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Showing 41 - 60 results of 87 for search '(( elements mean algorithm ) OR ((( data encoding algorithm ) OR ( data groups algorithm ))))', query time: 0.11s Refine Results
  1. 41

    Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes by Muhammad Mohsin Khan (22150360)

    Published 2025
    “…Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. …”
  2. 42

    The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

    Published 2021
    “…Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. …”
  3. 43

    Competitive learning/reflected residual vector quantization for coding angiogram images by Mourn, W.A.H.

    Published 2003
    “…Medical images need to be compressed for the purpose of storage/transmission of a large volume of medical data. Reflected residual vector quantization (RRVQ) has emerged recently as one of the computationally cheap compression algorithms. …”
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    article
  4. 44

    Multidimensional Gains for Stochastic Approximation by Saab, Samer S.

    Published 2019
    “…The proposed algorithms here aim for per-iteration minimization of the mean square estimate error. …”
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    article
  5. 45

    Design and analysis of entropy-constrained reflected residual vector quantization by Mousa, W.A.H.

    Published 2002
    “…Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. …”
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    article
  6. 46

    An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System by Alkhatib, Osama

    Published 2019
    “…Despite having different performance results on predicting failures, most of the models produced close outcomes. Meaning no “perfect” machine learning algorithm that will produce good results at particular problem, in fact for each type of problem a specific algorithm is suited and might achieves good outcome, while another algorithm fails heavily. …”
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  7. 47

    Efficient Seismic Volume Compression using the Lifting Scheme by Khene, M. F.

    Published 2000
    “…In addition, the lifting scheme offers: 1) a dramatic reduction of the required auxiliary memory, 2) an efficient combination with parallel rendering algorithms to perform arbitrary surface and volume rendering for interactive visualization, and 3) an easy integration in the parallel I/O seismic data loading routines. …”
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    article
  8. 48

    Using machine learning for disease detection. (c2013) by Jreij, Georges Antoun

    Published 2016
    “…Classification consists of predicting group membership for new data instances by learning from pre-classified data instances. …”
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    masterThesis
  9. 49

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

    Published 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. …”
  10. 50

    Impact Of Multidisciplinary Maternal Resuscitation Training Program on Improving the Front-Line Care Provider’s Readiness to Manage Maternal Cardiac Arrest: A Pre-test/Post-test St... by Mohamed Elsayed Saad Aboudonya (18466385)

    Published 2024
    “…The sample size consisted of three groups of front-line multidisciplinary obstetric maternal resuscitation teams (physicians, midwives, and nurses) divided into a pre-test group (N=30) and post-test group (N=30). …”
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    Structural similarity evaluation between XML documents and DTDs by Tekli, J.

    Published 2007
    “…We consider the various DTD operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes. Our approach is based on the concept of tree edit distance, as an effective and efficient means for comparing tree structures, XML documents and DTDs being modeled as ordered labeled trees. …”
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    conferenceObject
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    Impact of birth weight to placental weight ratio and other perinatal risk factors on left ventricular dimensions in newborns: a prospective cohort analysis by Ashraf Gad (17040114)

    Published 2024
    “…Chi-squared and one-way analysis of variance were used to compare BW/PW groups and the best regression model was selected using a genetic and backward stepwise algorithm.…”
  16. 56

    A Geometric-Primitives-Based Compression Scheme for Testing Systems-on-a-Chip by El-Maleh, Aiman H.

    Published 2001
    “…In this paper, it is assumed that an embedded core will be used to execute the decompression algorithm and decompress the test data.…”
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    article
  17. 57

    A geometric-primitives-based compression scheme for testingsystems-on-a-chip by El-Maleh, A.

    Published 2001
    “…In this paper, it is assumed that an embedded core will be used to execute the decompression algorithm and decompress the test data…”
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    article
  18. 58

    STEM: spatial speech separation using twin-delayed DDPG reinforcement learning and expectation maximization by Muhammad Salman Khan (7202543)

    Published 2025
    “…For stationary sources, the proposed system gives satisfactory performance in terms of quality, intelligibility, and separation speed, and generalizes well with the test data from a mismatched speech corpus. Its perceptual evaluation of speech quality (PESQ) score is 0.55 points better than a self-supervised learning (SSL) model and almost equivalent to the diffusion models at computational cost and training data which is many folds lesser than required by these algorithms. …”
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    Soft Sensor for NOx Emission using Dynamical Neural Network by Shakil, M.

    Published 2020
    “…Neural network model is trained using real data logs of an industrial boiler. Principal Component Analysis (PCA) is used to reduce number of input variables. …”
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    article