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Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Hybrid Zones

Hybrid Zones

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Heuristics

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.
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Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Decision Making: Traditional Method

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.
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Nursing Clinical Information System

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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The Nature-Based Solutions Case-Based System: A hybrid expert system.

Shahryar Sarabi¹, Qi Han¹, Bauke de Vries¹

¹Information Systems in the Built Environment (ISBE) Group, Department of Built Environment, Eindhoven University of Technology, Groene Loper 3, 5612 AE Eindhoven, Netherlands.

Journal of Environmental Management

|November 10, 2022

Summary

This summary is machine-generated.

Learning from past Nature-Based Solutions (NBS) projects is key. An expert system, NBS-CBS, uses AI to extract knowledge from NBS repositories, aiding future planning and design.

Keywords:

Artificial intelligence Case-based reasoning Expert system Knowledge extraction NBS Nature-based solutions

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Area of Science:

Environmental science
Computer science
Artificial intelligence

Background:

Nature-Based Solutions (NBS) are increasingly adopted but face uncertainties.
Extracting knowledge from existing NBS implementation data is challenging.
Effective information systems are needed to facilitate knowledge extraction from NBS repositories.

Purpose of the Study:

To introduce the NBS Case-Based System (NBS-CBS), an expert system designed for knowledge extraction from NBS experience repositories.
To develop an intelligent, adaptive, and explainable system for deriving information and recommendations for NBS planning and design.

Main Methods:

The NBS-CBS utilizes a hybrid architecture combining artificial neural networks (ANNs) and case-based reasoning (CBR).
A 'black-box' ANN model is integrated with a 'white-box' CBR model for comprehensive analysis.
Expert testing was conducted to evaluate the system's functionality and accuracy.

Main Results:

The NBS-CBS successfully derives information and provides recommendations from NBS experience repositories.
The hybrid system demonstrates intelligence, adaptability, and explainability.
Expert evaluations confirmed the system's functionality and accuracy.

Conclusions:

The NBS-CBS effectively facilitates knowledge extraction from NBS implementation data.
The system offers valuable insights and inspirational recommendations for NBS planning and design.
This approach enhances the successful adoption of Nature-Based Solutions by leveraging past experiences.