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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Water and Mineral Acquisition02:34

Water and Mineral Acquisition

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Specialized tissues in plant roots have evolved to capture water, minerals, and some ions from the soil. Roots exhibit a variety of branching patterns that facilitate this process. The outermost root cells have specialized structures called root hairs that increase the root surface, thus increasing soil contact. Water can passively cross into roots, as the concentration of water in the soil is higher than that of the root tissue. Minerals, in contrast, are actively transported into root cells.
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相关实验视频

Updated: May 24, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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基于混合堆叠模型和特征选择的水可饮性分类.

Ahmed M Elshewey1, Rasha Y Youssef2, Hazem M El-Bakry3

  • 1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O. Box: 43221, Suez, Egypt. ahmed.elshewey@fci.suezuni.edu.eg.

Environmental science and pollution research international
|March 6, 2025
PubMed
概括

准确的饮用水分类对于清洁水至关重要. 集体学习,特别是堆叠模型,显著提高了水质预测的准确性和可靠性.

关键词:
功能选择 功能选择机器学习是机器学习.堆叠组合组合堆叠组合组合水的饮用性水的饮用性水的饮用性分类水的饮用性.

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科学领域:

  • 环境科学 环境科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 准确的水质分类对于确保清洁水的获取至关重要.
  • 现有的水的饮用性 (WP) 评估方法需要强大的预测模型.
  • 使用了一个公开的Kaggle数据集,包括3276个水体,具有各种质量指标.

研究的目的:

  • 为机器学习准备一个水可饮性数据集.
  • 用优化算法识别水质分类中最重要的特征.
  • 评估和比较各种机器学习分类器的性能,以预测水的饮用性.
  • 通过集体学习技术,特别是堆叠来提高预测性能.

主要方法:

  • 数据预处理涉及中位数归算,规范化和合成少数人过量采样技术 (SMOTE) 进行类不平衡.
  • 使用二元粒子群优化 (BPSO) 和二元鱼优化算法 (BWAO) 进行特征选择 (FS),以确定关键水质指标.
  • 包括随机森林 (RF),梯度提升 (GB),支持向量机 (SVM),额外树 (ET),决策树 (DT) 和XGBoost在内的多个分类器被训练和评估.
  • 使用逻辑回归作为meta-learner,RF,ET和XGBoost作为基础学习者,开发了一个堆叠组合模型.

主要成果:

  • BPSO确定了七个基本特征的子集,平均误差为0.3745.
  • 额外树 (ET) 分类器在单个模型中实现了最高的性能,准确率为70.63%,F1得分为71.17%.
  • 堆叠模型表现出更好的性能,达到69.53%的准确性,71.17%的F1得分和77.62%的AUC.
  • 集体学习,特别是堆叠,在创建一个强大的水质分类框架方面被证明是有效的.

结论:

  • 集体学习方法,特别是堆叠,在水可饮性分类准确性方面提供了显著的改进.
  • 使用BPSO的特征选择有效地确定了关键的水质参数.
  • 堆叠模型为增强水质测量和管理提供了可行的和强大的方法.