Search alternatives:
quantification » identification (Expand Search)
quantitative » qualitative (Expand Search)
Showing 1 - 18 results of 18 for search 'learning methods ((quantization OR quantification) OR quantitative) network', query time: 0.10s Refine Results
  1. 1
  2. 2
  3. 3
  4. 4
  5. 5

    A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures by Bo Lyu (16522643)

    Published 2024
    “…<p dir="ltr">In recent years, there has been a significant advancement in memristor-based neural networks, positioning them as a pivotal processing-in-memory deployment architecture for a wide array of deep learning applications. …”
  6. 6
  7. 7
  8. 8

    Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting by Panja, Madhurima

    Published 2023
    “…While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. …”
    Get full text
  9. 9
  10. 10

    Artificial neural networks for predicting the performance of novice CAD users based on their profiled technical attributes by Ammouri, A.H.

    Published 2017
    “…This paper utilizes Artificial Neural Networks (ANN) to forecast the mechanical CAD performance of novice trainees involved in formal training. …”
    Get full text
    Get full text
    Get full text
    Get full text
    conferenceObject
  11. 11

    COVID-19 infection localization and severity grading from chest X-ray images by Anas M. Tahir (16870077)

    Published 2021
    “…An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). …”
  12. 12

    Hydrogen Sulfide (H<sub>2</sub>S) Sensor: A Concept of Physical Versus Virtual Sensing by Ahmed Alsarraj (16876014)

    Published 2021
    “…The merits of the proposed system are as follows: 1) a virtual sensing concept is combined with a physical sensing platform to enhance the proposed model’s estimation power in quantifying H<sub>2</sub>S in air samples; 2) a new feature extraction method based on fractional derivatives is proposed to further enhance the model’s learning capabilities; 3) an array of four gas sensors is fabricated in the in-house foundry to record and analyze the signature of H<sub>2</sub>S at various concentration levels; 4) a shallow neural network (NN) model is trained and tested on the recorded data, and based on the NN’s input–output relation, a mathematical model is presented for the quantification of H<sub>2</sub>S; and 5) the proposed model is a highly sensitive and reliable H<sub>2</sub>S gas sensing scheme with the ability to detect the gas instantaneously. …”
  13. 13
  14. 14

    Use of mobile devices in mathematics education: A case of higher education in the United Arab Emirates by DANI, ANITA

    Published 2017
    “…Cross sectional survey method was applied for collecting quantitative data. …”
    Get full text
  15. 15

    Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma by Rawan AlSaad (14159019)

    Published 2019
    “…In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). …”
  16. 16

    Cyber-risk Mitigation Strategies and the Role of Artificial Intelligence in Mitigating Cyber-Risks: The Case of the United Arab Emirates Transport Sector by ALYAMMAHI, ALI SAEED ALI AHMED

    Published 2025
    “…The research employed a quantitative method, a survey questionnaire, which was completed by 286 participants. …”
    Get full text
  17. 17
  18. 18

    An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges by Tekli, Joe

    Published 2016
    “…Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.…”
    Get full text
    Get full text
    Get full text
    Get full text
    article