Balanced Lattice Designs under Uncertain Environment DOI Creative Commons

Abdulrahman AlAita,

Muhammad Aslam

Journal of Statistical Theory and Applications, Год журнала: 2024, Номер 23(3), С. 275 - 289

Опубликована: Июнь 19, 2024

Abstract Balanced lattice designs are vital in numerous fields, especially experimental design, where controlling variability among units is crucial. In practical experiments, various sources of uncertainty can lead to ambiguous, vague, and imprecise data, complicating the analysis process. To address these indeterminacies, a novel approach using neutrosophic within balanced design framework proposed, termed (NBLD). This innovative method employs statistics derive mathematical sums squares construct variance (NANOVA) table. The effectiveness proposed NBLD demonstrated through numerical example, showing that it outperforms traditional methods handling uncertainty.

Язык: Английский

(k,s)-fractional integral operators in multiplicative calculus DOI
Xiaohua Zhang, Yu Peng, Tingsong Du

и другие.

Chaos Solitons & Fractals, Год журнала: 2025, Номер 195, С. 116303 - 116303

Опубликована: Март 23, 2025

Процитировано

0

An AI-driven Predictive Model for Pancreatic Cancer Patients Using Extreme Gradient Boosting DOI Creative Commons
Aditya Chakraborty, Chris P. Tsokos

Journal of Statistical Theory and Applications, Год журнала: 2023, Номер 22(4), С. 262 - 282

Опубликована: Сен. 11, 2023

Abstract Pancreatic cancer is one of the deadliest carcinogenic diseases affecting people all over world. The majority patients are usually detected at Stage III or IV, and chances survival very low once late stages. This study focuses on building an efficient data-driven analytical predictive model based associated risk factors identifying most contributing influencing times diagnosed with pancreatic using XGBoost (eXtreme Gradient Boosting) algorithm. grid-search mechanism was implemented to compute optimum values hyper-parameters by minimizing root mean square error (RMSE). hyperparameters final were selected comparing 243 competing models. To check validity model, we compared model’s performance ten deep neural network models, grown sequentially different activation functions optimizers. We also constructed ensemble Boosting Machine (GBM). proposed outperformed models considered regard After developing individual ranked according their contribution response predictions, which extremely important for research organizations spend resources causing/influencing particular type cancer. three found be age patient, current BMI, cigarette smoking years percentages 35.5%, 24.3%, 14.93%, respectively. approximately 96.42% accurate in predicting performs excellently test data. methodology can utilized prediction purposes. It predict time death related a specific cancer, given set numeric, non-numeric features.

Язык: Английский

Процитировано

8

Artificial intelligence-based expert weighted quantum picture fuzzy rough sets and recommendation system for metaverse investment decision-making priorities DOI Creative Commons

Gang Kou,

Hasan Dınçer, Dragan Pamučar

и другие.

Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)

Опубликована: Сен. 5, 2024

Язык: Английский

Процитировано

2

Enhancing language acquisition through personalized learning: The role of collaborative filtering and recommender systems in TESOL DOI Creative Commons

Yeqin Guo

Applied and Computational Engineering, Год журнала: 2024, Номер 57(1), С. 200 - 205

Опубликована: Апрель 29, 2024

This paper explores the transformative potential of Collaborative Filtering (CF) and Recommender Systems (RS) in Teaching English to Speakers Other Languages (TESOL). By leveraging data-driven insights from learner interactions, these technologies offer personalized learning experiences that significantly enhance language acquisition, engagement, retention. Through empirical evidence quantitative analyses, we demonstrate positive impact CF RS on learners' proficiency, vocabulary communicative competence. The integration into TESOL not only facilitates adaptive pathways but also addresses practical implementation challenges, including privacy, ethical concerns, technological barriers. study underscores efficacy recommendations creating more engaging, efficient, effective environments.

Язык: Английский

Процитировано

1

Balanced Lattice Designs under Uncertain Environment DOI Creative Commons

Abdulrahman AlAita,

Muhammad Aslam

Journal of Statistical Theory and Applications, Год журнала: 2024, Номер 23(3), С. 275 - 289

Опубликована: Июнь 19, 2024

Abstract Balanced lattice designs are vital in numerous fields, especially experimental design, where controlling variability among units is crucial. In practical experiments, various sources of uncertainty can lead to ambiguous, vague, and imprecise data, complicating the analysis process. To address these indeterminacies, a novel approach using neutrosophic within balanced design framework proposed, termed (NBLD). This innovative method employs statistics derive mathematical sums squares construct variance (NANOVA) table. The effectiveness proposed NBLD demonstrated through numerical example, showing that it outperforms traditional methods handling uncertainty.

Язык: Английский

Процитировано

1