يعرض 361 - 380 نتائج من 443 نتيجة بحث عن '(( elements data algorithm ) OR ((( data models algorithm ) OR ( involves learning algorithm ))))', وقت الاستعلام: 0.11s تنقيح النتائج
  1. 361

    Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM) حسب AL SHOUBAKI, HAMZA YOUNIS

    منشور في 2024
    "…We also explore a range of algorithms, including traditional classifiers and deep learning models, to determine and select the most suitable and accurate models of identifying poets' names from the verses. …"
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  2. 362
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  4. 364

    A geographic information system method to generate long term regional solar radiation resource maps: enhancing decision-making حسب Sachin Jain (19161721)

    منشور في 2024
    "…Digital surface model data is used as an auxiliary variable to further scale down to 30 m resolution. …"
  5. 365

    Positive Unlabelled Learning to Recognize Dishes as Named Entity حسب TAREK, AIMAN

    منشور في 2019
    "…With the lack of labelled data, I try to overcome the cold start and avoid manual labelling by building a lookup table from a dictionary. …"
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  6. 366

    Digital Image Watermarking Using Balanced Multiwavelets حسب Ghouti, L.

    منشور في 2006
    "…In this paper, a robust watermarking algorithm using balanced multiwavelet transform is proposed. …"
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    article
  7. 367

    Precision nutrition: A systematic literature review حسب Daniel Kirk (17302798)

    منشور في 2021
    "…Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. …"
  8. 368

    EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach حسب Muhammad Adeel Asghar (6724982)

    منشور في 2019
    "…Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. …"
  9. 369

    Predicting COVID-19 cases using bidirectional LSTM on multivariate time series حسب Ahmed Ben Said (14158926)

    منشور في 2022
    "…Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. …"
  10. 370

    Process Mining over Unordered Event Streams حسب Awad, Ahmed

    منشور في 2020
    "…This requires online algorithms that, instead of keeping the whole history of event data, work incrementally and update analysis results upon the arrival of new events. …"
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  11. 371

    A novel few shot learning derived architecture for long-term HbA1c prediction حسب Marwa Qaraqe (10135172)

    منشور في 2024
    "…For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. …"
  12. 372

    Active distribution network type identification method of high proportion new energy power system based on source-load matching حسب Qinlin, Shi

    منشور في 2023
    "…Firstly, the typical daily output scenarios of DG are extracted by clustering method, and the generalized load curve model is solved by the optimization algorithm to obtain the source load operation data; Secondly, calculate the source-load matching indicators (including matching performance, matching degree, and matching rate) according to the source load data of each region, and identify the distribution network type according to the range of the index values; Finally, several indicators are introduced to quantify the characteristics of different types of distribution networks. …"
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    article
  13. 373

    On the complexity of multi-parameterized cluster editing حسب Abu-Khzam, Faisal

    منشور في 2017
    "…In other words, Cluster Editing can be solved efficiently when the number of false positives/negatives per single data element is expected to be small compared to the minimum cluster size. …"
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    article
  14. 374

    An Infrastructure-Assisted Crowdsensing Approach for On-Demand Traffic Condition Estimation حسب Abdul Rahman, Sawsan

    منشور في 2019
    "…Our approach combines the strengths of mobile crowdsensing, with the support of the mobile infrastructure, a multi-criteria algorithm for the participants' selection, and a deductive rule-based model for traffic condition estimation. …"
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    article
  15. 375

    Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology حسب Ajay Vikram Singh (204056)

    منشور في 2023
    "…As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. …"
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    Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review حسب Daksh Dave (17949239)

    منشور في 2025
    "…Although AI models have shown promising improvements in sensitivity and specificity, challenges such as algorithmic bias, interpretability, and the generalizability of models across diverse populations remain. …"
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    A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis حسب Alaa Abd-Alrazaq (17430900)

    منشور في 2021
    "…Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.…"
  20. 380

    Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose حسب Atiq Ur Rehman (8843024)

    منشور في 2020
    "…These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. …"