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Showing 21 - 40 results of 847 for search '(((( data using algorithm ) OR ( based machine algorithm ))) OR ( element network algorithm ))', query time: 0.15s Refine Results
  1. 21

    A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method by Amit Kumar Balyan (18288964)

    Published 2022
    “…To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”
  2. 22

    Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems by Amal Hichri (21383672)

    Published 2022
    “…The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. …”
  3. 23

    Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network by Mohammad Reza Chalak Qazani (13893261)

    Published 2024
    “…Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. …”
  4. 24

    Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms by Md Ferdous Wahid (13485799)

    Published 2022
    “…The values are 18.44 % and 23.9 % for CV-RMSE, 11.6 % and 10.06 % for MAPE, and 7.5 % and 6.75 % for MdAPE, using ANN and GP, respectively. While the previous studies mostly used ANN to demonstrate the capability of MLs to predict PG over the mechanistic or correlation-based models, the present research has shown that GP is even better than ANN using a wide range of FPs and a large data set.…”
  5. 25

    Optimizing Document Classification: Unleashing the Power of Genetic Algorithms by Ghulam Mustafa (458105)

    Published 2023
    “…Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. …”
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    TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection by Zina Chkirbene (16869987)

    Published 2020
    “…TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. …”
  8. 28

    QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning by Md Nazmul Islam Shuzan (21842426)

    Published 2024
    “…We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. …”
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    Machine Learning Approach for the Design of an Assessment Outcomes Recommendation System by Abu Zitar, Raed

    Published 2021
    “…Our research processes and workflows supported by AI utilize machine learning technology in order to interpret big data, analyze broad data sets and recognize associations with more reliably. …”
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  11. 31

    Allocation and re-allocation of data in a grid using an adaptive genetic algorithm by Mansour, N.

    Published 2006
    “…Allocation and re-allocation of data in a grid using an adaptive genetic algorithm. …”
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  12. 32

    Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

    Published 2021
    “…Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. …”
  13. 33

    FPGA-Based Network Traffic Classification Using Machine Learning by Elnawawy, Mohammed

    Published 2020
    “…The proposed design achieves an average throughput of 163.24 Gbps, exceeding throughputs of reported hardware-based classifiers that use comparable approaches, which in turn ensures the continuity of realtime traffic classification at congested data centers.…”
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    Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark by Ameema Zainab (16864263)

    Published 2021
    “…The paper proposes a concurrent job scheduling algorithm in a multi-energy data source environment using Apache Spark. …”
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    Efficient Approximate Conformance Checking Using Trie Data Structures by Awad, Ahmed

    Published 2021
    “…By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. …”
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  18. 38

    Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch (17983846)

    Published 2025
    “…Additionally, an online classifier is developed for streaming data, combining online PCA with a kernel-based recursive classifier using a stochastic approximation algorithm. …”
  19. 39

    Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment by Zakaria Tolba (16904718)

    Published 2022
    “…This paper provides a deep learning-based decryptor for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing ciphertext pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plaintext from the ciphered text without any P-box expertise. …”
  20. 40

    Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm by Abu Zitar, Raed

    Published 2022
    “…Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. …”
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