Showing 81 - 100 results of 103 for search '(( elements ices algorithm ) OR ((( image processing algorithm ) OR ( neural coding algorithm ))))', query time: 0.12s Refine Results
  1. 81

    Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur (712349)

    Published 2024
    “…It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. …”
  2. 82

    Developing a framework for using face recognition in transit payment transactions by HABEH, ORABI MOHAMMAD ABDULLAH

    Published 2021
    “…The argued face recognition accuracy between 98%-99.2% and average processing time including metro gate opening time ranges between 1114-1400 milliseconds. …”
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  3. 83

    Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben Romdhane, Haifa

    Published 2023
    “…An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. …”
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  4. 84

    Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system by Ali Farhat (1461478)

    Published 2018
    “…</p><h2>Other Information</h2> <p> Published in: EURASIP Journal on Image and Video Processing<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1186/s13640-018-0298-2" target="_blank">http://dx.doi.org/10.1186/s13640-018-0298-2</a></p>…”
  5. 85

    A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass by Uzma Nawaz (21980708)

    Published 2025
    “…Deep learning approaches have made significant progress in digital image processing, particularly in object recognition and classification, and are among the most popular computer vision tools. …”
  6. 86

    Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben-Romdhane, Haïfa

    Published 2023
    “…An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. …”
    Get full text
    article
  7. 87
  8. 88

    Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

    Published 2024
    “…For tabular data, we also present the Auto-Inflater neural network, utilizing an exponential loss function for Autoencoders. …”
  9. 89
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  11. 91

    DASSI: differential architecture search for splice identification from DNA sequences by Shabir Moosa (14153316)

    Published 2022
    “…The demand for robust algorithms over the recent years has brought huge success in the field of Deep Learning (DL) in solving many difficult tasks in image, speech and natural language processing by automating the manual process of architecture design. …”
  12. 92

    A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education by Abu Zitar, Raed

    Published 2022
    “…Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. …”
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  13. 93

    An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control by Mahmoud Dahmani (18810247)

    Published 2020
    “…This paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input is images of the user’s eye that are processed to estimate the gaze direction and the wheelchair was moved accordingly. …”
  14. 94

    Developing an online hate classifier for multiple social media platforms by Joni Salminen (7434770)

    Published 2020
    “…We then experiment with several classification algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). …”
  15. 95

    Exploring new horizons in neuroscience disease detection through innovative visual signal analysis by Nisreen Said Amer (17984077)

    Published 2024
    “…To address this, our study focuses on visualizing complex EEG signals in a format easily understandable by medical professionals and deep learning algorithms. We propose a novel time–frequency (TF) transform called the Forward–Backward Fourier transform (FBFT) and utilize convolutional neural networks (CNNs) to extract meaningful features from TF images and classify brain disorders. …”
  16. 96

    From Collatz Conjecture to chaos and hash function by Masrat Rasool (17807813)

    Published 2023
    “…The effectiveness and dependability of the proposed hash function are evaluated by comparing it with two well-known hash algorithms, namely SHA-3 and SHA-2, as well as several other Chaos-based hash algorithms. …”
  17. 97

    Single-channel speech denoising by masking the colored spectrograms by Sania Gul (18272227)

    Published 2025
    “…<p>Speech denoising (SD) covers the algorithms that remove the background noise from the target speech and thus improve its quality and intelligibility. …”
  18. 98
  19. 99

    Secure and Anonymous Communications Over Delay Tolerant Networks by Spiridon Bakiras (16896408)

    Published 2020
    “…Instead, our work introduces a novel message forwarding algorithm that delivers messages, from source to destination, via a random walk process. …”
  20. 100

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

    Published 2021
    “…We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). …”