by K. Prudhvi
Original Research
In this paper, we obtain a unique common fixed point theorem for pair of self-mappings of OWC(Occasionally Weakly Compatible) pair of mappings in C-MS(Cone -Metric Space) and also given the example for supporting this result. Our result is a generalization and improvement of the some results they are present in this references.
American Journal of Applied Mathematics and Statistics. 2023, 11(3), 98-99. DOI: 10.12691/ajams-11-3-3
Pub. Date: November 10, 2023
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by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang and Yuanxin Yao
Original Research
Water resources are essential for sustaining human livelihoods and environmental well-being. Accurate water quality prediction plays a pivotal role in effective resource management and pollution mitigation. In this study, we assess the effectiveness of five distinct predictive models—linear regression, Random Forest, XGBoost, LightGBM, and MLP neural network—in forecasting pH values within the geographical context of Georgia, USA. Notably, LightGBM emerges as the top-performing model, achieving the highest average precision. Our analysis underscores the supremacy of tree-based models in addressing regression challenges, while revealing the sensitivity of MLP neural networks to feature scaling. Intriguingly, our findings shed light on a counter-intuitive discovery: machine learning models, which do not explicitly account for time dependencies and spatial considerations, outperform spatial-temporal models. This unexpected superiority of machine learning models challenges conventional assumptions and highlights their potential for practical applications in water quality prediction. Our research aims to establish a robust predictive pipeline accessible to both data science experts and those without domain-specific knowledge. In essence, we present a novel perspective on achieving high prediction accuracy and interpretability in data science methodologies. Through this study, we redefine the boundaries of water quality forecasting, emphasizing the significance of data-driven approaches over traditional spatial-temporal models. Our findings offer valuable insights into the evolving landscape of water resource management and environmental protection.
American Journal of Applied Mathematics and Statistics. 2023, 11(3), 89-97. DOI: 10.12691/ajams-11-3-2
Pub. Date: November 02, 2023
212 Views16 Downloads
by DevaArul S and Arunthadhi N
Original Research
In this article a new control chart to monitor the compound fraction defectives is developed. The variability in the sample sizes and fraction defectives in the process are jointly monitored by using a single chart. The newly developed chart has good advantages over other charts by maintaining a single chart for two variable characteristics. This chart can monitor and control the fraction defectives in a process and at the same time will control the variability in the sample sizes. The ARL values are determined which is compared with other charts. It was found that the ARL of the proposed control chart better performed than the other control charts.
American Journal of Applied Mathematics and Statistics. 2023, 11(3), 83-88. DOI: 10.12691/ajams-11-3-1
Pub. Date: October 11, 2023
209 Views15 Downloads