is student-driven, interdisciplinary, collaborative and technology-based. To test the hypothesis that project-based learning maximises course performance, we analysed a difficulty index of examination scores or failure rates and compared between 422 students in the 2016/2017 session, who took the Vector Calculus course and project-based learning with 342 students from the 2015/2016 session without project-based learning. The analysis of the difficulty index is used to investigate the achievement of the course outcome and the analysis on the correlation between the project-based learning scores and the final exam scores are identified using Pearson's product-moment correlation. The effect sizes indicate that on average examination scores improved by about 12% with project-based learning and students in classes with project-based learning were 3.4 times more likely to get as than students in classes without project-based learning. It is observed that the difficulty index for all course outcomes are achieved and distributed between a good range of 0.3–0.8. It is also proven that the students find it easier to answer the exam questions after the project-based learning is implemented based on the results of their mid and final semester exams.

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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.

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Publication

Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset

(Semarak Ilmu Publishing, 2023)

Chow Yong Huan

;

Wan Zuki Azman Wan Muhamad

;

Zainor Ridzuan Yahya

;

Nor Hizamiyani Abdul Azziz

;

Tan Chye Lih

;

Tan Xiao Jian

The Mahalanobis-Taguchi System (MTS) is a statistical approach used in breast cancer research to facilitate early detection and promote efficient treatment. The technique analyses mammogram images for significant features using a multivariate statistical analysis technique. It combines the Mahalanobis distance (MD) and Taguchi's method to determine the differences between benign and malignant samples. While orthogonal array (OA) has been widely used in MTS, it has been criticised for providing suboptimal results due to insufficient coverage of feature combinations during the feature optimisation process. To address this issue, the Binary Whale Optimisation Algorithm (BWOA) is proposed as an improved search algorithm for MTS. This paper aims to develop a novel hybrid method that enhances the efficiency of the Mahalanobis Taguchi System (MTS). The performance of feature selection ability due to different MTS hybrid algorithms were also compared. BWOA simulates the hunting behaviour of humpback whales and works by exploring new regions of the solution space, gradually narrowing the search space, and fine-tuning the solution. MTS-BWOA demonstrated its enhanced capability in feature optimisation compared to traditional MTS methods and has the potential to be applied in other medical imaging domains.

Publication

Hybrid singular value decomposition based alpha trimmed mean-median filter in eliminating high density salt and pepper noise from grayscale image

( 2024)

Mohd Saifunnaim Mat Zain

;

Ahmad Kadri Junoh

;

Achmad Abdurrazzaq

Publication

Influence of perturbations on linear and nonlinear optical properties of quantum dot

(Springer, 2023)

Collins Okon Edet

;

Emre Bahadir Al

;

Fatih Ungan

;

Etido Patrick Inyang

;

Norshamsuri Ali @ Hasim

;

Muhammad Mahyiddin Ramli

;

Rosdisham Endut

;

Syed Alwee Aljunid Syed Junid

This study focused on investigating the influence of perturbations on the linear and nonlinear optical properties of GaAs/ Ga1-xAlxAs screened modified Kratzer potential (SMKP) quantum dot (QD). The optical absorption coefficients (OACs) and refractive index changes (RICs) for GaAs/ Ga1-xAlxAs have been presented. The density matrix and iterative approaches were used to derive expressions of OACs and RICs in SMKP QD. The diagonalization method has been used to obtain energy eigenvalues and eigenfunctions of GaAs/ Ga1-xAlxAs SMKP QD under the effects of Al concentration-x, hydrostatic pressure, and temperature. Our results reveal that the Al concentration-x, hydrostatic pressure, and temperature greatly impact the position and amplitude of the resonant peaks of the linear and nonlinear OACs and RICs. Interpretations have been presented in detail. The results of this study will find applications in the optical physics of semiconductors and other systems.

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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>

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Publication

Mathematical model of dengue virus with predator-prey interactions

(Penerbit UKM, 2020)

Sarinah Banu Mohamed Siddik

;

Farah Aini Abdullah

;

Ahmad Izani Md. Ismail

In this paper, a mathematical model of dengue incorporating two sub-models that: describes the linked dynamics between predator-prey of mosquitoes at the larval stage, and describes the dengue spread between humans and adult mosquitoes, is formulated to simulate the dynamics of dengue spread. The effect of predator-prey dynamics in controlling the dengue disease at the larval stage of mosquito populations is investigated. Stability analysis of the equilibrium points are carried out. Numerical simulations results indicate that the use of predator-prey dynamics of mosquitoes at the larval stage as biological control agents for controlling the larval stage of dengue mosquito assists in combating dengue virus contagion

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Publication

Multi-unit discrete hopfield neural network for higher order supervised learning through logic mining: optimal performance design and attribute selection

(Springer/King Saud University, 2023)

Nur' Afifah Rusdi

;

Mohd Shareduwan Mohd Kasihmuddin

;

Nurul Atiqah Romli

;

Gaeithry Manoharam

;

Mohd. Asyraf Mansor

In the perspective of logic mining, the attribute selection, and the objective function of the best logic is the two main factors that identifies the effectiveness of our proposed logic mining model. The non-significant attributes selected will cause the Discrete Hopfield Neural Network to learned and obtain wrong synaptic weight. Thus, this will result to suboptimal solution. Although we might select the correct attributes, the conventional objective function of the best logic limits the search space to obtained more induced logic during the retrieval phase of Discrete Hopfield Neural Network. Therefore, this paper proposes a novel logic mining by integrating statistical analysis in the pre-processing phase to ensure that only optimal attributes will be selected. Supervised learning approach via correlation analysis is implemented for the purpose of attribute selection. Additionally, permutation operator serves to enhance the probability of the higher order satisfiability logical rule to be satisfied by having finite arrangement of attributes. During the learning phase, we proposed multi-unit Discrete Hopfield Neural Network to enhance the search space which leads to optimal solution. The efficiency of the proposed model is tested on 15 real-life datasets by comparing the performance of the model with existing works in logic mining using five performance metrics including accuracy, sensitivity, precision, Matthews Correlation Coefficient (MCC) and F1 Score. According to the results, the proposed model has its own strength by dominating most of the average rank of the performance metrics. This demonstrates that the proposed model can differentiate across all domains in the confusion matrix. Additionally, the p-value obtained based on the five-performance metrics indicate that there is a significantly difference between the proposed model and all existing works since the value obtained for accuracy (0.000), sensitivity (0.001), precision (0.000), F1 score (0.000) and MCC (0.000) are less than 0.05. This finding statistically prove that the proposed model is more effective compared with existing works in logic mining.

Publication

Pattern selection for Darcy-Bénard convection with local thermal nonequilibrium

(Elsevier, 2020)

Biliana Bidin

;

D. Andrew S. Rees

A weakly nonlinear analysis is performed on the classical Darcy-Bénard problem to determine the effects of local thermal nonequilibrium on the planform of convection immediately post-onset. It is found that two-dimensional rolls are always favoured. Although disturbances which are perpendicular to the roll whose stability is being assessed usually form the most dangerous mode of instability, it is also found that there are regions of parameter space where the cross-roll instability becomes inoperative as an instability mechanism.

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Publication

Preformation probability and kinematics of cluster emission yielding Pb-daughters

(IOP Publishing and Chinese Physical Society, 2023)

Joshua T. Majekodunmi

;

Muruthujaya Bhuyan

;

Khairul Anwar Mohamad Khazali

;

Nooraihan Abdullah

;

Raj Kumar

In the present study, the newly established preformation formula is applied for the first time to study the kinematics of the cluster emission from various radioactive nuclei, especially those that decay to the double shell closure Pb nucleus and its neighbors as daughters. The recently proposed universal cluster preformation formula has been established based on the concepts that underscore the influence of mass and charge asymmetry ( and ), cluster mass , and the Q-value, paving the way to quantify the energy contribution during preformation as well as during the tunneling process separately. The cluster-daughter interaction potential is obtained by folding the relativistic mean-field (RMF) densities with the recently developed microscopic R3Y using the NL and the phenomenological M3Y NN potentials to compare their adaptability. The penetration probabilities are calculated from the WKB approximation. With the inclusion of the new preformation probability , the predicted half-lives of the R3Y and M3Y interactions are in good agreement with the experimental data. Furthermore, a careful inspection reflects slight differences in the decay half-lives, which arise from their respective barrier properties. The for systems with double magic shell closure Pb daughter are found to be an order of higher than those with neighboring Pb daughter nuclei. By exploring the contributions of the decay energy, the recoil effect of the daughter nucleus is evaluated, in contrast to several other conjectures. Thus, the centrality of the -value in the decay process is demonstrated and redefined within the preformed cluster-decay model. Additionally, we have introduced a simple and intuitive set of criteria that governs the estimation of recoil energy in the cluster radioactivity.

Publication

Quantum information entropy of a particle trapped by the Aharonov–Bohm-type effect

(IOP Publishing Ltd., 2023)

Francisco Cleiton E. Lima

;

Allan R.P. Moreira

;

C A S Almeida

;

Collins Okon Edet

;

Norshamsuri Ali @ Hasim

In this research article, we use the Shannon’s formalism to investigate the quantum information entropy of a particle trapped by the Aharonov-Bohm-type field. For quantum information study, it is necessary to investigate the eigenstates of the quantum system, i.e. the wave functions and energies of the quantum states. We assumed that the particle is in principle, confined in a cylindrical box in the presence of Aharonov-Bohm-type effect due to dislocation defect. Analysis of the quantum information entropy, reveals that the dislocation influences the eigenstates and, consequently, the quantum information of the system.

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