Search Results - binary ing learning with errors

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  1. 1

    Machine learning approaches to analyze histological images of tissues from radical prostatectomies by Gertych, Arkadiusz, Ing, Nathan, Ma, Zhaoxuan, Fuchs, Thomas J., Salman, Sadri, Mohanty, Sambit, Bhele, Sanica, Velásquez-Vacca, Adriana, Amin, Mahul B., Knudsen, Beatrice S.

    ISSN: 0895-6111, 1879-0771, 1879-0771
    Published: United States Elsevier Ltd 01.12.2015
    Published in Computerized medical imaging and graphics (01.12.2015)
    “…•Machine learning approaches were applied to separate stroma from epithelium in prostate tissue images…”
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    Journal Article
  2. 2

    Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing by Murugesan, Subramaniam Subramanian, Velu, Sasidharan, Golec, Muhammed, Wu, Huaming, Gill, Sukhpal Singh

    ISSN: 2168-2194, 2168-2208, 2168-2208
    Published: United States IEEE 01.09.2024
    “…) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove…”
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    Journal Article
  3. 3

    A Hybrid Transformer-LLM Pipeline for Function Name Recovery in Stripped Binaries by Petrache, Remus, Lemnaru, Camelia

    Published: IEEE 04.08.2025
    “…Recovering meaningful function names from stripped executables is a difficult challenge in reverse engineer- ing and security analysis…”
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    Conference Proceeding
  4. 4

    Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images by Ukwuoma, Chiagoziem C, Cai, Dongsheng, Heyat, Md Belal Bin, Bamisile, Olusola, Adun, Humphrey, Al-Huda, Zaid, Al-Antari, Mugahed A

    ISSN: 2213-1248, 1319-1578, 2213-1248
    Published: Saudi Arabia Springer 01.07.2023
    “… Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error…”
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    Journal Article
  5. 5

    A Novel Method for Diagnosing Alzheimer’s Disease from MRI Scans using the ResNet50 Feature Extractor and the SVM Classifier by Islam, Farhana, Rahman, Md. Habibur, -, Nurjahan, Hossain, Md. Selim, Ahmed, Samsuddin

    ISSN: 2158-107X, 2156-5570
    Published: West Yorkshire Science and Information (SAI) Organization Limited 2023
    “… Since there is a scarcity of experienced neurologists, manual diagnosis of AD is very time-consuming and error-prone…”
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    Journal Article
  6. 6

    Early warning system for coffee rust disease based on error correcting output codes: a proposal by Corrales, David Camilo, Peña Q, Andrés J, León, Carlos, Figueroa, Apolinar, Corrales, Juan Carlos

    ISSN: 1692-3324, 2248-4094
    Published: Universidad de Medellín 01.12.2014
    “… Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Machines (SVM…”
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    Journal Article
  7. 7

    A new technique for cataract eye disease diagnosis in deep learning by Mahmood, Salwa Shakir, Chaabouni, Sihem, Fakhfakh, Ahmed

    ISSN: 2303-4521, 2303-4521
    Published: 31.12.2023
    “…Automated diagnosis of eye diseases using fundus images is challenging because manual analysis is time-consuming, prone to errors, and complicated…”
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    Journal Article
  8. 8

    Combining a Forward Supervised Filter Learning Approach and Sparse Nmf for Breast Cancer Histopathological Image Classification by Karuppasamy, ArunaDevi

    ISBN: 9798311973793
    Published: ProQuest Dissertations & Theses 01.01.2022
    “… Hence, there is a need for automat ing the histopathological image analysis process. Early machine learning methods for histopathological image processing rely…”
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    Dissertation
  9. 9

    3d Lung Nodule Classification In Computed Tomography Images by Carvalho, Ana Rita Felgueiras

    ISBN: 9798383293256
    Published: ProQuest Dissertations & Theses 01.01.2019
    “…Lung cancer is the leading cause of cancer death worldwide. Usually, the diagnosis is made by physicians reading computed tomographies, CTs a task prone to errors…”
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    Dissertation