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Related Concept Videos

Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
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Self-Awareness and Its Effects01:21

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Self-awareness is a psychological state in which the individual becomes the focal point of their attention. This inward focus transforms the self into an object of contemplation and assessment, influencing how individuals perceive their actions and their alignment with personal and societal standards.Triggers and Contexts for Self-AwarenessSelf-awareness can be activated by external stimuli that make individuals visually or audibly aware of themselves, such as mirrors, cameras, or recordings.
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Altered States of Awareness01:06

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Altered states of consciousness represent significant deviations from one's normal mental state. These deviations can range from subtle changes in awareness to profound transformations in perception, thought processes, and sensory experiences. Altered states of consciousness can be triggered by various factors, including drug use, meditation, hypnosis, illness, or even intense fatigue.
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Subconsciousness and No Awareness01:15

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The concept of subconscious awareness refers to the processing of information below the level of conscious thought, which significantly influences both behaviors and decisions. It is also known as waking subconscious awareness. This complex level of cognition operates without the direct awareness of the individual, facilitating rapid and simultaneous handling of multiple information streams.
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High-Level and Low-Level Awareness01:19

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Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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Classifying Matter by State02:49

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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis.

Aryan Mobiny1, Aditi Singh2, Hien Van Nguyen2

  • 1Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA. amobiny@uh.edu.

Journal of Clinical Medicine
|August 21, 2019
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Bayesian deep learning enhances skin lesion classification accuracy. A hybrid approach with physicians achieved 90% accuracy, improving diagnostic safety in medical AI.

Keywords:
Bayesian deep networkMonte Carlo dropoutmodel uncertaintyphysician-friendly machine learningskin lesion

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Biology

Background:

  • Machine learning safety is critical in medicine.
  • Confidence estimation is key for reliable AI predictions.
  • Current models often lack transparency in their certainty.

Purpose of the Study:

  • To evaluate Bayesian deep learning for skin lesion classification.
  • To improve the performance of machine-physician diagnostic teams.
  • To enhance the safety and reliability of AI in clinical settings.

Main Methods:

  • Utilized the HAM10000 dataset for skin lesion classification.
  • Implemented Bayesian deep networks, specifically DenseNet-169.
  • Developed a hybrid physician-machine workflow for diagnosis.

Main Results:

  • Bayesian deep networks improved diagnostic performance from 81.35% to 83.59%.
  • The hybrid workflow achieved 90% classification accuracy.
  • Only 35% of cases required physician referral, optimizing workload.

Conclusions:

  • Bayesian deep learning offers improved diagnostic accuracy without increased computational cost.
  • Risk-aware AI enhances the effectiveness of human-AI collaboration in healthcare.
  • This approach facilitates wider clinical adoption of machine learning.