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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Dynamic sampling of images from various categories for classification based incremental deep learning in fog

Swaraj Dube1, Yee Wan Wong1, Hermawan Nugroho1

  • 1Department of Electrical and Electronic Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia.

Peerj. Computer Science
|July 29, 2021
PubMed
Summary

This study introduces a novel data sampling algorithm for incremental learning in deep neural networks. The method reduces communication costs and training time by pre-selecting training images, maintaining performance in fog computing environments.

Keywords:
Artificial neural networksClass incremental learningClassificationComputer visionData samplingDeep learningFog computingIncremental learningSupervised learningTransmission costs

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Machine Learning

Background:

  • Incremental learning enables deep neural networks to adapt to continuously arriving data.
  • High-dimensional image data and continuous data streams pose computational and communication challenges in deep learning, especially in fog computing.
  • Existing methods struggle with high communication costs between fog devices and centralized servers during incremental learning.

Purpose of the Study:

  • To develop a novel data sampling algorithm to reduce communication costs and training time in incremental learning within fog computing environments.
  • To maintain the learning performance of deep neural networks despite reducing the amount of training data.
  • To demonstrate the effectiveness of the proposed method across various model architectures, datasets, and learning settings.

Main Methods:

  • A novel data sampling algorithm was developed to discard specific training images per class before initial training.
  • The algorithm was tested with Convolutional Neural Networks (CNNs) in a fog computing setup.
  • Performance was evaluated for both static and incremental learning scenarios, focusing on communication costs, training time, and model accuracy.

Main Results:

  • The proposed data sampling algorithm effectively reduces transmission costs from fog devices to centralized servers.
  • Significant reduction in model training time was achieved without compromising learning performance.
  • The method demonstrated consistent effectiveness across different model architectures, datasets, and learning configurations.

Conclusions:

  • The novel data sampling algorithm offers an efficient solution for managing data in incremental deep learning within fog computing.
  • This approach mitigates the challenges of high communication costs and lengthy training times.
  • The method is robust and applicable to diverse deep learning scenarios, enhancing the practicality of distributed intelligence.