https://thaijmath2.in.cmu.ac.th/index.php/thaijmath/issue/feedThai Journal of Mathematics2024-07-09T08:27:41+00:00Prof.Dr. Suthep Suantaithaijmath@cmu.ac.thOpen Journal Systems<p><strong>Thai Journal of Mathematics</strong><br />Thai Journal of Mathematics (TJM) is a peer-reviewed, open access international journal publishing original research works of high standard in all areas of pure and applied mathematics.</p> <p><br /><strong>Publication Frequency</strong><br />From 2020 onwards, TJM publishes one volume per year which consists of four regular issues (March, June, September, and December). All manuscripts are refereed under the same standards as those used by the finest-quality printed mathematical journals. Accepted papers will be published online in their final forms using the TJM template and will be published in four issues annually.</p>https://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1573Hybrid Machine Learning Algorithm with Fixed Point Technique for Medical Data Classification Problems Incorporating Data Cryptography2024-03-15T10:03:59+00:00Wasana Ngaogatewasana.n@ubu.ac.thAlain Jeanalain.j@ubu.ac.thRattanakorn Wattanataweekulrattanakorn.w@ubu.ac.thKobkoon Janngamkobkoon_jan@cmu.ac.thTossaporn Alherbetossaporn.c@ubu.ac.th<p>Utilizing machine learning (ML) techniques for disease classification can enhance the precision and speed of disease diagnosis, enabling quicker decision-making and improved patient outcomes. ML algorithms can analyze large and complex datasets, facilitating the discovery of patterns and connections between medical history, symptoms, and disease risk. As patient medical data is sensitive and confidential, it is increasingly targeted by theft and hackers. Therefore, it is essential to safeguard this information to prevent unauthorized access. This paper proposes a hybrid approach of a fixed-point extreme learning machine with backpropagation for classifying breast cancer, heart disease, and diabetes datasets. Moreover, we used the strategy software design pattern to create a class diagram for our method. We also propose using a tool to encrypt patient data at rest, encrypting data to be safely stored on the database's hard disk. Experimental outcomes highlight the superior performance of the hybrid machine learning algorithm in comparison to the backpropagation algorithm found in the literature, particularly in the domain of data classification.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1647On Primary and Regular $\Gamma$-Semihypergroups2024-07-09T07:27:39+00:00Safoora J. Ansariansari.sjacs@snjb.orgKishor F. Pawarkfpawar@nmu.ac.in<p>In this paper the notions of primary and semiprimary $\Gamma$-hyperideals in $\Gamma$-semihypergroup are introduced. It is shown that if $rad.(I)$ is a maximal $\Gamma$-hyperideal of $S$ then $I$ is a semiprimary $\Gamma$-hyperideal of $S$. It is proved that a $\Gamma$-semihypergroup $S$ is semiprimary if and only if prime $\Gamma$-hyperideals of $S$ forms a chain under set inclusion. Regular set in $\Gamma$-semihypergroup and regular $\Gamma$-hypergroup are defined with several examples. Characterizations of regular $\Gamma$-semihypergroup is established in terms of left and right $\Gamma$-hyperideals.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1648F-Contraction-Type Fixed Point Theorems in b-Metric Spaces2024-07-09T08:15:24+00:00Muhammad Sirajo Abdullahiabdullahi.sirajo@udusok.edu.ngAbor Isa Garbagarba.isa@udusok.edu.ngJamilu Abubakarabubakar.jamilu@udusok.edu.ngPoom Kumampoom.kumam@mail.kmutt.ac.th<p>In this paper, we present a generalization and improvement of some recent results concerning F-contraction. We establish some fixed point theorems in the setting of b-metric spaces. The obtained results are proper generalization of many results in the literature. Finally, we constructed some examples to support our findings.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1649On Halpern's Proximal Point Algorithm in p-Uniformly Convex Metric Spaces2024-07-09T08:27:41+00:00Chinedu Izuchukwuizuchukwuc@ukzn.ac.zaGrace Nnennaya Ogwograceogwo@aims.ac.zaOluwatosin Temitope Mewomomewomoo@ukzn.ac.za<p>The main purpose of this paper is to introduce a Halpern-type proximal point algorithm, comprising a nonexpansive mapping and a finite composition of p-resolvent mappings associated with proper convex and lower semicontinuous functions. A strong convergence of the proposed algorithm to a common solution of a finite family of minimization problems and fixed point problems for a nonexpansive mapping is established in a complete p-uniformly convex metric space. Also, numerical examples of the proposed algorithm in nonlinear settings are given to illustrate the applicability of the obtained results.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1500Existence Theory for a Langevin Fractional q-Difference Equations in Banach Space2023-11-21T23:39:25+00:00Abdelatif Boutiaraboutiara_a@yahoo.com<p>In this work, we establish an existence theorem of solutions for a new class of nonlinear Langevin fractional q-difference equation involving Caputo q-derivative in Banach space. Indeed, we will introduce the notion of kuratowski measure of noncompactness and the Monch's fixed-point theorem, on which; our analysis of the problem will essentially be based. An example is provided to show the applicability of the main result.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1641A Note on the General Split Common Fixed Point Problem in Hilbert Spaces2024-06-10T17:37:27+00:00Pachara Jailokapachara.j4@gmail.com<p>This paper examines a general form of the split common fixed point problem in which a finite family of bounded linear operators is involved. We propose viscosity approximation methods with choosing two different types of stepsizes (one depends on operator norms and the other is selected in a self-adaptive way) for classes of attracting quasi-nonexpansive mappings and demicontractive mappings, respectively. Using the Landweber technique and some properties of the attracting quasi-nonexpansiveness, strong convergence results of the proposed methods are established in Hilbert spaces. Our results presented in this paper generalize many existing results in the literature.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1489On the Diophantine Equations $q^x + p(2q + 1)^y = z^2$ and $q^x + p(4q + 1)^y = z^2$2024-02-10T13:37:37+00:00Piyada Phosripiyada.p@lawasri.tru.ac.thSuton Tadeesuton.t@lawasri.tru.ac.th<p>In this paper, by using basic concepts of number theory, we present some conditions of the non-existence of non-negative integer solutions $(x, y, z)$ for the Diophantine equations $q^x + p(2q + 1)^y = z^2$ and $q^x + p(4q + 1)^y = z^2$, where $p$ and $q$ are prime numbers.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1496Strong Convergence Theorem of Bregman Algorithm for Solving Variational Inequalities in Banach Spaces2023-11-09T03:21:29+00:00Papatsara Inkrongpapatsara.inkrong@gmail.comPrasit Cholamjiakprasit.ch@up.ac.thKriengsak Wattanawitoonkriengsak.wat@rmutl.ac.thUamporn Witthayaratuamporn.wi@up.ac.th<p>Due to the significance of the variational inequality which related to solve various problems in other branches of sciences and engineering, in this paper, we introduce a new algorithm for finding solution of this problem by using Bregman method in real reflexive Banach spaces. Under some mild conditions, the convergence result is exactly proved. Our result improved and extended some previous results in the literature.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1155Understanding Characteristics of Thai Students through PISA 2018 Data Using Data Visualization Techniques2023-10-21T05:14:19+00:00Nirattaya Khamsemanannirattaya@siit.tu.ac.thCholwich Natteecholwich@siit.tu.ac.th<p style="font-weight: 400;">Education is the cornerstone of a nation's development, economy, and competitiveness. A clear and deep understanding of the current educational situation is needed. The Programme for International Student Assessment (PISA) has been widely used as an indicator of a nation's school system. This study is conducted using a topological data analysis technique called UMAP to obtain a deeper understanding of Thai students based on PISA 2018 data. For each subject---mathematics, reading, and science---students's performances are preprocessed into high-dimensional vectors. Our proposed method reduces the high-dimensional vectors into two dimensions. The results show deeper insights beyond mere average scores, which are unattainable through other techniques. For example, although the Thai students' average scores are below the OECD averages, their performance across various skills was not uniformly poor. Thai students excelled in the interpretation skill in both mathematics and science compared to others. In reading, Thai students performed well in the cognitive process of locating information but struggled with the process of gaining understanding. Students with higher scores were found to read more books, have higher expectations of completing a bachelor's degree, and experience less bullying. These insights are valuable for policymakers and educators aiming to improve the Thai education system.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1454The PM 2.5 Prediction & Air Quality Classification Using Machine Learning2023-10-20T09:31:41+00:00Pichitpong Soontornpipitpichitpong.soo@mahidol.ac.thLertsak Lekawatlertsak@ine.co.thChatchai Trithamchatchai.tri@mahidol.ac.thChattabhorn Trithammemodia@live.comPornanong Pongpaiboolpornanong.pon@nstda.or.thNarachata Prasertsuknarachata.pra@nstda.or.thWachirapong Jirakitpuwapatwachirapong.jira@hotmail.com<p>Forecasting plays a vital role in air pollution alerts and the management of air quality. Studies and observations conducted in Thailand indicate a concerning rise in pollution levels, particularly in the concentration of PM2.5. Bangkok, in particular, has been flagged for its alarmingly high PM2.5 concentrations. By projecting the future PM2.5 concentrations in these urban areas, we can obtain valuable short-term predictive information regarding air quality. After conducting experiments using four different machine learning algorithms, it was found that the LSTM (Long Short-Term Memory) model provides the most accurate forecasts based on various statistical evaluation indicators. These indicators include a Root Mean Square Error (RMSE) of 2.74, Mean Absolute Error (MAE) of 1.97, R-squared value of 0.94, and Mean Absolute Percentage Error (MAPE) of 10.53. Then the classified air quality based on PM2.5 from the LSTM model gives the best performance indicators including accuracy = 0.9072, precision = 0.8466, negative predict value = 0.9403, sensitivity = 0.8144, specificity = 0.9381, and F1-score = 0.8169. The results show that the machine learning model can predict PM2.5 concentration, which is suitable for early warning of pollution and information provision for air quality management systems in Bangkok.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1628Parameter Estimation for Weighted Two-Parameter Exponential Distribution2024-05-01T06:34:08+00:00Eakkpop Panyahaneakkpop.kong@gmail.comWisunee Paggardwisunee.p@cmu.ac.thManachai Rodchuenmanachai.r@cmu.ac.th<p>Herein, estimation techniques for the two parameters of the weighted two-parameter exponential distribution (WTED) are presented. To this end, various methods such as maximum likelihood estimation (MLE), method of moment (MOM), jackknife of MLE (JMLE), and jackknife of MOM (JMOM) were utilized. In a simulation study, the performance of the proposed methods were compared based on their mean square error estimates. The results show that JMLE, MLE, and MOM provide the most suitable estimators for the WTED in cases where $\theta > \beta$, $\theta = \beta$, and $\theta < \beta$, respectively.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematicshttps://thaijmath2.in.cmu.ac.th/index.php/thaijmath/article/view/1534Modified Popov's Extragradient-like Method for Solving a Family of Strongly Pseudomonotone Equilibrium Problems in Real Hilbert Space2024-01-20T07:52:33+00:00Passakorn Yordsornpassakorn.yor@mail.rru.ac.thHabib ur Rehmanhrehman.hed@gmail.com<p>In this article, we are introducing a new proximal based extragradient method and examining its convergence analysis in order to solve equilibrium problems that incorporate strongly pseudomonotone bifunction. The main superiority of this technique, in particular that the construction of an approximation solution, proof of its convergence and also proof of its appropriateness, does not needed previous information of the modulus of strong pseudo-monotonicity and the Lipschitz-type bi-functional parameters. In addition, the method uses a decreasing and non-summable stepsize sequence. Finally, numerical experiment results are provided to illustrate the method on a test problem to equate the efficiency with previously known algorithms.</p>2024-06-30T00:00:00+00:00Copyright (c) 2024 Thai Journal of Mathematics