Search alternatives:
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
design optimization » bayesian optimization (Expand Search)
a learning » _ learning (Expand Search), e learning (Expand Search)
binary co » binary _ (Expand Search)
co design » _ design (Expand Search)
primary a » primary _ (Expand Search), primary i (Expand Search), primary aim (Expand Search)
Showing 1 - 20 results of 284 for search '(( binary co design optimization algorithm ) OR ( primary a learning optimization algorithm ))', query time: 0.64s Refine Results
  1. 1
    Features selected by optimization algorithms.

    Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

    Published 2024
    “…After the image has been pre-processed, it is segmented using the Thresholding Level set approach. Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
    Save to List
    Saved in:
  2. 2
    Table_4_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX

    Table_4_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX by Josefa Díaz-Álvarez (5572427)

    Published 2022
    Save to List
    Saved in:
  3. 3
    Table_1_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX

    Table_1_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX by Josefa Díaz-Álvarez (5572427)

    Published 2022
    Save to List
    Saved in:
  4. 4
    Table_2_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX

    Table_2_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX by Josefa Díaz-Álvarez (5572427)

    Published 2022
    Save to List
    Saved in:
  5. 5
    Table_5_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.docx

    Table_5_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.docx by Josefa Díaz-Álvarez (5572427)

    Published 2022
    Save to List
    Saved in:
  6. 6
    Table_3_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX

    Table_3_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX by Josefa Díaz-Álvarez (5572427)

    Published 2022
    Save to List
    Saved in:
  7. 7
    Data_Sheet_1_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.PDF

    Data_Sheet_1_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.PDF by Josefa Díaz-Álvarez (5572427)

    Published 2022
    Save to List
    Saved in:
  8. 8
    Data_Sheet_2_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.PDF

    Data_Sheet_2_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.PDF by Josefa Díaz-Álvarez (5572427)

    Published 2022
    Save to List
    Saved in:
  9. 9
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
    “…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
    Save to List
    Saved in:
  10. 10
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
    “…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
    Save to List
    Saved in:
  11. 11
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
    “…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
    Save to List
    Saved in:
  12. 12
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
    “…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
    Save to List
    Saved in:
  13. 13
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
    “…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
    Save to List
    Saved in:
  14. 14
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
    “…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
    Save to List
    Saved in:
  15. 15
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
    “…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
    Save to List
    Saved in:
  16. 16
    Trainable parameters for different algorithms.

    Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

    Published 2024
    Save to List
    Saved in:
  17. 17
    Total primary energy demand by fuel in China.

    Total primary energy demand by fuel in China. by Da Lian (18880882)

    Published 2024
    Save to List
    Saved in:
  18. 18
    A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS<sub>2</sub>‑Based Microparticles in a Breast Cancer Model

    A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS<sub>2</sub>‑Based Microparticles in a Breast Cancer Mode... by Maria Kanelli (5494031)

    Published 2024
    “…MoS<sub>2</sub> nanosheets exhibit high photothermal conversion efficiency and require low-power laser irradiation. A machine learning algorithm was applied to acquire the optimal laser operation conditions. …”
    Save to List
    Saved in:
  19. 19
    Primary training parameters for the model.

    Primary training parameters for the model. by Junjie Lu (160350)

    Published 2025
    Save to List
    Saved in:
  20. 20
    Proposed reinforcement learning architecture.

    Proposed reinforcement learning architecture. by Enoch Solomon (21416703)

    Published 2025
    “…Experimental evaluation were conducted employing Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) algorithms within ViZDoom and Unity reinforcement learning environments. …”
    Save to List
    Saved in:

Search Tools: