Showing 461 - 480 results of 720 for search '(((( implement finding algorithm ) OR ( relevant maya algorithm ))) OR ( data using algorithm ))', query time: 0.12s Refine Results
  1. 461

    Security in wire/wireless networks: sniffing attacks prevention/detection techniques in LAN networks & the effect on biometric technology by Al-Hemairy, Moh'd Hussain

    Published 2010
    “…Accordingly security threats and data banks attacks turn out to be a phenomenon. Thus, granting protection to such crucial information becomes a high demand. …”
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  2. 462

    High-order parametrization of the hypergeometric-Meijer approximants by Abouzeid M. Shalaby (16329062)

    Published 2023
    “…To solve this problem, we formulate an equivalent (order by order) linear set of equations which is easy to solve in an appropriate time using normal PCs. We also show that such extension of the hypergeometric resummation algorithm is able to employ non-perturbative information like strong-coupling and large-order asymptotic data which are always used to accelerate the convergence. …”
  3. 463

    Recovery of business intelligence systems by Haraty, Ramzi A.

    Published 2018
    “…The efficiency of the data recovery algorithm is substantial for e-healthcare systems. …”
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    article
  4. 464
  5. 465

    A forward-backward Kalman for the estimation of time-variant channels in OFDM by Al-Naffouri, T.Y.

    Published 2005
    “…In this paper, we propose an expectation-maximization (EM) algorithm for joint channel and data recovery. The algorithm makes use of the rich structure of the underlying communication problem-a structure induced by the data and channel constraints. …”
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    article
  6. 466

    Shuffled Linear Regression with Erroneous Observations by Saab, Samer S.

    Published 2019
    “…Existing methods are either applicable only to data with limited observation errors, work only for partially shuffled data, sensitive to initialization, and/or work only with small dimensions. …”
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    conferenceObject
  7. 467

    Benchmarking Concept Drift Detectors for Online Machine Learning by Mahgoub, Mahmoud

    Published 2022
    “…The main task is to detect changes in data distribution that might cause changes in the decision bound aries for a classification algorithm. …”
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  8. 468

    Clustering Tweets to Discover Trending Topics about دبي (Dubai) by ALYALYALI, SALAMA KHAMIS SALEM KHAMIS

    Published 2018
    “…Then, creating a word vector to the tweets by using TF-IDF methodology. After this, log results into k- mean clustering algorithm with cosine similarity to measure similarity between objects of each cluster. …”
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  9. 469

    A smart decentralized identifiable distributed ledger technology‐based blockchain (DIDLT‐BC) model for cloud‐IoT security by Shitharth Selvarajan (14157976)

    Published 2024
    “…The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. …”
  10. 470

    An EM-Based Forward-Backward Kalman Filter for the Estimation of Time-Variant Channels in OFDM by Al-Naffouri, T.Y.

    Published 0000
    “…The algorithm makes a collective use of the data and channel constraints inherent in the communication problem. …”
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    article
  11. 471

    Edge intelligence for network intrusion prevention in IoT ecosystem by Mansura, Habiba

    Published 2023
    “…This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. …”
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    article
  12. 472
  13. 473

    A new estimator and approach for estimating the subpopulation parameters by Mohammad Salehi M. (21259490)

    Published 2021
    “…The criterion shows that the traditional total subpopulation estimator for unknown subpopulation size will be more efficient if the subpopulation mean is close to zero. Using an innovative procedure, we develop a new estimator, and we study its properties using real data. …”
  14. 474

    Deep Reinforcement Learning for Resource Constrained HLS Scheduling by Makhoul, Rim

    Published 2022
    “…The two main steps in HLS are: operations scheduling and data-path allocation. In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. …”
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    masterThesis
  15. 475

    Starvation Problem in CPU Scheduling for Multimedia Systems by Salah, Khaled

    Published 2002
    “…Multimedia applications have timing requirements that cannot generally be satisfied using the time-sharing algorithms of general-purpose operating systems. …”
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    article
  16. 476

    Edge intelligence for network intrusion prevention in IoT ecosystem by Mansura Habiba (17808302)

    Published 2023
    “…This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. …”
  17. 477

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

    Published 2019
    “…I work with Yelp dataset, going through each text review, using each noun as a candidate, label the positive samples using the aforementioned lookup table, then using Positive Unlabelled learning techniques to recognise more entities within the unlabelled data, by predicting the probability for each candidate. …”
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  18. 478

    NEURAL NETWORK MODEL FOR PLANNED REPLACEMENT OF BOEING 737 BRAKES by Al-Garni, Ahmed Z.

    Published 2020
    “…Three years of data are used for model building and validation. …”
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    article
  19. 479

    A novel hybrid methodology for fault diagnosis of wind energy conversion systems by Khaled Dhibi (16891524)

    Published 2023
    “…Therefore, a hybrid feature selection based diagnosis technique, that can preserve the advantages of wrapper and filter algorithms as well as RF model, is proposed. In the first phase, the neighborhood component analysis (NCA) filter algorithm is used to reduce and select only the pertinent features from the original raw data. …”
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