Data Analytics and Applied Mathematics (DAAM)
DAAM is a biannually peer-reviewed journal (June and December Issues) - dedicated to publish significant advances covering data analytics and applied mathematics fields. DAAM welcomes submissions in statistics and data science, pure mathematics, operational research, applied mathematics, and computational mathematics. Publication in DAAM is free of charge. Starting 2022, DAAM will be published as March and September Issues.
Teaching basic data literacy through Python: Integrating basic computer science and Mathematics for lower secondary students
This conceptual paper proposes an interdisciplinary pedagogical approach to teaching basic data literacy to lower secondary students by combining elements from the Basic Computer Science (BCS) and Mathematics syllabi in Malaysia's KSSM curriculum. Motivated by the growing importance of digital skills and real-world data analysis, this conceptual paper aims to address the limited exposure students have to data handling and interpretation in current classroom practice. The proposed method uses Python to introduce concepts such as data preprocessing, data visualization, and basic statistical analysis, with example tasks and hypothetical datasets to demonstrate how the approach can be implemented. Although the approach has not yet been implemented in classroom settings, it is designed to align with syllabus standards and promote cross-curricular connections. No student feedback or empirical results are reported in this paper. Instead, it presents a pedagogical approach that makes data literacy accessible, engaging, and practically relevant for young learners.
Copyright (c) 2026 The Author(s) https://creativecommons.org/licenses/by-nc/4.0
Modelling the age-dependent force of infection for hepatitis in Plateau State using a Catalytic Linear Infection Model
This study investigates the age-specific transmission dynamics of hepatitis infections in Plateau State, Nigeria, using the Catalytic Linear Infection Model (CLIM). Age-stratified surveillance data from the Surveillance, Outbreak Response Management and Analysis System and the National Population Commission were analysed to estimate the force of infection and mean time to infection (MTI). Maximum likelihood estimation was employed to fit the CLIM, with bootstrap procedures providing robust uncertainty measures. Competing models, including the Weibull and Exponential infection-age models, were fitted for comparative evaluation using log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The CLIM achieved the best fit, exhibiting the lowest AIC and BIC values. Estimated parameters indicated a linearly increasing force of infection after a threshold age of approximately 1.5 years, with a mean time to infection of 12.13 years ( ). A comprehensive simulation study demonstrated consistent estimator performance, with decreasing bias and RMSE as sample size increased. The findings highlight the suitability of CLIM for modelling hepatitis transmission in settings with gradual age-related exposure and provide insights for optimising age-targeted public health interventions. The study extends catalytic modelling literature and offers the first CLIM-based characterisation of hepatitis transmission in Plateau State.
Comparative study of threshold selection methods in the generalised Pareto distribution with application to rainfall datasets
Extreme rainfall events pose significant challenges for flood risk management and infrastructure planning, necessitating robust statistical tools for accurate risk assessment. This study rigorously compares four threshold selection methods for the generalised Pareto Distribution across five distinct rainfall datasets from Southwest England, New Zealand, Bangladesh, Singapore, and the US (Seattle). The methods evaluated include the classical mean residual life plot, a goodness-of-fit p-value-based approach, a parameter stability method, and an automated procedure that combines goodness-of-fit testing with the method of estimation. Return level estimates for 10-, 50-, and 100-year events were estimated, with uncertainties quantified via a bootstrap percentile method. The results reveal that, while each method has its merits, the approach based on goodness-of-fit criteria coupled with the method of estimation generally provides a slight edge. It consistently delivers logically interpretable thresholds that balance bias and variance effectively, particularly in managing datasets with a high prevalence of zero rainfall events. Although alternative methods occasionally yield narrower confidence intervals, they sometimes sacrifice the accurate representation of tail behaviour. Importantly, the study does not dismiss the reliability of other techniques; rather, it underscores that threshold selection is inherently dataset dependent. Overall, this study proposes that while each method offers specific advantages depending on the dataset's characteristics, the approach that integrates goodness-of-fit testing with estimation techniques consistently achieves a favourable balance between simplicity, interpretability, and statistical robustness for threshold selection in generalised Pareto modelling of rainfall extremes. These findings highlight the importance of methodological adaptability and contribute valuable insights toward improving flood risk assessments under diverse climatic conditions.
The numerical solutions in three-dimensional double diffusive Jeffrey fluid flow with injection effect
Various engineering and industrial applications are employing the idea of convective double diffusive fluid stream, particularly through an extending surface. In recent times, many researchers have been interested in investigating the stream of Jeffrey fluid due to its application in modelling the behaviour of polymer melts during processing, such as extrusion or injection moulding. Therefore, this study is intended to develop a mathematical model for double diffusive Jeffrey fluid streaming past an elongating sheet. The influence of the injection parameter, together with the non-Newtonian properties, is taken into consideration. By using a similarity transformation, the governing equations, boundary conditions, and physical parameters have been altered into ordinary differential equations. As a result, it is found that the fluid flow is influenced by the injection rate, mixed convection, and buoyancy ratio. This statement is supported by the study, which shows the uplift of velocity as the injection rate parameter increases for larger values of boundary layer thickness. The stream velocity also surges with the rise of the mixed convection parameter and the buoyancy parameter, resulting in a slimmer boundary layer. Meanwhile, the thermal and mass exchange properties depend on the Soret–Dufour factor, where the temperature profile augments for several values of Dufour parameter, and the concentration profile increases for diverse values of Soret parameter.
Development of a retrieval-augmented generation framework for automated proposal generation through Azure cloud
Artificial intelligence (AI) has been increasingly adopted to improve knowledge retrieval and decision-making in enterprise environments; however, proposal preparation in the pre-sales stage still relies heavily on manual searches across fragmented document repositories and heterogeneous file formats. This study addresses this gap by proposing a retrieval-augmented generation (RAG)-based system that automates the retrieval, summarization, and generation of proposal-related information from both internal repositories, such as OneDrive and SharePoint, and external web sources. The proposed system integrates Azure Blob Storage, Azure AI Search, Azure OpenAI, and Bing Search within a RAG framework, supported by a web-based interface developed using React/Next.js. Unlike conventional keyword-based search tools, the system interprets user intent and delivers consolidated, relevant information to support proposal drafting. Experimental evaluation in a pre-sales use case demonstrates a reduction in manual information retrieval effort and improved content relevance, while achieving an average response generation time of 102 milliseconds, enabling real-time interaction. Overall, the findings demonstrate how secure, enterprise-grade cloud integration and RAG-based conversational systems can transform pre-sales workflows by allowing professionals to shift their focus from manual information gathering to higher-value strategic content development.
Evaluating job stress factors among secondary school teachers using fuzzy decision-making trial and evaluation laboratory (DEMATEL)
Stress is defined as an individual’s struggle to manage challenges from their surroundings, leading to unhappiness and discomfort. Job stress is a mental and physical state that influences one's productivity, effectiveness, and well-being in the workplace. Individuals experiencing job stress may face negative emotional states and teachers are not exempt from encountering stress in their professional environment. Despite growing concerns, existing research lacks a structured, data-driven approach to analyze key stress factors. Thus, this study aims to identify and evaluate the primary causes and effects of job stress among secondary school teachers using a systematic decision-making approach. The fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with Simplified Centroid Defuzzification (SCD) was applied to assess nine stress-related criteria which are job satisfaction, mental health, burnout, workload, environment discomfort, student’s behavior, role conflict, unrelated tasks, and personal problems. Data were collected through expert evaluations from three secondary schools. The findings indicate that mental health, role conflict, and personal problems were the primary causes of teacher stress, while the remaining factors were effects. Based on the distribution map result, mental health influenced all the effect criteria while job satisfaction, burnout, workload and student’s behavior have mutual relationship. Personal problem was categorized as isolated criteria since it was neither influenced by nor influencing other criteria. These findings provide valuable insights for educators, school administrators, and policymakers to implement targeted interventions. Addressing the right key factors that cause stress can improve teacher well-being, enhance job satisfaction, and contribute to a more effective education system.
Copyright (c) 2025 The Author(s) https://creativecommons.org/licenses/by-nc/4.0
Modified magnetic flow of Williamson nanofluid over a stretching sheet with Newtonian heating
In systems where heat transfer and flow dynamics are crucial, the Williamson nanofluid model is a useful tool for studying non-Newtonian fluids. Numerous industrial applications, such as energy systems, manufacturing processes, and thermal management systems, depend on non-Newtonian fluid flows. In conjunction to this, this paper aims to investigate numerically the boundary layer flow of a Williamson nanofluid across a stretching sheet under modified magnetic influence and Newtonian heating (NH). Thus, the goal of the study is to investigate how the velocity and temperature profiles are affected by several characteristics, such as the concentration of nanoparticles, the strength of the magnetic field, the Williamson fluid properties, and thermal boundary conditions. To solve the nonlinear ordinary differential equations resulting from the application of similarity variables to the governing partial differential equations, the Runge–Kutta–Fehlberg (RKF45) method is utilized. To comprehend their impact on the fluid's behaviour and heat distribution, the analysis assesses several pertinent physical characteristics. The findings show that while magnetic effect reduces velocity, raising the Williamson parameter improves thermal performance. It is anticipated that these discoveries will advance our knowledge of non-Newtonian nanofluid flows and offer guidance for improving engineering systems that employ them.
Graphical user interface-based fuzzy logic system for efficient student selection in Sekolah Berasrama Penuh, Malaysia
The selection process for Malaysia's Sekolah Berasrama Penuh (SBP) is essential for identifying and providing opportunities to qualified students who demonstrate strong academic performance, leadership skills, and active participation in cocurricular activities. Nonetheless, various inefficiencies, including potential biases, inconsistent judgments from evaluators, and prolonged procedures, hinder the current manual evaluation system. This study investigates these limitations through a qualitative methodology that includes in-depth interviews with SBP evaluators and a thorough examination of current selection criteria and procedures. The findings highlight multiple critical challenges associated with the manual system, including the labour-intensive process of handling multiple evaluation criteria, the inherent subjectivity in assessments, and variability in interpretations among evaluators. This project seeks to create a system grounded in fuzzy logic to transform the selection process for SBP. The fuzzy logic model was developed in MATLAB, using academic, leadership, and co-curricular scores as input variables. The analysis yielded a thorough, proportionate assessment for each candidate. A Mamdani FIS is used to build a model that addresses subjectivity in non-academic assessments, thereby enhancing equity and consistency among candidates. A user-friendly graphical user interface (GUI) has been developed using MATLAB as a desktop application, facilitating easy data input and enabling quick evaluations. The findings indicate that adopting an automated system powered by fuzzy logic may greatly enhance both the fairness and efficiency of student selection for SBP Malaysia.
RIDIT scoring method for ranking finance fraud cases
The arising number of fraud cases poses a serious threat to financial security. Detecting fraud is challenging due to dataset characteristics, particularly the imbalance between fraudulent and non-fraudulent activities, which may lead to biased results. To address the issue, this study introduces the Relative to an Identified Distribution (RIDIT) scoring method to identify potential financial fraud. RIDIT scores are calculated for different years in financial data, helping rank financiers’ preferences based on fraud risk. The study applies this method to the New York Suspicious Activity Report Statistics dataset, which includes 14 types of fraudulent activities such as credit or debit card fraud, wire fraud, Ponzi schemes, and consumer loan fraud. Each category is ranked based on mean RIDIT scores, which range between 0 and 1. A score below 0.5 indicates lower fraud risk, while a score above 0.5 suggests higher potential fraud. Results show that credit or debit card fraud (0.3282) and wire fraud (0.4475) have the lowest potential fraud. In contrast, consumer loan fraud has the highest mean RIDIT score (0.7081), highlighting a greater risk and the need for close monitoring. The novelty of applying RIDIT Scoring in this study contributes to the financial system by providing a useful tool to rank suspicious activities. It helps the financiers and organizations to identify the high-risk of suspicious activity effectively. Thus, allows financiers minimize losses and promote financial transparency.
Shooting method with root finding algorithm to solve boundary value problems
One important numerical method for converting Boundary Value Problems (BVPs) into Initial Value Problems (IVPs) is the shooting method, which is mostly used as a root-finding technique. Until the boundary conditions are met, this approach entails making initial-condition predictions and iteratively improving them. Since it guarantees that the initial values yield a solution that satisfies the given boundary conditions, root discovery plays a critical role. Although the shooting approach works well, it can be sensitive to initial estimates, which can sometimes cause convergence problems. Therefore, choosing the right root-finding techniques becomes essential. Thus, this study will assess how well the shooting method performs when paired with various root-finding algorithms to solve BVPs. The effectiveness of these algorithms is compared based on their convergence rate, accuracy, and robustness. The results demonstrate the effectiveness of combining the shooting method with root-finding algorithms to deliver accurate and efficient solutions to BVPs, making it a valuable tool in scientific and engineering applications.
A comparative study of the Regula Falsi method, Newton's method, and the steepest descent method for solving nonlinear equations
A numerical method has been introduced to help mathematicians to solve the functions. Numerical methods are powerful tools used to approximate solutions to equations that may not have exact solutions or are difficult to solve analytically. This study presents a comparative analysis of numerical methods that is the Regula Falsi method, Newton's Method, and Steepest Descent method. These methods are employed for solving nonlinear equations. All these methods will be compared and tested with eight different types of test functions including polynomials, exponentials, cubic, and trigonometric functions and also with different tolerance and initial guess. The performance of these methods was evaluated using performance profiles based on the number of iterations and CPU time. The results demonstrate that Newton's Method outperforms the other approaches, exhibiting the fastest convergence, and the least computational cost. Regula Falsi shows moderate performance, while Steepest Descent lags in efficiency due to its higher iteration count and CPU usage. The findings underscore the significance of selecting appropriate numerical techniques to optimize computational efficiency, with potential applications across diverse scientific and engineering disciplines.
Fuzzy inference system for corrosion engineer interview selection: Insights from Idemitsu Sdn. Bhd.
Recruitment is a crucial factor that organizations consider while seeking the most appropriate candidates, hence requiring a balance between equity and efficiency. This research concentrates on constructing a fuzzy inference system (FIS) appropriate for the recruitment of corrosion engineers at Idemitsu Sdn. Bhd. It is intended that the FIS be used to reduce subjectivity and complexity in assessing the candidate by utilizing fuzzy logic principles. Academic qualifications, certifications, industry experience, and skills were among the key criteria transformed into homogenized inputs for the system. MATLAB Application Designer tools were developed to translate the FIS into an operational format. MATLAB Application Designer was also used for real-time evaluation of prospects and immediately produced an acceptance or rejection decision based on its suitability score. This study demonstrates that implementing FIS provides significant benefits by making the recruitment process more objective, consistent, and efficient.
The impact of velocity on Casson fluid passing through an infinite inclined accelerated plate
The current research examines the impact of velocity on a Casson fluid passing through an infinite inclined accelerated plate. The problem is the occurrence of radiation and magnetic parameters, and the governing equations are solved analytically using the Laplace transform method. The outcomes are illustrated through graphical representations, followed by a discussion on various physical aspects of the problem. The findings reveal that higher Casson parameter values reduce the velocity profile due to stronger yield-stress effects, while enhanced thermal radiation increases the fluid’s kinetic energy, thereby accelerating motion. Over time, the velocity profiles extend deeper into the fluid domain. Conversely, stronger magnetic fields suppress the velocity, whereas greater porosity enhances it by reducing resistance within the porous medium and enabling smoother flow. Inclined plates are widely used to study heat and fluid transport in systems such as geothermal units, heat exchangers, and solar collectors. Their geometry introduces gravitational effects, making the analysis more realistic than for horizontal or vertical surfaces.
Magnetohydrodynamic flow and convective heat transfer of hybrid ferrofluid towards stagnation point on horizontal flat plate
Ferrofluid is broadly used for dissipating heat for devices and equipment with an applied magnetic field. However, the conventional ferrofluid needs to be improved to achieve higher thermal conductivity, better heat dissipation and improved stability. Therefore, this study examines the hybrid ferrofluid flow and convective heat transfer at the stagnation point on a horizontal flat plate in the presence of a magnetic field and thermal radiation. A hybrid ferrofluid is composed of magnetite (Fe3O4) ferroparticles and copper (Cu) nanoparticles in water base fluid employed in this investigation. The interaction between the hybrid ferrofluid flow and the heated surface is measured using the modified boundary layer mathematical model. These equations are simplified using the similarity transformation, then solved numerically by the Keller-box method, which is implemented in MATLAB software. The influence of particle volume fraction, magnetic parameter and thermal radiation parameter on the flow field is studied. The results of the velocity profile, temperature profile and Nusselt number for the hybrid ferrofluid are compared with conventional ferrofluid to discover the hybrid ferrofluid's performance. It is found that a reduced Nusselt number rate for the hybrid ferrofluid increases compared to the conventional ferrofluid between 2.01% and 2.34%. Consequently, the hybrid ferrofluid has better thermomagnetic convection properties than conventional ferrofluid, which is probably suitable for use in various electronic devices or equipments.
Copyright (c) 2024 The Author(s) https://creativecommons.org/licenses/by-nc/4.0
Application of statistical quality control technique: Applications in the silicone manufacturing industry
Nowadays, silicone rubber material is used for the manufacturing of a wide range of automotive applications, electrical and electronic applications, lighting applications, medical and healthcare applications, cookware, etc. leading to rapid growth in the silicone rubber subsector. Quality-related problems were the major obstacle that leads to customer complaints. Therefore, it is important to review the quality control procedures for the silicone rubber products. In this article, the objectives are mainly focused on the study of the process stability in the ABC Industrial Sdn. Bhd. using statistical quality control tools, the analysis of the most frequently occurring types of defects and the identification of the possible causes of the defects. The result of the studies showed that both filling and packing processes in ABC Industrial Sdn. Bhd. are statistically control. Secondly, the most frequently occurring types of defects identified in both filling and packing process in ABC Industrial Sdn. Bhd. are folding issue at the end of tube, followed by inconsistent filling weight, leaking, seal too tight, mixed colour and wrong tube issues. The possible causes consisted of new operator, careless, lack of focus during work, supplier non-conformance, defective raw material, lack of expert to setup machines, wear and tear, aging of machines, poor working environment, lack of supervision and training, and unclear standard operating procedures (SOPs). Results are beneficial and useful for silicone rubber manufacturing sector for continuous improvement by applying statistical quality control techniques.
Analysis of mixed convection near a stagnation point on a vertical porous plate and heat generation
This study explores the heat transfer characteristics of mixed convection flow near a stagnation point on a vertical plate in the presence of internal heat generation. The analysis assumes a steady, two-dimensional laminar flow and considers the combined effects of forced and natural convection. The boundary layer equations are employed to develop a model that considers heat generation and buoyancy. Similarity transformations are used to transform the governing partial differential equations into a system of ordinary differential equations which are then numerically solved using BVP4C solver in (MATLAB). The variation of the heat transfer coefficients is the focus of the numerical results for different values of the mixed convection parameter heat generation parameter and the Prandtl number . The findings illustrate that when Prandtl number increases, the heat transfer coefficients rise as well, signifying greater thermal diffusion and thinner thermal boundary layers. The heat transfer rate is also increased by increasing the mixed convection parameter since buoyancy-driven flow occurs. Higher heat transfer coefficients are also influenced by the presence of heat generation parameter with the becoming more noticeable at higher Prandtl number values. These results show how responsive the area of stagnation variation flow and thermal factors to provide important details to improve thermal management in systems that have significant volumetric and convectional heat sources.
Development of a deep learning model for prediction of cardiovascular disease
15.1% of medically certified deaths in 2023 were due to ischemic heart disease (IHD), according to Department of Statistics Malaysia (DOSM) statistics on causes of death in Malaysia published in October 2024. Despite the slight decline, IHD remains a significant health concern in Malaysia, especially among males and individuals aged 41–59 years, where it accounted for 19.8% of deaths in that age group. Regular checks are one approach to preventing heart disease in its early stages; however, they can be expensive and time-consuming. With the advancement of technology, people can now conveniently check their blood pressure, heart rate, and electrocardiogram (ECG) using smartwatches. However, since some people lead busy lives and occasionally forget to track or monitor their health through the applications, monitoring alone is insufficient. The primary goal of this research was to develop a deep learning model for predicting cardiovascular disease (CVD) using data from smartwatches, which offer non-invasive and real-time health monitoring capabilities. The research employs two deep learning techniques: Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. ECG and heart rate data were collected from 20 volunteers using local smartwatches supplemented with a publicly available dataset from Kaggle. Data pre-processing involved denoising ECG signals and normalising heart rate readings to ensure accuracy and reliability. The models were evaluated using precision, recall, F1-score, and accuracy metrics, achieving over 99 per cent across all measures. While both models demonstrated high predictive power, the LSTM model outperformed the CNN in computational efficiency, completing model training in 31 minutes compared to 87 minutes for the CNN. The study highlights the potential for wearable devices for real-time CVD monitoring and early diagnosis. Future work will explore the inclusion of additional data sources and advanced modelling involving ensemble techniques.
Potholes Detection from Dashcam using Deep Learning Approach
Deep learning has attracted considerable interest in the previous ten years and has established itself as a leading technology in the artificial intelligence sector. In object detection based on image processing, characteristics are extracted from images, and after that, data including category, position, and motion are collected and analyzed. Object detection same goes with potholes detection. Due to the potential for crashes and injuries, potholes can be dangerous for cyclists, pedestrians, and moving automobiles. The discovery and repair of potholes may fundamentally change with the development of automated pothole detecting systems. Road repair is necessary to prevent such traffic accidents in the future. Therefore, in order to perform road maintenance, it is necessary to identify difficulties with things like potholes. The aims of this research is to identify number of potholes on asphalt or concrete roads from the images obtained for both primary and secondary data, to investigate the accuracy of the testing by using the deep learning approach which is Convolutional Neural Network (CNN) algorithm and to differentiate between normal road and potholes by evaluating them using confusion matrix which are “actual potholes”, “predicted potholes”, “actual normal” and “predicted normal”. Data collecting has been done around Universiti Teknologi Malaysia's road. Deep learning has been applied in this study is CNN (Convolutional Neural Networks), which was used as the model of this project. Results obtained are in confusion matrix which it concludes the actual number of potholes there. The actual potholes obtained were less than expected so this means more data needed if this project extended in the future. To improve and to achieve better performance of this project, data collecting can be done by adding other places so there will be more data and resolution of the images can be sharper so the results may be more accurate. To make our roadways reliable, efficient, and long-lasting, research on pothole detection is essential.
Designing an implicit Block Backward Differentiation Formula (BBDF) for stiff ordinary differential equations
First-order Ordinary Differential Equations (ODEs) are often characterized by stiffness, especially in models that describe complex real-world processes. This work presents a four-point, fixed-coefficient, diagonally implicit block backward differentiation formula (4BBDF) of second order, developed to address the numerical challenges associated with stiffness. The formulation incorporates a diagonal matrix into the Lagrange interpolation polynomial and is constructed using Maple to ensure accuracy and stability. Newton’s method is used to handle the nonlinear systems that arise. The proposed 4BBDF method is mathematically verified to be consistent, zero-stable, A-stable, and of second-order accuracy. Its implementation in C++ shows improved computational efficiency, reducing the number of steps required by approximately 50% when compared to existing methods. These results indicate that the proposed scheme is a reliable and effective tool for solving stiffness in ODE.
Characteristics of nerve graph: A view from graph terminologies
In this study, the characteristics of nerve graph are investigated through graph theory. This study addresses the problem of identifying and characterizing nerve graph from the perspective of simple, degree, bipartite, adjacency and cycles terminologies. Employing the proof-by-cases method, this article systematically analyses the characteristics of the nerve graph and reveals distinct features based on its structural configurations. This research significantly contributes to graph theory field by enhancing the understanding of nerve graph and paving the way for further research and practical applications.
Milestones and developments in classical statistical time series forecasting: a comprehensive review
Time series offers a potent framework for forecasting the future based on past data. Time series forecasting, a cornerstone of predictive analytics, is essential to decision-making in many fields, including finance, economics, weather, and healthcare, necessitating various forecasting methodologies. In the 1950s, statistical tools for forecasting began utilising exponential smoothing methods. These approaches were changed depending on the pattern seen in the data sets and the objective of the analysis. Automated functions were used after simple additive and multiplicative effects to assess the complexity of the data for forecasting purposes. This paper thoroughly reviewed the developments and turning points in classical time series forecasting techniques. Time series analysis has long been built on classical forecasting methods, shaping the field's growth. A thorough examination of the literature and historical data has identified noteworthy advancements that have significantly influenced the area. The review emphasises how classical approaches can still be helpful in today's data-driven society and how they can be combined with cutting-edge data science methodologies. This review also provides insightful analyses into the lengthy past and bright future of time series forecasting by charting the development of conventional approaches.
Analytical Hierarchy Process based on intuitionistic interval approximation of trapezoidal intuitionistic fuzzy numbers
The fuzzy Analytical Hierarchy Process (AHP) is a method in fuzzy decision-making, which helps decision makers make the best choice of alternatives when dealing with uncertainty. The objective of this paper is to propose a conversion method of trapezoidal intuitionistic fuzzy numbers (TrIFN) into interval-valued intuitionistic fuzzy numbers (IVIFN), followed by a new fuzzy AHP based on the proposed approximation. The linguistic judgments of decision makers are converted into TrIFN before being transformed into IVIFN using the nearest weighted intuitionistic interval approximation (NWIIA). The proposed model is implemented in supplier selection problems by taking into account five criteria comprising cost, quality, service, cycle time, and reputation. The results showed that the proposed model simplifies the fuzzy AHP using TrIFN without affecting the consistency of the solutions from the existing method, thus supporting the fact that TrIFN has a better capability of processing ill-defined quantities. This is because TrIFN entails a generalization of the triangular intuitionistic fuzzy number (TIFN), making it a better form of representation of linguistic variables for reducing the inexactness.
Nik Muhammad Farhan Hakim Nik Badrul Alam
Carbon nanotube hybrid nanofluid induced by a stretched sheet with aligned magnetic field
Hybrid nanofluid are a new fluid created by combining two different types of nanoparticles dispersed in a base fluid to enhance the properties of base fluid. The incorporation of carbon nanotubes (CNTs) into a base fluid greatly enhances heat transfer performance over metal-based nanofluids. Partial differential equations (PDEs) are then transformed into simpler form of Ordinary differential equations (ODEs) using similarity transformation variables. The obtained ODEs are then encoded in Maple software using the Runge-Kutta Fehlberg Fourth Fifth (RKF45) method. By comparing the findings with those from earlier research, the authenticity of the result is confirmed. The results and discussion are focused on several parameters, including the stretching parameter, heat sink/source parameter, magnetic field parameter and aligned angle parameter over the velocity and temperature profiles. The results show that the velocity profile increases due to the increasing stretching parameter and decreases due to increasing aligned angle parameter and magnetic field parameter. The increasing value of heat source/sink parameter showed no effect on velocity. The temperature profile increases due to the increasing magnetic field parameter and aligned angle parameter. Meanwhile, the temperature profile decreases owing to the increase of stretching parameter and heat source/sink parameter. Findings of this study is related to the aerospace applications, specifically in the development of thermal protection systems where the thermal load management depends on the material conductive properties.
Efficiency of Ridders’ method in solving nonlinear equations using Scilab programming
The study aims to compare the efficiency of numerical methods such as Bisection, Secant, Newton-Raphson, Ridders and Halley’s methods in solving nonlinear scalar equations. The research provides numerical experiments on the efficiency and accuracy of the methods by focusing on the number of iterations, accuracy, and computational time. Based on the numerical results, Ridder’s method outperforms other methods in terms of accuracy and efficiency for all the nonlinear problems. Although the Secant method did perform well for problems involving polynomial function and the Bisection method did perform well for problems involving exponential function, the method is not as efficient as Ridder's method in terms of computational time. Newton-Raphson method although gives quadratic convergence the method has slightly higher number of iterations than the Ridder’s and Halley’s methods. Hence, the research underscores the significance of numerical methods in solving nonlinear equations by using an open-source programming language which is Scilab.
Conway-Maxwell-Poisson model fitting to HFMD data in Malaysia
Since the first Hand, Foot and Mouth Disease (HFMD) outbreak occurred in Sarawak, Malaysia in 1997, the number of reported cases has remained in a cyclical pattern. Numerous HFMD research involve clinical and laboratory findings. However, there is limited research that models the distribution of the HFMD dataset in Malaysia using a count data model fitting. This study aims to demonstrate the flexibility of the Conway-Maxwell-Poisson (COM-Poisson) regression model. All daily reported cases of HFMD from 2009 to 2019 were analysed and presented by each state in Malaysia. A normal Poisson was extensively used, but it has limitations in terms of the equi-dispersion assumption. Thus, the performance of the COM-Poisson model was investigated and compared to the Poisson model by looking at the goodness of fit (log-likelihood, AIC, and BIC) test values. The results showed that COM-Poisson models fit the HFMD dataset well with lower AIC and BIC values compared to the Poisson model. The parameter coefficients' estimated values also indicated smaller values than in the Poisson model. Given the versatility of the COM-Poisson distribution, it is effective for statistical applications and procedures, such as modelling count data. In addition, the potential for related research was also being examined to improve the accuracy of the predictive model.
Finite cyclic q-group’s automorphisms with qr -generator
The finite cyclic q-group, where q being odd prime, requires generators to validate the formation of automorphisms. A summary of cyclic groups, automorphisms, and characteristics is provided as a foundation for this research. By evaluating the automorphisms, the generators for the qr -power cyclic group in their specific sequence have been uncovered and presented as the key finding.
Determining the differential operator boundary value problems on graphs
The behaviour of functions on graph structures is studied mathematically in the context of differential operator boundary value issues on graphs. The finding of differential operators for graph boundary value issues is the focus of this abstract. The main goal is to comprehend how these operators can be utilized to fully describe and solve the problem of differential equations with boundary conditions for a second-order differential operator. These operators can also be applied to analyse and solve problems pertaining to graph structures, system formulation of a boundary value problems operator on graphs, and boundary value problems operator on graphs.
Dual slip effects on mixed convective MHD flow with viscous dissipation and Joule heating past an unsteady exponentially stretching sheet
This study aims to conduct a quantitative examination of the dynamic behaviour of a two-dimensional mixed convection unsteady flow with magnetohydrodynamics (MHD) effects over an exponentially stretching sheet submerged in a permeable medium. In addition to the temperature and velocity slip conditions at the boundary, the study takes into account a number of other variables, including suction or blowing effects, viscous dissipation, heat production or absorption, Joule heating, and thermal radiation. The first step in solving this intricate issue is to apply the necessary similarity transformation to convert the controlling partial differential equations into a set of linked nonlinear ordinary differential equations (ODEs). Subsequently, numerical techniques implemented in MATLAB, particularly employing the bvp4c solver, are utilized to solve the reduced equations. This approach facilitates the exploration of the system's behaviour under different parameter regimes, shedding light on the underlying physical phenomena. The study utilizes both graphs and tables to examine how different parameters affect temperature, velocity, skin-friction coefficients, and Nusselt numbers. Increasing the velocity slip parameter slows down heat transfer and raises skin-friction coefficients, while higher thermal slip parameters reduce heat transmission rates.
Predicting Malaysian stock market with relative strength index and moving average convergence-divergence indicators using long short-term memory
Some Malaysians hold stocks or mutual funds, and majority of them are new investors. Bursa Malaysia’s CEO stated that based on stock exchange data up to 2022, there are 80.8 million units of odd lot shares in Central Depository System (CDS) accounts, which has been expanding over the years. However, many people still need to learn how to use technical indicators for investing. Even though many researchers have used the Relative Strength Index (RSI) and Moving Average Convergence-Divergence (MACD) on other stock markets throughout the globe, there are limited studies that have been utilizing both indicators in the Malaysian stock market. This study mainly focuses on developing predictive modeling for individual investors, specifically RSI and MACD indicators on conventional Malaysian stock. Our research hypothesis is that the machine learning (ML) approach, the Long Short-Term Memory (LSTM) technique, can predict the Malaysian stock market. By combining the RSI and MACD indicators with LSTM, this study explores the possibilities of timing (buy and sell signals) in Malaysian stock market. This study used daily data gathered from the KLCI Index from 2011 until 2021. Performance metrics, including mean return, standard deviation risk ratio, and Sharpe ratio, were calculated, and hypothesis testing was conducted using t-tests on mean return and F-tests for risk comparison. The empirical data from the Malaysian market demonstrated that the RSI and MACD indicators combined with ML provide a considerable excess return. This strategy is applicable to Malaysia's conventional stock market since it generates an excess profit. The results show that MACD-LSTM provides more profitable trading than RSI-LSTM.
Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit
Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.
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