Sampling Strategy Optimization

Sampling Strategy Optimization is a methodological framework that systematically determines the most efficient and representative approach to selecting samples within a given population or process, distinguishing itself by integrating statistical rigor, resource constraints, and measurement objectives to enhance decision accuracy in contexts such as Continued Process Verification and Grape Maturity Assessment.

305

Publications

11.6K

Citations

1.1K

Authors

475

Institutions

Publications per year

2017–2026

100

Authors

1.1K

  1. MA
    Muhammad Aslam

    King Abdulaziz University, Saudi Arabia

    14 publications · 443 citations

    11

    h-index

  2. CJ
    Chi‐Hyuck Jun

    Pohang University of Science and Technology, South Korea

    10 publications · 384 citations

    10

    h-index

  3. SB
    S. Balamurali

    Kalasalingam Academy of Research and Education, India

    8 publications · 147 citations

    8

    h-index

  4. YS
    Yin Sun

    Auburn University, United States

    5 publications · 467 citations

    5

    h-index

  5. MA
    Muhammad Azam

    Pohang University of Science and Technology, South Korea

    5 publications · 243 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Magnetic Resonance in Medicine

    MRM · 6 publications · 421 citations

    6

    h-index

  2. International Journal of Production Research

    5 publications · 163 citations

    5

    h-index

  3. Cancer Chemotherapy and Pharmacology

    5 publications · 154 citations

    5

    h-index

  4. Therapeutic Drug Monitoring

    4 publications · 64 citations

    4

    h-index

  5. Computers & Industrial Engineering

    4 publications · 160 citations

    4

    h-index

1–5