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Showing 61 - 80 results of 717 for search '(((( experimental data algorithm ) OR ( element network algorithm ))) OR ( data using algorithm ))*', query time: 0.11s Refine Results
  1. 61

    Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain by Al Sadawi, Alia

    Published 2021
    “…The primary tool used in the data supply chain is data batch processing which requires efficient scheduling. …”
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    article
  2. 62

    DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data by Assaf, Ali

    Published 2022
    “…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
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    masterThesis
  3. 63
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  5. 65

    A Parallel Neural Networks Algorithm for the Clique Partitioning Problem by Harmanani, Haidar M.

    Published 2002
    “…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and c cliques. …”
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    article
  6. 66

    A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS by Youssef, H.

    Published 2020
    “…In this paper, we present a Simulated Evolution algorithm for the design of campus network topology. …”
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    article
  7. 67

    LaScaDa: A Novel Scalable Topology for Data Center Network by Zina Chkirbene (16869987)

    Published 2020
    “…LaScaDa forwards packets between nodes using a new hierarchical row-based routing algorithm. …”
  8. 68

    Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data by Arfan Ahmed (17541309)

    Published 2023
    “…One of the key aspects of WDs with machine learning (ML) algorithms is to find specific data signatures, called Digital biomarkers, that can be used in classification or gaging the extent of the underlying condition. …”
  9. 69

    Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations by Mansour, Nashat

    Published 1992
    “…Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. …”
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    article
  10. 70
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    A Novel Steganography Technique for Digital Images Using the Least Significant Bit Substitution Method by Shahid Rahman (16904613)

    Published 2022
    “…<p>Communication has become a lot easier in this era of technology, development of high-speed computer networks, and the inexpensive uses of Internet. Therefore, data transmission has become vulnerable to and unsafe from different external attacks. …”
  12. 72
  13. 73

    SemIndex+: A semantic indexing scheme for structured, unstructured, and partly structured data by Tekli, Joe

    Published 2018
    “…This paper describes SemIndex+, a semantic-aware indexing and querying framework that allows semantic search, result selection, and result ranking of structured (relational DB-style), unstructured (IR-style), and partly structured (NoSQL) data. Various weighting functions and a parallelized search algorithm have been developed for that purpose and are presented here. …”
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  14. 74
  15. 75

    A Survey of Data Clustering Techniques by Sobeh, Salma

    Published 2023
    “…Clustering, an unsupervised learning technique, aims to identify a specific number of clusters to effectively categorize the data through data grouping. Hence, clustering is related to many fields and is used in various applications that deal with large datasets. …”
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    masterThesis
  16. 76
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  18. 78

    KNNOR: An oversampling technique for imbalanced datasets by Ashhadul Islam (16869981)

    Published 2021
    “…<p>Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. However, if the training data is not balanced among different classes, the performance of ML models deteriorate heavily. …”
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    Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

    Published 2023
    “…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”