Adaptive Mobile-Assisted Language Learning: A Bayesian Framework Study for Optimal Learning Content Selection DOI
Yanmei Zhao, Mohd Mokhtar Muhamad, Siti Salina Mustakim

et al.

Published: Dec. 4, 2023

With the development of mobile technology, mobile-assisted language learning (MALL) is increasingly becoming a mainstream method. This study explores using Bayesian framework to optimize content selection meet learners' diverse and individual needs. The researcher briefly introduces Bayes' Theorem prior posterior modeling. Considering challenges learner diversity resources, also discusses how these affect adaptive selection. Based on theoretical foundations, model MALL constructed, design data processing, parameter selection, algorithms are described. effectiveness advantages in optimizing validated through case Duolingo, selected application. concludes with summary key findings, recommendations for practical applications, directions future research possible challenges. Specifically, results demonstrate that significantly enhances adaptability personalization MALL, evidenced by improved engagement efficiency Duolingo. These highlight potential approach revolutionizing personalized experiences digital platforms.

Language: Английский

The log-cosine-power unit distribution: A new unit distribution for proportion data analysis DOI Creative Commons
Suleman Nasiru, Christophe Chesneau, Selasi Kwaku Ocloo

et al.

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 10, P. 100397 - 100397

Published: Jan. 11, 2024

Continuous formulations of new distributions defined on the unit interval have gained attention because their relevance in modeling proportion data. We innovate this research direction by combining logarithmic, cosine, and power functions to create a log-cosine-power cumulative distribution function that defines distribution. The corresponding probability density has originality having tangent as primary term. Furthermore, graphical analysis shows can produce truly attractive model: is capable in-depth data exhibiting inverted-J, J, decreasing-constant-increasing shapes. This demonstrated using two datasets, results reveal it potential provide better parametric fit proportional than other existing with support interval.

Language: Английский

Citations

10
A Statistical Framework for a New two-parameter Unit Bilal Distribution with Application to Model Asymmetric Data DOI Creative Commons
Tabassum Naz Sindhu, Anum Shafiq, Muhammad Bilal Riaz

et al.

Heliyon, Journal Year: 2024, Volume and Issue: unknown, P. e38340 - e38340

Published: Sept. 1, 2024

Language: Английский

Citations

8
A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction DOI Creative Commons
Muhammad Siddique,

Faisal Saleem,

Muhammad Umar

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2712 - 2712

Published: April 25, 2025

This study presents a hybrid deep learning approach for bearing fault diagnosis that integrates continuous wavelet transform (CWT) with an attention-enhanced spatiotemporal feature extraction framework. The model combines time-frequency domain analysis using CWT classification architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (BiLSTM), and 1D convolutional residual network (1D conv ResNet). effectively captures both spatial temporal dependencies, enhances noise resilience, extracts discriminative features from nonstationary nonlinear vibration signals. is initially trained on controlled laboratory dataset further validated real artificial subsets of the Paderborn dataset, demonstrating strong generalization across diverse conditions. t-SNE visualizations confirm clear separability between categories, supporting model’s capability precise reliable potential real-time predictive maintenance in complex industrial environments.

Language: Английский

Citations

0
Unit Gamma-Lindley Distribution: Properties, Estimation, Regression Analysis, and Practical Applications DOI Open Access
Kadir Karakaya, Şule Sağlam

GAZI UNIVERSITY JOURNAL OF SCIENCE, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 1

Published: April 26, 2025

This study proposes the unit Gamma-Lindley distribution, a novel bounded statistical model that extends flexibility of existing distributions for modeling data on (0,1) interval. The proposed distribution is characterized, by closed-form expressions derived its cumulative probability density, and hazard rate functions. Some properties, including moments, order statistics, Bonferroni, Lorenz curves, entropy, etc. are examined. To estimate unknown parameters, several estimation methods introduced their performance assessed through Monte Carlo simulation experiment based bias mean square error criteria. A real application focusing firm management cost-effectiveness highlights practical utility model, demonstrating superior fit compared to current distributions, such as beta Kumaraswamy. Furthermore, regression developed with parameter performed using maximum likelihood method. new provides an alternative analyzing response variables. findings contribute literature offering flexible comprehensive framework data, theoretical advancements applicability.

Language: Английский

Citations

0
Statistical analysis of disability: Utilizing the modified kies power unit inverse Lindley model DOI
Mohamed A. Abd Elgawad, Safar M. Alghamdi,

Rana H. Khashab

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 126, P. 181 - 195

Published: May 1, 2025

Language: Английский

Citations

0
Quasi-Cauchy Regression Modeling for Fractiles Based on Data Supported in the Unit Interval DOI Creative Commons
José Sérgio Casé de Oliveira, Raydonal Ospina, Víctor Leiva

et al.

Fractal and Fractional, Journal Year: 2023, Volume and Issue: 7(9), P. 667 - 667

Published: Sept. 4, 2023

A fractile is a location on probability density function with the associated surface being proportion of such function. The present study introduces novel methodological approach to modeling data within continuous unit interval using or quantile regression. This has unique advantage as it allows for direct interpretation response variable in relation explanatory variables. new provides robustness against outliers and permits heteroscedasticity be modeled, making tool analyzing datasets diverse characteristics. Importantly, our does not require assumptions about distribution variable, offering increased flexibility applicability across variety scenarios. Furthermore, addresses mitigates criticisms limitations inherent existing methodologies, thereby giving an improved framework interval. We validate effectiveness introduced two empirical applications, which highlight its practical utility superior performance real-world settings.

Language: Английский

Citations

6
Certain Properties and Characterizations of Multivariable Hermite-Based Appell Polynomials via Factorization Method DOI Creative Commons
Mohra Zayed, Shahid Ahmad Wani, Ali M. Mahnashi

et al.

Fractal and Fractional, Journal Year: 2023, Volume and Issue: 7(8), P. 605 - 605

Published: Aug. 4, 2023

This paper introduces a new type of polynomials generated through the convolution generalized multivariable Hermite and Appell polynomials. The explores several properties these polynomials, including recurrence relations, explicit formulas using shift operators, differential equations. Further, integrodifferential partial equations for are also derived. Additionally, study showcases practical applications findings by applying them to well-known such as Hermite-based Bernoulli Euler Thus, this research contributes advancing understanding utilization hybrid in various mathematical contexts.

Language: Английский

Citations

2
Advanced Mathematical Approaches in Psycholinguistic Data Analysis: A Methodological Insight DOI Creative Commons
Cecília Castro, Víctor Leiva, Maria do Carmo Lourenço-Gomes

et al.

Fractal and Fractional, Journal Year: 2023, Volume and Issue: 7(9), P. 670 - 670

Published: Sept. 5, 2023

In the evolving landscape of psycholinguistic research, this study addresses inherent complexities data through advanced analytical methodologies, including permutation tests, bootstrap confidence intervals, and fractile or quantile regression. The methodology philosophy our approach deeply resonate with fractal fractional concepts. Responding to skewed distributions data, which are observed in metrics such as reading times, time-to-response, time-to-submit, analysis highlights nuanced interplay between time-to-response variables like lists, conditions, plausibility. A particular focus is placed on implausible sentence response showcasing precision chosen methods. underscores profound influence individual variability, advocating for meticulous rigor handling intricate complex datasets. Drawing inspiration from mathematics, findings emphasize broader potential sophisticated mathematical tools contemporary setting a benchmark future investigations psycholinguistics related disciplines.

Language: Английский

Citations

1
Study of the inverse continuous Bernoulli distribution DOI Open Access
Festus C. Opone, Christophe Chesneau

Malaya Journal of Matematik, Journal Year: 2024, Volume and Issue: 12(03), P. 253 - 261

Published: July 1, 2024

The continuous Bernoulli distribution, a one-parameter probability distribution defined over the interval [0, 1], has recently garnered increased attention in realm of applied statistics. Numerous studies have underscored both its merits and limitations, alongside proposing extended variants. In this article, we introduce an innovative modification through inverse transformation, thereby introducing distribution. main characteristic lies transposition distribution’s properties onto \( [1, +\infty)\), without necessitating any additional parameters. initial section article elucidates mathematical novel encompassing essential functions quantiles. Inference for associated model is carried out via widely employed maximum likelihood estimation method. To evaluate efficacy estimated model, comprehensive simulation study conducted. Subsequently, model’s performance assessed practical context, using data sets from diverse array sources. Notably, our findings demonstrate superior comparison to broad spectrum analogous models support even surpassing established Pareto model.

Language: Английский

Citations

0
New Statistical Residuals for Regression Models in the Exponential Family: Characterization, Simulation, Computation, and Applications DOI Creative Commons
Raydonal Ospina, Patrícia L. Espinheira, Lelio Alejandro Arias Vizcaino

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(20), P. 3196 - 3196

Published: Oct. 12, 2024

Residuals are essential in regression analysis for evaluating model adequacy, validating assumptions, and detecting outliers or influential data. While traditional residuals perform well linear regression, they face limitations exponential family models, such as those based on the binomial Poisson distributions, due to heteroscedasticity dependence among observations. This article introduces a novel standardized combined residual nonlinear models within family. By integrating information from both mean dispersion sub-models, new provides unified diagnostic tool that enhances computational efficiency eliminates need projection matrices. Simulation studies real-world applications demonstrate its advantages interpretability over residuals.

Language: Английский

Citations

0