Applied Mathematics and Computational Intelligence (AMCI)

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https://hdl.handle.net/20.500.14170/364
AMCI is peer-reviewed and published as an online open-access journal as well as in printed copy. The journal welcomes original and significant contributions in the area of applied mathematics and computational intelligence. It emphasises on empirical or theoretical foundations, or their applications to any field of investigation where mathematics and computational intelligence techniques are used. The journal is designed to meet the needs of a wide range of mathematicians, computer scientists and engineers in academic or industrial research.

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  • Publication
    60-year Research history of missing data: a bibliometric review on Scopus database (1960-2019)
    ( 2020-12)
    Farah Adibah Adnan
    ;
    Mohd Hafiz Zakaria
    ;
    Safwati Ibrahim
    esearch on missing data was initiatedin1960and the study on this topicgrew exponentially acrossvarioussubject areas since then.Therefore, this study aims to analyze those studies, specificallyjournal articles publishedin the context of missing data.Scopus databaseand analysis tools wereutilized to retrieve all available journal articles related tomissing dataand its data. Next, due to the large number of articles foundin the Scopus database, its informationcan only be efficiently extracted and combined using Mendeley software.To further obtained insights on the extractedinformation, VOSviewer was used to obtain network visualization and overlay visualization on authors’ keywordand citation metrics was obtained using HarzingPublish or Perish software. Additionally, the growth of publication, languages used, subject area, countriesinvolved,and publication activity were also presented using bibliometric analysis. In total, 6227journal articles werefound. The recordshows thata drastic increment of research in missing data happened in 2016, with 446publications compared to 361 in 2015. Most of the articleswere affiliated with researchers in the United States and werewritten mainly in English. Mathematics, decision sciences, medicine, and computer scienceare four subject areas that have high number of articles. It is expected that thepublications on this topic will increase significantly in 2020 due to its research trend that is currently blooming in the area of medicine and thereforeleadtopotential directions for future research
      2  14
  • Publication
    A Bibliometric analysis for AI-Powered Chatbots
    (Universiti Malaysia Perlis, 2025-02-17)
    Che Wan Shamsul Bahri Che Wan Ahmad
    ;
    Syed Arbaz Ahmed
    ;
    Khirulnizam Abd Rahman
    ;
    Syarbaini Ahmad
    ;
    Mokmin Basri
    ;
    Sahidan Abdulmana
    ;
    Alfin Hikmaturokhman
    This study reports on the bibliometric analysis of AI chatbots from 2004 to 2024 (20 years) from the Elsevier Scopus database. Through bibliographical analysis of 915 Scopus-indexed documents, the review found that this is very recent literature, with over 98.46% of the relevant documents published since 2016. The contributions of institutional publications by affiliation showed that University of Toronto had the highest number of publications. In this bibliometric analysis, we examine the application of AI-powered chatbots across various domains, focusing on their potential for service enhancement and the challenges associated with their implementation in universities and higher education environment. By reviewing selected research articles, we identify trends, patterns, and key contributors in this expanding field. Notably, AI chatbots offer numerous advantages, such as efficiently handling user inquiries, which are relevant across multiple sectors. We ensure the scientific validity of the study and provide a concise analysis of the existing literature. This bibliometric analysis aims to contribute to the knowledge base and facilitate discussions and planning for the effective deployment of AI chatbots in different sectors and also in university environment in future. In conclusion, this research offers practical recommendations to policymakers, industry leaders, and technology developers on the utilization of AI chatbots to maximize their positive impact and foster supportive environments across different industries in future.
  • Publication
    A classifying and ranking selection of healthcare tourism services
    ( 2020-12)
    Nur Syahirah Mohd Asri
    ;
    Norshahrizan Nordin
    The study is conducted to classify and rankingselection ofhealthcare tourism servicesusing the integration of the Kano model and the Fuzzy Analytic Hierarchy Process (FAHP). The selection of healthcare tourism servicesis the determinant factor towards patients’ satisfaction. FAHP isone of the quality improvement methods toimprovehealth tourism. However, FAHP cannot identify the patients’ satisfaction. The Kano Model provides a way to better understandingof patients’ satisfaction through the Kano Quality Attribute categories. Thus, the integration between the Kano Model and FAHP is proposed in this study. The study firstidentifiesthe determinant factors towards patients’ satisfaction requirements in healthcare tourism services. Next, the study measured patients’ satisfaction using Kano and classified them into five groups: must-be,attractive,one-Dimensional,indifferent,andreverse. Finally, the study ranked the determinant factors towards patients’ satisfaction requirements by usingFAHPto prioritize the most important patients’ satisfaction requirements. The findings will help the healthcare decision-maker to design and improve health tourism to enhance patients’ satisfaction in healthcare tourism services based on the most important patients’ satisfaction requirement.
      1  10
  • Publication
    A Comparison of the Rank-based and Slope-based Nonparametric Tests for Trend Detection in Climate Time Series
    (Universiti Malaysia Perlis, 2024-02-14)
    Norhaslinda Ali
    ;
    Nur Adilah Abdul Ghani
    Trend detection in climate time series data is crucial for understanding climate change, predicting future climate patterns, assessing impacts, managing resources, and formulating policies. Several trend detection methods have been introduced in the literature, including parametric and non-parametric approaches. Nonparametric trend detection methods are often considered more preferable than parametric methods in certain situations due to their flexibility and robustness. Comparing various nonparametric methods of trend detection is vital in data analysis because different techniques can yield divergent results based on the same dataset. In this study, three nonparametric trend tests which were the MannKendall (MK), Sen’s Innovative Trend Analysis (ITA) and Modified Mann-Kendall by Sen’s Innovative Trend Analysis (MMK_ITA) were compared based on their power. The MK test is a rank-based test and the ITA is a slope-based test. Meanwhile, the combination of rank-based and slope-based methods is known as the MMK_ITA test. The power analysis was conducted through Monte Carlo simulation on normal, non-normal and autocorrelated time series. The simulation results indicated that test power relied on magnitude of linear trend slope, sample sizes, distribution type and variation in time series. These tests were then applied to monthly maximum temperature from 2002 until 2021 for Selangor, Malaysia. This study found that the slope-based test performed better compared to the rank-based test and their combined methods from the simulation studies and real data application based on the calculated power.
  • Publication
    A Deep Learning Approach for Face Detection and Recognition to Initiate Human-Robot Conversation
    (Universiti Malaysia Perlis, 2024-06-04)
    Abdul Halim Ismail
    ;
    Mohammed Khaled Ahmed Al Ghaili
    ;
    Mohamad Amir Hamzah Md Yusof
    ;
    Saeed Akash Mastoi
    ;
    Muhammad Hisyam Rosle
    ;
    Bukhari Ilias
    ;
    Muhamad Safwan Muhamad Azmi
    Artificial Intelligence (AI) is currently booming at almost all field. The inauguration of OpenAI ChatGPT using Natural Language Processing (NLP) has played a vital role in exposing AI to the public. It is estimated about 1.8 billion users visit ChatGPT site in a month, with further planning of apps creation in iTunes Apple App Store and Android Google Playstore. Therefore, it is interesting and natural to implement such technology in robotic field. This paper presents the attempt to employ AI into the mobile robot system towards the main goals of conversational intelligence between human and robot. First, the robot head is designed and assembled, then a screen that functioned as the robot face is attached. Afterwards the detection and recognition system were developed giving the ability to the robot to recognize registered persons and the robot eye is able to track where the person is, in the camera Field-of-View (FOV). In addition, all these systems are developed on in-situ device i.e. NVIDIA® JetsonTM Nano. It is targeted that the proposed system is able to initiate a natural conversation between a robot and a human user.
      1  11
  • Publication
    A family of third order iterative methods for solving nonlinear equations free of second derivative
    ( 2022-12)
    Muhammad Shakur Ndayawo
    ;
    Babangida Sani
    In this paper, we proposedand analysed a family of iterative methods for solving nonlinearproblems. The methods have been developed by applying Adomian decomposition method to Taylor’s series expansion. Using one-way ANOVA, the methods are compared with other existing methods in terms of number of iterations and solution to convergence between the individual methods used. Numerical examples are used in the comparison to justify the efficiency of the new iterative methods.
      1  11
  • Publication
    A fitted numerical method for a class of singularly perturbed convection delayed dominated diffusion equation
    ( 2020-12)
    Rakesh Ranjan
    ;
    H. S. Prasad
    ;
    Md. J, Alam
    A new exponentially fittednumerical method based on uniform meshis proposed to obtain the solution of a class of singularly perturbed convection delayed dominated diffusion equation.The considered equation is first reduced to the ordinary singularly perturbedproblem by expanding the term containing negative shift using Taylor series expansionprocedure and then a three-termschemeis obtained using thetheory of finite differences.Afitting factor is introduced in the derived scheme with the help of singular perturbationtheory. Thomas algorithm is employed to find the solution of the resulting tridiagonal systemof equations. Stability and convergence of the proposed method are discussed. The method is shown to be first accurate. Computational results for two example problems are presented for different values of the grid point,Nand perturbation parameter,.It is observed that the method is capable of approximating the solution very well
      1  16
  • Publication
    A heuristic technique for finding a solution of job sequencing problem
    ( 2022-12)
    Rajpal Rajbhar
    ;
    L. N. Das
    ;
    Arun Kumar Bhardwaj
    A finite set of sequential jobs performed through a setup machines assignment within a time-bound shift-wise manner or processing with a minimum time delay and using effectiveness process of machine sequencing order within the limited resource is called job sequencing problem. This paper proposed a heuristic technique known as a time deviation for an appropriate solution to the job sequencing problem to minimize the total minimum elapsed time and idle time in detail. We have written a process n number of sequential jobs through the m machines. Initially, we discussed the job sequencing problem for n jobs processed through two and three machines separately. We have also expanded the mentioned variables in n jobs for m machines. In conclusion, we have found the sequence of the job specification with the assigned machines. If the elapsed time for the n number of jobs process through m number of machines is known, for a more significant value of n, the final optimal assignment sequential jobs determined a listed solutionby the MATLAB programming is discussed in this paper. If the total elapsed time of machines' jobs is concerned, a relationship of pre-assigned matrix elements.
  • Publication
    A human face recognition using Alyuda Neurointelligence
    ( 2019-12)
    Norpah Mahat
    ;
    Afifah Sakinah Mohamad Zuki
    Nowadays, face recognition has been one of the most popular studies. It is considered as a highly interesting topic to do a study on. With the advancement of today’s technology, face recognition has been used ina wide range of areas. For instance, face recognition is very common in the security industry. The main idea of this study is to identify the best algorithm with the smallest mean squared error (MSE). The analyses were carried out to compare the algorithms with the smallest mean squared error and to improve the previous research on face recognition based on artificial neural networks. The study on face recognition data and their evaluation by neural networks is important in detecting human faces. This study was conducted by using 45 different face images. The architecture forthe network was obtained by Alyuda Neurointelligencewhere the most popular learning algorithm such as Quick Propagation, Conjugate Gradient Descent, Quasi Newton, Limited Memory Quasi Newton, Levenberg-Marquadt, Online Back Propagation and Batch Back Propagation have been implemented and tested to measure the percentage of success. The results indicated that the Adaptive Techniques were extremely useful pattern recognition especially in identifying human faces. The Limited Memory Quasi-Newton becomes the most suitable algorithm to train the human face recognition data with the smallest MSE. Furthermore, this study has shown a strong positive relationship proven by the R-squared and correlation coefficient for all algorithms
      1  3
  • Publication
    A Hybrid Fuzzy Time Series Forecasting Model with 4253HT Smoother
    (Universiti Malaysia Perlis, 2022-12)
    Nik Muhammad Farhan Hakim Nik Badrul Alam
    ;
    Nazirah Ramli
    ;
    Adie Safian Ton Mohamed
    ;
    Noor Izyan Mohamad Adnan
    Forecasting time series data is crucial for predicting upcoming observations, especially in the market and business. Proper actions can be taken when there are some figures on future data, which are predicted based on the previous data. The fusion of fuzzy time series in forecasting has made forecasting using linguistic variables possible. However, the existence of extreme values in the time series data has led to inaccurate forecasting since the values are too large or too small. Hence, this paper proposes a hybrid fuzzy time series forecasting model with the 4253HT smoother to reduce the uncertainty of data. In this study, students’ enrolment data at the University of Alabama are implemented to illustrate the proposed hybrid forecasting model. The results show that the proposed model improves the forecasting performance since the mean square, root mean square, and mean absolute errors have been reduced. In the future, the implementation of data smoothing using the 4253HT smoother can be used in other fuzzy time series and intuitionistic fuzzy time series forecasting models.
      2  3
  • Publication
    A New Fifth Order Variable Step Size Block Backward Differentiation Formula with Off-Step Points for the Numerical Solution of Stiff Ordinary Differential Equations
    (Universiti Malaysia Perlis, 2023-11-10)
    Buhari Alhassan
    ;
    Yusuf Hamza
    ;
    Musa Hamisu
    ;
    Naghmeh Abasi
    A new fully implicit two point variable step size based on block backward differentiation formula with two off-step points for the numerical integration of first order stiff ordinary differential equations in initial value problems is proposed. The methods are derived by introducing three different values of the step size ratio to the existing fifth order 2-point block backward differentiation formula with off-step points for solving stiff ordinary differential equations. The methods approximate two solutions values with two off-step points simultaneously at each step of the integration in block. The order, error constant, and consistency of the methods are presented. The stability analysis of the methods indicates that the methods are both zero and A-stable. The proposed methods are implemented in Microsoft Dev C++ compiler using Newton’s iteration and the numerical comparison of results with existing algorithm of the same order shows that the proposed methods are better in terms of accuracy and compete with 3DIBBDF in terms of computation time. Hence, the proposed methods serve as alternative solver for stiff ODEs.
  • Publication
    A New Hybrid Three-Term HS-DY Conjugate Gradient In Solving Unconstrained Optimization Problems
    (Universiti Malaysia Perlis, 2024-02)
    Muhammad Aqiil Iqmal Bin Ishak
    ;
    Siti Mahani Binti Marjugi
    Conjugate Gradient (CG) method is an interesting tool to solve optimization problems in many fields, such design, economics, physics and engineering. Until now, many CG methods have been developed to improve computational performance and have applied in the real-world problems. Combining two CG parameters with distinct denominators may result in non-optimal outcomes and congestion.In this paper, a new hybrid three-term CG method is proposed for solving unconstrained optimization problems. The hybrid threeterm search direction combines Hestenes-Stiefel (HS) and Dai-Yuan (DY) CG parameters which standardized by using a spectral to determine the suitable conjugate parameter choice and it satisfies the sufficient descent condition. Additionally, the global convergence was proved under standard Wolfe conditions and some suitable assumptions. Furthermore, the numerical experiments showed the proposed method is most robust and superior efficiency compared to some existing methods.
      1  1
  • Publication
    A new method for solving fully fuzzy linear fractional programming with a triangular fuzzy numbers
    ( 2014-12)
    N. Safaei
    In this paper, we propose a method of solving the fully fuzzy linear fractional pro-gramming (FFLFP) problems, where all the parameters and variables are triangular fuzzynumbers. In the proposed method, the given FFLFP problem is decomposed into three crisplinear fractional programming (CLFP) problems with bounded variables constraints, threeCLFP problems are solved separately and by using its optimalsolutions, the fuzzy optimalsolution to the given FFLFP problem is obtained. Fuzzy ranking functions and addition ofnonnegative variables were not used and there is no restriction on the elements of coefficientmatrix in the proposed method. Finally, numerical example are used in order to show theefficiency and superiority of the proposed method.
      8  9
  • Publication
    A new Soil Management System in Enhancing the Yield of Plantation Using IoT Technique and Machine Learning for Smart Pineapple Farming
    (Universiti Malaysia Perlis, 2024-02-14)
    Norhanna Amalin binti Che Ismail
    ;
    Elmy Johana binti Mohamad
    The machine learning technique is studied to aid farmers in decision-making and analysing soil quality based on the nitrogen, phosphorus, and potassium NPK nutrients as the current soil in Malaysia experience degradation of soil organic that affect in production of the nutrient for the crops. The research aim is to study and analyse the Artificial Neural Network model in analysing the quality of soil based on the prediction of NPK level class, which the data collected from Smart Agri-Scan. Next objective is to evaluate the prediction and accuracy of the model. The ANN model is constructed in Neural Net Fitting App in MATLAB. A feedforward neural network is applied to the ANN model and trains it with two different training functions and a different number of neurons of hidden layers. The model with the smallest Mean Square Error is chosen for data analysis as it means the model has the best performance. From the prediction graph, the output of training and validation that corresponds to the prediction model is observed. The points of the output prediction close to the reference line are considered a good prediction model, which means it can analyse soil quality accurately. In future, the model might be able to do the analysis and decision directly at the monitoring platform based on the real-life prediction data.
  • Publication
    A note on “a new method for solving fully fuzzy linear fractional programming with a triangular fuzzy numbers”
    ( 2015-12)
    Sapan Kumar Das
    ;
    T. Mandal
    ;
    S. A. Edalatpanah
    The objective of this paper is to deal with a kind of fuzzy fractional programming problem where all the parameters and variables are triangular fuzzy numbers. We point out an error in the recently published article (Safaei, Appl. Math. Comp. Intel., 3 (2014) 273-281.) and then correct it. An example is also presented to demonstrate the new form.
      6  10
  • Publication
    A numerical study of the mathematical model offlow in the petroleum reservoirs
    ( 2013-07)
    P. Reihani Ardabili
    This paper concerns with the solution of a nonlinear, degenerate, convection-diffusion problem describing two-phase flow in porous media. A numerical procedure based on decomposition scheme is developed to solve the proposed problem. For illustration purpose, two test problems are considered and their series and exact solutions are compared.
      26  12
  • Publication
    A preliminary report on the utilization of Galerkin-PSO method for solving interpolation-like problem
    ( 2012-12)
    Ayu Fitri Yanti
    ;
    Iwan Pranoto
    The interpolation-like problem discussed in this paper is to search an optimal curve minimizing a functional cost and at the same time interpolating several given points. Instead of solving the optimization problem with constrain directly, we transform the problem into a pure optimization problem, without constrain. After that, the Galerkin Method is used to make the problem finite dimensional one. The problem becomes finding a minimal point and value of a finite dimensional function. The Particle Swarm Optimization (PSO) algorithm is used to minimize this function.
      29  8
  • Publication
    A production inventory model with constant production rate, linear level dependent demand and linear holding cost
    ( 2022-12)
    Alhamdu Atama Madaki
    ;
    Babangida Sani
    In this paper, a production inventory model is proposed which considers products with limited life and a little amount of decay. In real life problem, there are many scenarios that happened in production inventory which were not taken into consideration byShirajul Islam and Sharifuddin [19], who formulated a production inventory model and considered both the holding cost and the production rate to be constant. They assumed that the demand is a linear level dependent. Their paper has been modified and extended by considering the holding cost to be linearly dependent on time and the demand rate during production is assumed to be smaller than the demand rate after production. The proposed production inventory model is formulated using systems of differential equations including initial and boundary conditions and typical integral calculus were also used to analyze the inventory problems. These differential equations were solved to give the best cycle length of the model to minimize the inventory cost. A mathematical theorem and proof are presented to establish the convexity of the cost function. From the numerical examples giving to illustrate the application of the model, a Newton-Raphson method has been used to determine the optimal length of ordering cycle to be 0.54814, optimal cycle time=2.3014 (840days), optimal quantity=32.9675 and total optimal average inventory cost per unit time=18.253 and accompanied by sensitivity analysis to see the effects of the parameter changes.
      4  17
  • Publication
    A robust diagonally implicit block method for solving first order Stiff IVP of ODEs
    ( 2022-12)
    Abdu Masanawa Sagir
    ;
    Muhammad Abdullahi
    In this work, a block of diagonally implicit backward differentiation method with two off-step points for solving first order stiff initial value problem of ordinary differential equation was derived. In the proposed block method two approximate solution values of 𝑦𝑛+1and 𝑦𝑛+2with two off-step points 𝑦𝑛+12and 𝑦𝑛+32 are computed concurrently for each iteration. The properties of the newly proposed method were found to be an A-stable, Zero stable and capable for solving first order Stiff IVPs. To validate the performance of the proposed method, some first order stiff IVPs are solved and the result obtained was compared with other existing numerical schemes. From the tabulated results and the graphs plotted, the proposed method has shown advantages of accuracy in the scale error over the three methods and an advantage of executional time over two of the existing methods considered.
  • Publication
    A robust diagonally implicit block method for solving first order Stiff IVP of ODEs
    ( 2022-12)
    Abdu Masanawa Sagir
    ;
    Muhammad Abdullahi
    In this work, a block of diagonally implicit backward differentiationmethodwith two off-step points for solving first orderstiff initial value problem of ordinary differential equation wasderived. In the proposed block method two approximate solution values of 𝑦𝑛+1and 𝑦𝑛+2with two off-step points 𝑦𝑛+12and 𝑦𝑛+32are computed concurrently for each iteration. The properties of the newly proposed method were found to be an A-stable, Zero stable and capable for solving first order Stiff IVPs. To validate the performance of the proposed method, some first order stiff IVPs are solved and the result obtained was compared with other existing numerical schemes. From the tabulated results and the graphs plotted, the proposed methodhas shown advantages of accuracy in the scale error over the three methods and an advantage of executional time over two of the existing methods considered.
      2  2