Journal of Computational Innovation and Analytics (JCIA)
Journal of Computational Innovation and Analytics (JCIA) is an international, open-access, double-blind peer-reviewed journal initiated by the School of Quantitative Sciences (SQS), Universiti Utara Malaysia. Published biannually in January and July, JCIA is entirely free of charge for authors, requiring zero submission fees and no Article Processing Charges (APCs). It provides a scholarly platform for rigorous and innovative research at the intersection of mathematics, statistics, probability and uncertainty, decision sciences, computer science applications, and artificial intelligence, welcoming theoretical and original research articles, review articles, methodological studies, and applied or case-based contributions that develop, evaluate, synthesize, or apply mathematical and statistical models, analytical methods, algorithms, computational techniques, and data-driven approaches. With a strong emphasis on theoretical advancement, computational innovation, and quantitative analytics, JCIA seeks to advance knowledge, uncover meaningful patterns, and connect emerging ideas with impactful applications across rapidly evolving interdisciplinary fields.
Ranking of criteria for order allocation in a rubber glove manufacturing factory using Analytic Hierarchy Process (AHP)
Malaysia is the world’s leading producer of rubber gloves, among over 150 manufacturers worldwide. Based on current practice among the manufacturer of rubber gloves, there is no fixed guideline in planning for the orders based on various criteria as each criterion has its importance, and the orders are planned based on the real-time situation. Therefore, in this study, the criteria to be considered for order allocation to factories and their importance were determined using Analytical Hierarchical Process (AHP) technique. Six criteria, namely quality, cost, lead time, capacity, special requirement, and regulation compliance, were identified based on the literature search of past studies in the field and supported by the expert’s opinion. Later, the experts ranked the importance of each criterion using a specifically designed questionnaire employing the AHP method. The pairwise comparison matrix was consistent with a consistency ratio (CR) value of 0.0495. Thus, the six criteria by ranking top to bottom with respective weightage are quality (25.81%), cost (21.7%), lead time (20.73%), regulation compliance (16.86%), special requirement (7.86%), and capacity (7.04%). In summary, the objectives of this research have been successfully met, according to the findings, and the criteria ranking can be used as a guideline by rubber glove manufacturers in planning for order allocation.
Copyright (c) 2023 The Author(s) https://creativecommons.org/licenses/by/4.0
Preliminary evidence on gamified learning to improve competency in Standard Marine Communication Phrases (SMCP) in Malaysian maritime education
This study presents the results of a feasibility study on the use of gamified learning to improve competency in Standard Marine Communication Phrases (SMCP) in a two-day practical course at two Vessel Traffic Service centers in Malaysia. The Standard Marine Communication Phrases (SMCP) have been used mandatorily in ship-to-ship and ship-to-shore radio communication since November 2001, and, as the working language of merchant ships, have contributed significantly to reducing miscommunication at sea. 10 vessel traffic service operators from 2 vessel traffic centers in Melaka and Johor Darul Takzim participated in this feasibility study, which used gamified learning interventions to enhance their competency in SMCP. Via gamified learning, respondents were trained to effectively memorize and use 100 SMCP in their professional duties, which involve communicating with merchant vessels operating within their areas of operation. The results of this study, which used pre- and post-test techniques, revealed that all respondents recorded significant improvement in post-test scores, supporting the effectiveness of the gamified learning method used in the experiment. Furthermore, the paired-sample t-test also confirmed a significant difference between the pre- and post-test scores. The finding enriches knowledge of SMCP teaching and learning and offers a new way to deliver effective training. It benefits maritime educators seeking additional trainee engagement methods within teaching-learning-assessment approaches for STCW (International Convention on Standards of Training, Certification and Watchkeeping for seafarers) courses.
Copyright (c) 2026 The Author(s) https://creativecommons.org/licenses/by/4.0
Technology acceptance of GitHub Copilot: A study among computing students
AI pair programmers (AIPPs) are becoming increasingly used in software development environments. This is encouraging universities to examine the role of these tools in computing education and programming courses to ensure that their programs remain market-oriented. However, despite the rapid adoption of AI pair programmers, limited empirical evidence exists regarding students' acceptance and educational use of these tools in authentic project-based software development environments. One of the most widely used AIPPs is GitHub due to its integration with many development environments. This study investigates the Technology Acceptance (TA) of GitHub Copilot among computing students. We conducted a mixed-methods study with 114 senior computer science and software engineering students from three public universities in the Kurdistan Region of Iraq. The task was to develop a static-responsive portfolio website using GitHub Copilot as an AI pair programmer. The Technology Acceptance Model (TAM) was used to evaluate Perceived Ease of Use (PEU), Time Efficiency (TE), Support in Learning (SiL), Code Quality (CQ), and Willingness to Recommend (WtR). Quantitative descriptive statistics and thematic analysis of qualitative responses were used to evaluate these features. Findings indicate high positive initial acceptance. Thematic analysis showed that students value GitHub Copilot for overcoming syntax barriers and reducing development time, but also express concerns about understanding and over-reliance. This study presents one of the first empirical mixed-methods examinations of AIPP acceptance in computing education. The contribution of the study lies in identifying the dual nature of this acceptance: high enthusiasm tempered by reflective concerns about deep learning. The results suggest that Copilot can improve efficiency in programming courses, but its use should be balanced to avoid superficial dependency on the tool.
Predicting adoption of AI-driven e-learning systems in Bangladesh’s public higher education: A survey-based machine learning approach
Over the last few years, the rapid growth of information technology (IT) has changed how digital learning systems are integrated into public higher education systems. There still seems to be a lack of understanding of the factors that affect the successful implementation of artificial intelligence (AI)- driven systems in public higher education systems. Due to inadequate infrastructure, varying levels of digital literacy among users, and a lack of empirically based models to predict system outcomes, many public higher education systems have been unable to implement AI-driven systems in their eLearning environments. This paper aims to take the first step toward developing a predictive model of AI-driven eLearning system adoption. To do this, a machine learning approach will be used to develop the model. This study employed a fully quantitative method involving 287 academic staff and students from five public higher education institutions in Bangladesh. The findings identified three factors of the Institutional eLearning Framework as the most significant predictors of eLearning adoption: perceived usefulness of eLearning, institutional support, and data quality of the eLearning system. The main contribution of this study to the body of research on the higher education system is to link one or more established theories of acceptance with predictive analysis models, thereby developing a tool that can be put in the hands of a system planner.
A compiler-based framework for automated numerical method selection via deterministic finite automata
This study introduces a compiler-integrated framework for the automated selection of numerical methods through deterministic finite automata (DFA)-based structural classification of mathematical expressions. Traditional numerical method selection approaches typically rely on expert knowledge or data-driven models, which often lack interpretability and fail to integrate seamlessly with compiler systems. In contrast, the proposed framework employs a domain-specific language, lexical analysis, canonicalization, and DFA-based pattern recognition to classify mathematical expressions into categories such as linear, polynomial, nonlinear, recurrence relations, and ordinary differential equations (ODEs). Subsequently, a rule-based selection engine maps each expression to the most appropriate numerical solver, including Newton’s method, Bisection, Secant, Euler, RK2, and RK4 methods. Evaluation of the framework on a dataset of 150 expressions yielded 96.67% classification accuracy and 97% correctness in method selection. The solvers demonstrate convergence behavior consistent with theoretical expectations, while the computational overhead remains under 2 milliseconds. This approach offers an interpretable and efficient alternative to machine-learning-based methods and demonstrates the feasibility of integrating numerical reasoning directly into compiler systems and mathematical software.
Social media usage and perceptions of pop-up advertisements among Universiti Utara Malaysia students: A quantitative analysis
The increasing reliance on digital technology has significantly shaped the media consumption behaviors of young adult students, making them a key demographic in digital engagement and online marketing. This study investigates the digital behavior and media consumption patterns of students at Universiti Utara Malaysia (UUM), with a particular focus on their interaction with social media platforms and responses to digital advertisements. Using a quantitative research approach, participants were selected through convenience sampling, and data were collected from 255 students. Descriptive and correlational analyses were conducted later, and the results were visualized using a Power BI dashboard. The findings reveal that students aged 18 to 25 are the most active digital users, primarily engaging with TikTok (76.5%) and Instagram (62%) while spending an average of 2 to 4 hours daily on social media. However, despite their high digital literacy, these students exhibit strong skepticism toward pop-up advertisements, with over 65% expressing dissatisfaction due to perceived intrusiveness and irrelevance. Moreover, it is highlighted that students with higher education levels are more receptive to advertisements, while those who spend excessive time on social media become increasingly critical of digital ads. This study concludes that young adult students are highly engaged in digital media but require targeted, personalized, and credible advertising strategies to enhance ad effectiveness. Future research should explore evolving digital trends and their long-term impact on young adults’ media consumption and purchasing behavior.
Predicting the neonatal, infant, and under-five mortality outcomes in the Philippines using Count TS GLM and GLARMA
Mortality statistics are often considered indicators of overall population health, highlighting the need for accurate forecasting to support public health planning and policy development. While various statistical models have been applied to mortality forecasting globally, research on the use of count time series modeling for mortality trends in the Philippines remains limited. Addressing this gap, this study provides the first systematic comparison of Count Time Series Generalized Linear Models (TS GLM) and Generalized Linear Autoregressive Moving Average (GLARMA) models for forecasting Philippine neonatal, infant, and under-five mortality. The predictive performance of these models was evaluated for the period 2023–2027. Prior to model implementation, stationarity was assessed using the KPSS test, after which predictive models were developed and compared, with the best-performing specifications selected based on the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE). Results show that TS GLM consistently outperforms GLARMA in predictive accuracy, with the negative binomial distribution yielding the best results. The optimal TS GLM models for neonatal (1,2), infant (1,2), and under-five mortality (1,5) surpass their GLARMA counterparts. Projected trends indicate rising absolute numbers of deaths across all subgroups. These findings provide valuable guidance for government agencies, health institutions, and insurance providers in monitoring child mortality, anticipating healthcare demand, improving actuarial projections, and informing targeted strategies, while also serving as a reference for future research on mortality forecasting using count time-series models.
A novel extension of the Rayleigh distribution for biomedical data: Properties, estimation and performance
This study introduces a new compound distribution by combining the Rayleigh distribution with the sine type II Topp-Leone (STIITL) generator, resulting in the sine type II Topp-Leone Rayleigh (STIITLR) distribution. The proposed model incorporates a trigonometric function and an additional shape parameter, offering greater flexibility in modelling diverse data behaviours. Several statistical properties of the distribution are derived, including moments, the moment-generating function, hazard and survival functions, and entropy. To estimate the model parameters, four methods were employed: maximum likelihood, Anderson-Darling, Cramér-von Mises, and maximum product of spacings. A simulation study was conducted to evaluate the consistency and accuracy of these estimators. As sample sizes increased, the estimators showed improved performance, with reductions in bias and root-mean-square error. The STIITLR model was then applied to a biomedical dataset involving survival times of patients with acute myeloid leukaemia. Compared to competing distributions, the proposed model provided a superior fit. These findings demonstrate that the STIITLR distribution is a promising tool for modelling complex survival patterns in biomedical research, where flexibility and adaptability are essential.
Ant colony optimization for efficient emergency ambulance routing in urban environments
Efficient ambulance routing plays an important role in emergency medical services. However, solving the ambulance routing problem remains challenging. This study investigates the performance of ant colony optimization to solve the ambulance routing problem, aiming to improve the quality of route planning under constraints such as traffic, patient urgency, and ambulance capacity. To simulate realistic emergency scenarios, 27 benchmark instances from the classical vehicle routing problem were adapted to the ambulance routing context by mapping depots to ambulance stations, customer nodes to emergency sites, and incorporating patient urgency and ambulance capacity. The performance of ant colony optimization was compared with the genetic algorithm and particle swarm optimization. Each algorithm was independently applied to all instances, and route quality was evaluated based on best route cost, average cost, and standard deviations. The experimental results show that ant colony optimization consistently outperformed both genetic algorithm and particle swarm optimization across most instances. Specifically, ant colony optimization achieved shorter total route distances, which are measured as the cumulative route distance of ambulances required to serve the emergency sites. These improvements were accompanied by greater consistency in solution quality across multiple runs. These findings suggest that ant colony optimization is a robust and effective tool for ambulance routing optimization. This study contributes to the growing body of work on intelligent emergency logistics by demonstrating the practical advantages of ant colony optimization in critical decision-making. The findings are valuable for optimization researchers in enhancing ambulance routing efficiency.
Computational analysis of engineering licensure outcomes under curricular change using propensity score matching
The Philippine higher education sector underwent a significant curriculum transition with the implementation of the K–12 program, which reduced the engineering curriculum from five to four years. This study examines the impact of this change on the licensure examination performance of engineering graduates. A causal-comparative design was used, with propensity score matching (PSM) applied to control for confounding variables such as age, sex, academic performance, exam timing, and degree program. Both nearest-neighbor and full matching methods were tested, with full matching selected for its superior covariate balance and sample retention. Using the matched dataset, logistic regression modeling revealed that graduates under the new 4-year curriculum had significantly lower odds (OR = 0.94) of passing the board exam than those under the 5-year track. The marginal mean comparison showed a 6.06 percentage point decrease in passing probability for the new curriculum group (p < .001), with a Cohen’s d of –0.53, indicating a moderate negative effect. These findings suggest that transitioning to the restructured 4-year engineering curriculum has not yet translated into improved licensure performance. A closer review of curriculum content, implementation fidelity, and academic support programs is needed to address this gap and strengthen outcomes under the reformed education system.
Copyright (c) 2025 The Author(s) https://creativecommons.org/licenses/by/4.0
Analyzing the impact of external factors on stock market performance in the fast food industry during boycott movements using multiple linear regression
This study examines the impact of boycott movements on the stock market performance of McDonald’s and Pizza Hut from October 2023 to January 2025. Using Multiple Linear Regression (MLR), the research analyzes key factors affecting stock prices, including war-related news, boycott-related news, trading volume, and boycott duration. The findings indicate that historical stock prices significantly influence both companies’ stock performance, with McDonald’s showing an R-squared value of 83.0% and Pizza Hut 67.7%. War-related and boycott-related news were measured based on weekly article frequency, trading volume was obtained from official stock data, and boycott duration was calculated as the number of days since the boycott announcement, measured weekly. Trading volume was found not to be statistically significant in affecting McDonald’s stock price at the 99% confidence level. At the same time, other factors, such as boycott-related news and war-related news, do not have a significant impact on either company. The results suggest that investors prioritize historical stock trends over external socio-political events. This study provides insights into the financial consequences of consumer activism and can assist investors, policymakers, and business strategists in understanding market reactions to boycott movements.
A robust median CUSUM chart for monitoring high-risk stocks in Malaysia
This study analyzes stock price fluctuations and develops a robust monitoring approach for high-risk stocks. It identifies key factors influencing stock price volatility, constructs a median CUSUM control chart as an alternative to the mean CUSUM chart, which requires a normality assumption unsuitable for stock price data, and compares the median CUSUM chart’s performance during and after COVID-19. A Fishbone diagram is used to determine factors contributing to stock price fluctuations, while a risk matrix helps identify high-risk stocks. The median CUSUM control chart is then applied to monitor stock performance, focusing on FINTEC Global Berhad. Findings indicate that during and after COVID-19, FINTEC’s stock performance declined due to financial management challenges that affected investor sentiment. The study highlights the effectiveness of the median CUSUM chart in detecting small shifts in stock fluctuations compared to the mean CUSUM chart. This approach provides a more reliable framework for monitoring stock risk and volatility. By integrating quality tools such as the Fishbone diagram and risk matrix, this study enhances risk assessment strategies in financial market analysis, offering valuable insights for investors and analysts navigating uncertain market conditions.
House price prediction in Ajah, Lagos using a random forest regression model
Housing is one of the most essential need to commoner but several factors contribute to acquiring a suitable home, factor such as low income, high material cost and most importantly the presence of middleman in acquiring suitable house posed significant challenges thereby increases the cost, this study addresses this problem by developing a random forest regressor model to predict house price in Ajah Lagos, a dataset of 24,326 instances was sourced from Kaggle and filtered to 2142 instance containing Ajah listing. The data underwent several preprocessing steps; outliers were detected using the interquartile range, and Recursive Feature Elimination was used to identify relevant features that contribute to the model while eliminating less important ones. The RF model demonstrates a robust performance with an R-squared of 68.5%, a Mean Absolute Percentage Error of 18.96%, and a Root Mean Squared Error of ₦11.1 million. Compared with existing models in the literature, the model achieved a mean absolute error of ₦8.09 million, representing a 4.82% improvement and outperforming the ridge regression models. This research contributes to the field by providing stakeholders with a data-driven tool to enhance transparency and investment decision-making in the Nigerian real estate market.
Assessing the impact of fintech on financial inclusion in Algeria: An analysis of digital financial transformation indicators (2016-2022)
This research examines the correlation between financial inclusion and financial technology (fintech), particularly emphasizing how fintech can expedite the digital financial transformation in Algeria. This research examines how individuals and institutions are able to access financial products and services, contributing to broader economic development. The study covers the period from 2016 to 2022, employing a descriptive-analytical methodology and incorporating the deductive method for analyzing economic data derived from official statistics and World Bank reports. According to the World Bank data, the adoption of fintech is slowing down due to the deficiency in financial knowledge of the Algerian banking system. According to World Bank data, the results indicate that Algeria is making slow progress in implementing fintech solutions to enhance financial inclusion. This stagnation is attributed to low financial literacy levels and the underdeveloped banking system. The originality of this paper lies in its focus on the intersection between fintech adoption and financial inclusion in the Algerian context, a relatively underexplored area. The study affirms the need to accelerate the digitization of financial and banking services in Algeria by promoting fintech products, improving software infrastructure, and enhancing network readiness indicators. The research concludes with recommendations for policymakers to improve digital financial literacy and foster innovation in banking technologies to achieve broader financial inclusion.
Adequacy of inflation-indexed life annuity: A case of defined contribution pension plan in Malaysia
Defined Contribution (DC) pension plans in some Asian countries are currently operated by the lump-sum withdrawal scheme, which allows retirees to withdraw all accrued savings in a single withdrawal. This type of withdrawal does not protect pensioners from the longevity risk. This study examines the adequacy of an alternative for the lump-sum withdrawal system, namely the withdrawal system based on the Inflation-Indexed Life Annuity (IILA). This study proposes two types of IILA: the immediate IILA and the deferred IILA. The adequacy of a life annuity is measured using the ratio of projected wealth to the projected consumption at the retirement age. For the deferred IILA, this study uses the Euler-Lagrange method to assess the optimal amount that will be set aside to finance the annuity scheme. The IILA and deferred IILA will protect retirees from purchasing under-valued annuities by adjusting the IILA to inflation risk. Furthermore, deferred IILA may improve the life quality of retirees by providing adequate retirement income during the deferment years. Analysis of data from the Household Income and Expenditure Survey (HEIS, 2014) indicates that the immediate IILA offers an average lifetime income of over 50%, while the deferred IILA provides more than 40%.
Integrating analytical hierarchical process and quadrant matrix analysis in a decision analytics framework for flood management planning
Flood management planning (FMP) requires decision analytics approaches that can prioritize multiple criteria while accounting for uncertainty in expert judgment and data availability. This study presents a decision analytics framework that integrates the Analytic Hierarchy Process (AHP) and Quadrant Matrix Analysis (QMA) to identify key flood and trade-off criteria, and to develop a structural plan to support FMP decision making. Using the PESTEL framework, a refined set of 40 criteria was assessed through structured elicitation involving 10 experts from diverse disciplines. AHP was applied to find the relative importance of each criterion, while QMA mapped the criteria by both importance and certainty. The AHP results indicate that the Political, Legal, and Economic domains were the main driving criteria for FMP. At the criterion level, P1: Fair distribution of resources was ranked first (0.1917), followed by L1: Land ownership for flood protection (0.0739) and L4: International constitutional standards and guidelines (0.0543). QMA, in contrast, identified 25 “Must‑Have” criteria, primarily within the Environmental, Social, and Technological domains. A comparison of AHP and QMA outputs reveals that criterion certainty significantly affects practical applicability, even for criteria that are highly ranked in importance. By jointly considering importance and certainty, EC7: Economic loss, EC5: Financial budget, and L3: Flood Disaster Institutional emerged as "Very Highly Ranked" and "Must-Have". The framework also incorporates a structured criterion‑reflection stage, informed by the Weather Model, to refine expert evaluations. Overall, the proposed decision analytics framework enhances clarity and consistency in the selection of criteria and offers practical guidance for FMP in Malaysia and other planning contexts.
Game-based pedagogy meets simulation: Using board games to model seaport operations in maritime education
Game-based learning has gained traction as an innovative educational initiative, particularly in fields requiring strategic decision-making and operational management. This study explores the effectiveness of a board game, Port Management Game, as a pedagogical tool for enhancing students’ understanding of seaport management concepts. The primary objective of this study is to examine how board games can facilitate experiential learning, thus improving knowledge retention and supporting the development of critical decision-making skills among higher education students studying seaport management. A quasi-experimental research design involving a control and an experimental group was employed. The experimental group participated in board game sessions integrated into their coursework, while the control group underwent traditional lecture-based instruction. Data were collected through pre-tests and post-tests to assess students' learning outcomes. A split-plot ANOVA was conducted to determine the statistical significance of the differences in performance between the two groups. The findings revealed that students in the experimental group demonstrated a statistically significant improvement in their understanding of seaport management concepts compared to those in the control group. The results align with existing literature supporting the use of game-based learning as an effective pedagogical strategy in higher education. The significance of this study lies in its contribution to maritime education by validating the use of board games as an interactive learning medium and as a tool in enhancing students’ understanding of lessons. The study also highlights the potential of board games to bridge theoretical knowledge with practical application, preparing students for real-world challenges in the maritime industry. Furthermore, the research underscores the suitability of board games as a stepping stone toward apprenticeship training, as they provide a structured yet flexible environment for learning by doing.
PLS-SEM approach to analyzing the marketing mix and its impact on repurchase intentions in the parboiled rice market
The market landscape is marked by intense competition, necessitating continuous product enhancements, particularly within the food sector. To meet evolving consumer demands and adapt to changing consumption patterns, the marketing mix serves as a strategic tool to foster profitable relationships rooted in consumer satisfaction and loyalty. This study aims to investigate the impact of the marketing mix on consumer satisfaction with parboiled rice and to examine its influence on repurchase intentions. Data were collected from 300 respondents and analyzed using PLS-SEM. Findings indicate that marketing mix elements; product, price, place, and promotion, positively and significantly influence consumer satisfaction. However, the relationship between these variables and repurchase intentions varied. The final model supports the connection between satisfaction and the intention to repurchase. Product attributes, such as the health benefits of parboiled rice, play a crucial role, alongside the significance of the sales location. Remarkably, there was no evidence to support the notion that price and repurchase intention were related, and promotion had a negative correlation with repurchase intention, which calls for more research. These insights are invaluable for parboiled rice producers and marketing managers, highlighting the need to enhance these product aspects to boost consumer engagement and loyalty.
A novel probability distribution with robust parametric properties
The continuous evolution of statistical modelling has spurred the development of novel probability distributions to address complex data challenges. This paper introduces the Topp-Leone Epsilon (TopLE) distribution, derived by combining the Topp-Leone and epsilon distributions. The TopLE distribution exhibits attractive parametric properties, including flexible shape and scale parameters, a closed-form quantile function, and efficient parameter estimation using computational tools. Simulation studies demonstrate the distribution's efficiency, consistency, and stability, making it suitable for a wide range of applications in lifetime data analysis. Two comparative applications with four standard distributions have shown the TopLE distribution to be a competitive choice in modelling real-life datasets. This work contributes to the statistical literature by providing a versatile and competitive model for modern data complexities.
Greedy heuristic algorithms for multi-objective tourist trip design problem on Penang Island
Tourism is a vital sector in the global economy, significantly contributing to the development of many countries by creating jobs, generating income, and promoting cultural exchange. However, planning a tour itinerary presents a significant challenge due to the necessity of identifying points of interest (POI) and organizing them into a proper itinerary. This study focused on solving the multi-objective TTDP for Penang Island by developing two greedy heuristic techniques: Nearest Neighbour (NN) and Nearest Greedy Insertion (NGI). The study aimed to achieve three objectives: identifying tourists’ preferences and constraints relevant to the TTDP, proposing solutions using NN and NGI techniques, and evaluating the effectiveness of these methods. Adopted a hybrid weighted objective, both algorithms balanced maximizing the number of POIs visited and popularity index while satisfying several constraints such as time windows, popularity index, restaurant selection, POI categories, and budget limitations. The findings showed that the NN technique performed better for a one-day tour, delivering a higher popularity index (212.66) compared to NGI (171.43). However, the NGI technique outperformed in a two-day tour by balancing POI coverage and popularity index of 294.91, while NN had 268.68. For a three-day tour, NN achieved a higher popularity index (359.86) than NGI (337.03), while NGI visited more POIs. Future studies should explore metaheuristic methods, broader geographic applications, incorporate time-dependent variables, and tailored solutions for halal-conscious travellers.
An empirical study on the construction of a non-convex risk parity portfolio using a genetic algorithm
Risk-based portfolio optimization has become increasingly crucial due to the limitations and underperformance of traditional Mean-Variance (MV) portfolios. In the context of the Risk Parity (RP) portfolio, capital is allocated in such a way that each asset contributes equally to the overall risk of the portfolio. However, optimizing a non-convex RP portfolio presents significant challenges. While conventional numerical methods can be applied, they often struggle with inefficiency and fail to deliver optimal results. This study addresses these challenges by proposing the Genetic Algorithm (GA) and comparing its effectiveness against Successive Convex for RIsk Parity (SCRIP) to solve the non-convex RP portfolio. Using stock price data from companies listed on the Jakarta Islamic Index (JII), we demonstrate that the GA, although yielding a solution that slightly deviates from the ideal equal-risk-contribution, offers a more efficient and practical approach than SCRIP. Notably, the RP portfolio outperforms both the Equal Weight (EW) and Global Minimum Variance (GMV) portfolios in terms of lower Value at Risk (VaR) and Turnover (TO) ratios. This research contributes to the field by showcasing the GA as a viable tool for overcoming the limitations of traditional optimization methods, particularly in addressing the complexities of non-convex RP portfolios while considering practical investment constraints.
A flexible inverse Lomax Weibull-Weibull model for survival and reliability data
In this paper, an extension of the Weibull distribution using Inverse Lomax Weibull-G (ILWG) called Inverse Lomax Weibull-Weibull Distribution (ILWWD) was introduced. The statistical properties of the ILWWD were also derived. This includes the moment, moment generating function, quantile function, and distributions of order statistics. The parameters of the ILWWD were estimated using the maximum likelihood estimates (MLE) method. Simulation studies were presented for three distinct cases. Survival regression was also presented. Goodness-of-fit statistics were employed to assess the effectiveness of the ILWWD and Log-ILWWD. This indicated that the proposed distribution is superior in fitting leukemia data. Lastly, the flexibility of the ILWWD was demonstrated using a real-life dataset. These suggest that the proposed distribution is superior in fitting the datasets presented in this study.
Analyzing economic, community, and financial performances of Philippine social enterprises using R statistics environment and Statistical Package for the Social Sciences (SPSS) software
This research examines sustainability in a social enterprise, delving into the quantitative variables, namely, social impact, social mission, and financial responsibility towards financial performance. This study evaluated whether businesses practicing social entrepreneurship were sufficiently sustainable in light of media accounts that report their economic and community performance. By examining key indicators across economic, community, and financial performance, this study provides evidence-based insights into their contributions to sustainable development and identifies areas for improvement. The participants are Metro Manila-based social enterprises that have been operating for over five years. The inclusion criteria for the social enterprises were the following: (1) sought a mission to solve social problems, (2) established a sustainable model, and (3) recognized by domestic opinion leaders to be innovative and potentially leading to more considerable systematic change. The quantitative data were analyzed using R Statistics Environment and Statistical Package for the Social Sciences (SPSS) version 23. The data was summarized in frequency tables, and the output was presented using descriptive statistics. The ratio and gathered data were tested using the Multiple Regression Analysis (MRA) to show the effect between dependent and independent variables. The results show that Economic Performance and Community Performance significantly influence most sustainability metrics of the study subjects. Among the sustainability variables, only social mission had a significant impact on Return on Assets and Return on Equity. Social enterprises may utilize the results from this study to formulate better ideas in their respective fields and create opportunities to make their businesses more sustainable.
A modification of conjugate gradient parameter and its global convergence for solving unconstrained optimization problems
The methods of nonlinear conjugate gradient coefficients are significant and helpful in solving large-scale unconstrained optimization problems, due to their simplicity and lower storage requirement. Research activities on its applications to handle higher-dimensional systems of nonlinear equations are of paramount importance. Many authors studied and developed different kinds of conjugate gradient coefficients. Recently, some conjugate gradients were proposed with large dimensions, but they have small sample sizes. So, they cannot solve problems that have higher dimensions. Therefore, this research proposed a modified algorithm with a huge dimension and a large sample size. The strategy of strong Wolfe line search was applied in the convergence analysis, which makes it possible to converge globally with a descent property. Finally, the numerical results show that the proposed algorithm performs more efficiently and is superior to the existing Conjugate Gradient (CG) coefficients.
Magnetohydrodynamics inclination effects on Couette fluctuating micro-gas flow
An analytical investigation is conducted in the laminar flow regime to examine the impacts of Magnetohydrodynamics (MHD) inclination on Couette fluctuating basic micro-gas-flow, in which the bottom plate remains fixed while the top plate swings in its plane with sinusoidal velocity. Using a transformation technique, the partial differential equations that govern the flow situation have been reduced to a system of analytically solvable ordinary differential equations. The impact of MHD inclination on the dimensionless velocity and skin friction's numerical values are displayed in a detailed analytical and graphical form. The analysis's conclusion reveals that velocity rises with increasing phase angle values but drops with increasing inclination angle, magnetism, and frequency of the fluctuating driving force.
Ensuring sustainable waqf-zakat microtakaful protection through a system dynamics approach
The Islamic financial ecosystems, such as zakat and waqf, have roles in tackling poverty and can be integrated into providing financial protection, such as death benefits to the poor through microtakaful. However, unpredictable payouts and irregular contributions can hinder the implementation of microtakaful, affecting cash flows. This paper employs a system dynamics approach to simulate the cash flows of microtakaful funds, emphasizing the influence of three key factors: the rate of new participants, the mortality rate, and the contribution rate. Note that this approach is chosen for its ability to model complex systems and predict the behavior of microtakaful funds over time. The data collected were from various institutions, including the Life Insurance Association of Malaysia (LIAM), the Central Bank of Malaysia (BNM), and the Department of Statistics Malaysia (DOSM) from 2018 to 2020. The model indicates a minimum contribution rate of RM130 per year, with a 2% mortality rate and 0.8% annual increase in the new participants. The model serves as a foundation for designing a simple and understandable non-profit microtakaful product.
Copyright (c) 2024 The Author(s) https://creativecommons.org/licenses/by/4.0
Design and implementation of a web-based information system for university staff union
The Academic Staff Union of Universities (ASUU) in Nigerian public universities has long championed the welfare and interests of academic staff. However, operational inefficiencies stemming from manual processes hinder its effectiveness in addressing contemporary challenges. This inefficiency hampers ASUU’s effectiveness and limits its ability to interact efficiently with its members and stakeholders. Therefore, this study proposes that the ASUU Web-Based Information System (AWBIS) should modernize data management, communication, and decision-making within the union. Drawing on related work in online information systems across various sectors, the methodology involves requirements gathering, leading to the development and implementation of AWBIS. Leveraging development technologies, AWBIS offers modules for member management, communication, event organization, and biometric attendance. This project uses the waterfall model approach to developing software as the research methodology. The data collection techniques include interviews and questionnaires. The AWBIS enables various operations to be performed online, consisting of registering members by the admin, logging members, and allowing members to manage their profiles. The system also provides a login page for both members and admins, each with their own username and password. A secure and accessible database for storing member profiles, academic information, and other relevant data was created. The successful implementation of AWBIS will significantly streamline ASUU’s operations, resulting in improved communication, reduced administrative burden, and enhanced transparency. Members will have access to up-to-date information, and the system will facilitate better coordination of ASUU’s activities. Future enhancements, including financial management, election processes, loan applications, and mobile app development, are suggested to further optimize AWBIS’s functionality and accessibility. AWBIS is a model for leveraging technology to advance organizational efficiency and effectiveness within the education sector.
Statistical inference on sine-exponential distribution parameter
The Sine-Exponential (Sine-E) distribution is a probability distribution that combines the periodic behavior of the sine function with the decay characteristic of the exponential function. This study addresses the problem of identifying the most accurate and reliable estimation method for the parameter of the Sine-E distribution. The objective is to evaluate various parameter estimation techniques, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), Weighted Least Squares Estimation (WLSE), Maximum Product of Spacing Estimation (MPSE), Cramer-von-Mises Estimation (CVME), and Anderson-Darling Estimation (ADE), using Mean Square Error (MSE) as the criterion for determining the technique with the minimum error. The study’s findings reveal that as sample size increases, the parameter estimates for all techniques converge to the true parameter value, with decreases in bias, MSE, and mean relative estimates. Among the techniques evaluated, the MPSE method consistently provides estimates closest to the true parameter value and exhibits the least bias and lowest MSE across small, moderate, and large sample sizes, making it the best estimator for the Sine-E distribution.
Development and implementation of a new patient appointment scheduling system at Adustech Wudil: An evaluation of user satisfaction and system impact
Globally, the healthcare sector is the pivot and integral part of human lives. Any error committed in the clinic services might lead to defects or the termination of life. In the rapidly evolving field of healthcare, efficient and accessible appointment scheduling plays a crucial role in ensuring timely and quality care for patients. This article is centered around creating a Patient Appointment Scheduling System, with a focus on handling registration and management, as well as managing bookings or making appointments and scheduling sessions at Aliko Dangote University of Science and Technology, Wudil (ADUSTECH Wudil). Identification of the drawbacks of the existing traditional system leads to the design of a computerized system, which is more user-friendly and GUI-oriented. HTML, PHP, CSS, JavaScript, and MySQL were used for the development. Note that performance was improved to 92% from using the traditional system. Similarly, the productivity of participants increased to 90%. Furthermore, the waiting time for patients to be attended was enhanced to 90% in the proposed system.
Decentralized electronic voting with Ethereum blockchain in democratic and political elections
The Ethereum blockchain-based electronic voting (e-voting) systems can emerge as a viable strategy in this era of contemporary democracies to revolutionize political elections and augment the efficacy of the electoral process. There are myriad advantages that the Ethereum blockchain has to offer, from fairness to increased voting rates. Unlike traditional voting protocols, the Ethereum blockchain can assure substantial cost savings and eliminate the necessity for electoral intermediaries. The use of the Ethereum blockchain in political contexts also ensures that elections are held with integrity while preserving the voters’ privacy. Due to its popularity, provision of smart contracts logic, and various promising advantages, this systematic review aims to examine the potential deployment of decentralized e-voting systems integrated with Ethereum blockchain technology for democratic political elections. A systematic literature review (SLR) and the PICO approach, which stands for population, intervention, control, and outcomes, were adopted in this study to systematically analyze the existing literature. Key technological approaches identified in the voting system include the hybrid blockchain and privacy-preserving score voting. Among the noteworthy findings are the following: while adoption and complexity remain challenges across numerous e-voting frameworks, scalability, end-to-end security, enhanced efficiency, and effectiveness are key benefits. An exploration into the prospective future innovations, such as the integration of artificial intelligence and big data analytics into the Ethereum blockchain, was also included to further improve the reliability of the e-voting systems. It is believed that the Ethereum blockchain has a promising transformative impact on electoral politics and democratic processes, presenting a ray of hope for future elections.