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Related Concept Videos

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Softwoods and Hardwoods01:28

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Softwoods and hardwoods, derived from different types of trees, are distinguished by their leaf structures and cellular compositions, each serving unique purposes in construction and manufacturing. Softwoods come from cone-bearing trees with needle-like leaves and are predominantly composed of longitudinal cells called tracheids and a smaller proportion of radial cells known as rays. Due to their cellular structure, softwoods are commonly used in construction for structural frames, sheathing,...
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Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Related Experiment Video

Updated: Sep 20, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Improve the Deep Learning Models in Forestry Based on Explanations and Expertise.

Ximeng Cheng1,2, Ali Doosthosseini1, Julian Kunkel1

  • 1Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Göttingen, Germany.

Frontiers in Plant Science
|June 9, 2022
PubMed
Summary

This study uses explainable artificial intelligence (XAI) and feature unlearning to improve deep learning models in forestry. Explanations guide training, enhancing classification accuracy and model credibility.

Keywords:
classificationdeep neural networksexplainable artificial intelligencefeature unlearningforest care

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Area of Science:

  • Forestry science
  • Computer science
  • Artificial intelligence

Background:

  • Deep learning models excel in forestry applications like damage detection.
  • The 'black-box' nature of these models limits trust and practical use.
  • Explainable AI (XAI) and feature unlearning offer potential solutions.

Purpose of the Study:

  • To apply XAI and feature unlearning to forestry deep learning models for the first time.
  • To generate model explanations guided by expert knowledge.
  • To improve model performance and credibility in forestry applications.

Main Methods:

  • Utilized explainable artificial intelligence (XAI) methods for model interpretability.
  • Employed feature unlearning techniques to refine model performance.
  • Integrated expert knowledge via an automatically generated annotation matrix.
  • Conducted experiments using synthetic and real leaf image datasets.

Main Results:

  • Model training was successfully guided by expertise, as evidenced by explanations.
  • Classification accuracy improved by up to 4.6% across experiments.
  • Explanation assessment metrics (RMSE, cosine similarity, important pixel proportion) showed improvement.
  • Expertise was effectively encoded and utilized in an annotation matrix format.

Conclusions:

  • Deep learning studies in forestry should prioritize both performance and interpretability.
  • XAI and feature unlearning can enhance deep learning models using expert knowledge.
  • This integrated approach offers a novel pathway for credible AI applications in forestry.