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Entropy02:39

Entropy

36.3K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
36.3K
Entropy01:18

Entropy

3.6K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.6K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

24.9K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
24.9K
Entropy and Solvation02:05

Entropy and Solvation

8.4K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
8.4K
Entropy within the Cell01:22

Entropy within the Cell

12.9K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
12.9K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

5.0K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
5.0K

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Related Experiment Video

Updated: Feb 8, 2026

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
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Feature Selection Based on the Neighborhood Entropy.

Andrea Mariello, Roberto Battiti

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces neighborhood entropy, a new feature selection method that improves classification accuracy, especially for unbalanced and nonlinearly separable data. It offers a robust approach for offline analysis, prioritizing feature relevance and reducing uncertainty.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Pattern Recognition

    Background:

    • Mutual Information (MI) measures nonlinear feature-class relationships by quantifying uncertainty reduction.
    • Existing feature selection methods may struggle with highly unbalanced and nonlinearly separable datasets.

    Purpose of the Study:

    • Propose a novel feature selection measure, neighborhood entropy, related to MI.
    • Develop a filter method minimizing neighborhood entropy using a greedy approach.

    Main Methods:

    • Integration of sequential forward selection with approximated nearest-neighbors.
    • Application of locality-sensitive hashing for efficient computation.
    • Minimization of neighborhood entropy in a greedy procedure.

    Main Results:

    • Achieved higher classification accuracy compared to state-of-the-art algorithms.
    • Demonstrated superior performance on highly unbalanced and nonlinearly separable datasets.
    • Feature selection order improved accuracy with fewer features.

    Conclusions:

    • The proposed neighborhood entropy method offers a robust and effective feature selection technique.
    • Effective for offline scenarios requiring high accuracy, robustness to noise, and class imbalance.
    • Provides a superior feature ordering for enhanced model performance.