The Mindanawan Journal of Mathematics

Notes on Pell and Pell-Lucas Sequences with Negative Subcripts

Sukran Uygun — Fri, 31 May 2024 00:00:00 +0800

In this study, we establish some properties of Pell and Pell-Lucas sequences with negative subcripts by using nth power of a special matrix. Some of the properties for these sequences are obtained by matrix algebra.

On Henstock Approach to Uncertain Integral with respect to a Liu Process

Arlan Jr. Castro, Mhelmar Labendia, Michael Frondoza — Fri, 31 May 2024 00:00:00 +0800

In this paper, we introduced a new Henstock-type integral called Liu-Henstock integral, a generalized Liu integral of an uncertain process with respect to the Liu process. We then showed that the Liu-Henstock integral adheres to the standard properties of the integral.

Forecasting Monthly Rice Stock in the Philippines Using Time Series Models

Ma. Carmel Bajao, Anna Rose Barlisan, Karen Jalop, Johniel Babiera — Fri, 31 May 2024 00:00:00 +0800

This study investigated the status of rice stocks in the Philippines by analyzing data obtained from the Philippines Statistics Authority (PSA) spanning from January 2000 to March 2023. The exponential smoothing and Box-Jenkins methods were used to build a forecasting model for the rice stock in the Philippines. The different models for each method were evaluated in the training dataset using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Akaike's Information Criterion (AIC). The Holt-Winters(A,A,A) model and the ARIMA (0,1,0)(1,1,1)₁₂ model were the candidate models for the exponential smoothing method and Box-Jenkin method, respectively. The two candidate models had close performance in the training stage. However, the Holt-Winters(A,A,A) had smaller forecasting errors in the testing set. Thus, the final forecasting model was Holt-Winters(A,A,A). The forecast from the model suggests that Philippine rice stock may be enough to supply the country if there is no increase in the demand for rice until March 2024.

Integer Nonlinear Programming Formulation for some Variations of Paired Domination in Multigraphs without Loops

Lyle Leon Butanas, Mhelmar Labendia, Karlo Orge — Fri, 31 May 2024 00:00:00 +0800

In this paper, we construct integer programming formulations for the paired, twin paired, paired restrained, and outer paired dominating set problems.

Let $G$ be a multigraph. A set $S\subseteq V(G)$ is called a \textit{paired dominating set} (resp., an \textit{outer paired dominating set}) if it is a dominating set in $G$ and $\langle S\rangle$ (resp., $\langle S^c\rangle$) contains at least one perfect matching. The \textit{paired domination number} $\gamma_p(G)$ (resp., \textit{outer paired domination number} $\gamma_{op}(G)$) is defined to be the minimum cardinality of a paired (resp., an outer paired) dominating set $S$ in $G$. Moreover, a set $S\subseteq V(G)$ is called a \textit{twin paired dominating set} (resp., \textit{paired restrained dominating set}) in $G$ if $S$ is a paired dominating set and $\langle S^c\rangle$ contains a perfect matching (resp., contains no isolated vertex). The \textit{twin paired domination number} $\gamma_{tp}(G)$ (resp., \textit{paired restrained domination number} $\gamma_{pr}(G)$) is defined to be the minimum cardinality of a twin paired (resp., paired restrained) dominating set $S$ in $G$.

A Comparative Study of Machine Learning Algorithms for Regression in Predicting the Academic Performance of Students in General Mathematics

Mary Christine Ontolan, Redeemtor Sacayan, Bernadette Tubo — Fri, 31 May 2024 00:00:00 +0800

This study explores the application of predictive modeling techniques in assessing the academic performance of Senior High School students at Notre Dame of Midsayap College, focusing on General Mathematics. Employing three distinct machine learning algorithms — multiple linear regression (MLR), random forest regression (RFR), and support vector regression (SVR) — the study aims to predict students’ General Mathematics grades. Evaluation of these algorithms’ predictive capabilities is conducted utilizing metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and adjusted R-squared. Results indicate that the multiple linear regression model exhibits superior predictive performance, yielding lower RMSE and MAE values compared to RFR and SVR models, achieving an accuracy prediction of 97.29%.