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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Production Efficiency01:01

Production Efficiency

Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).

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Related Experiment Videos

A feature-centric decision-making framework for diagnosing and enhancing system efficiency in intelligent multi-agent

Kusum Yadav1, Lulwah M Alkwai2, Shahad Almansour3

  • 1College of Computer Science and Engineering, University of Ha'il, Ha'il, Kingdom of Saudi Arabia. y.kusum@uoh.edu.sa.

Scientific Reports
|June 9, 2026
PubMed
Summary

This study introduces a hybrid predictive framework for intelligent manufacturing systems. The approach enhances system efficiency prediction using feature selection and bio-inspired optimization, yielding accurate and interpretable results.

Keywords:
ANOVA variance decompositionMulti-agent systemsRecursive feature eliminationSHAP explainabilitySmart manufacturing

Related Experiment Videos

Area of Science:

  • Manufacturing Systems Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Industry 4.0 and 5.0 demand advanced analytics for intelligent multi-agent manufacturing.
  • Existing predictive models often lack interpretability, resilience, and high performance.
  • A feature-centric approach is needed to integrate diverse indicators for system efficiency forecasting.

Purpose of the Study:

  • To develop a feature-centric hybrid predictive framework for forecasting system efficiency in intelligent multi-agent manufacturing.
  • To enhance the interpretability, resilience, and performance of predictive analytics in Industry 4.0/5.0.
  • To integrate operational, learning-based, and cyber-physical indicators for a comprehensive system view.

Main Methods:

  • A structured pipeline involving recursive feature elimination for feature selection.
  • ANOVA-based sensitivity assessment for statistical variance attribution.
  • SHAP for global explainability and integration with tree-based models (decision trees, random forests, CatBoost, extra trees) and meta-heuristic optimizers (prairie dog optimization, electric eel foraging optimization).

Main Results:

  • Hybrid models, particularly prairie dog optimization-enhanced random forest and extra trees, significantly improved accuracy, stability, and error reduction.
  • Sensitivity analyses identified production efficiency, machine usage, Q-value, and security event as key predictors.
  • The feature-driven modeling and bio-inspired optimization approach proved viable for robust and interpretable smart manufacturing applications.

Conclusions:

  • The proposed framework offers a novel, explainable, and deployable predictive intelligence paradigm for modern multi-agent industrial systems.
  • Feature-centric modeling combined with biologically inspired optimization delivers practical, robust, and interpretable outcomes.
  • The research validates the effectiveness of integrating diverse indicators and advanced optimization techniques for industrial performance dynamics.