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improve classification » image classification (Expand Search), average classification (Expand Search), type classification (Expand Search)
based optimization » whale optimization (Expand Search)
binary help » binary health (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
mapk based » mask based (Expand Search), task based (Expand Search), app based (Expand Search)
improve classification » image classification (Expand Search), average classification (Expand Search), type classification (Expand Search)
based optimization » whale optimization (Expand Search)
binary help » binary health (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
mapk based » mask based (Expand Search), task based (Expand Search), app based (Expand Search)
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1
Related studies on IDS using deep learning.
Published 2024“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”
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2
The architecture of the BI-LSTM model.
Published 2024“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”
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Comparison of accuracy and DR on UNSW-NB15.
Published 2024“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”
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4
Comparison of DR and FPR of UNSW-NB15.
Published 2024“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”
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5
Supplementary Material 8
Published 2025“…SMOTE overcomes this issue by generating synthetic samples of the minority class (resistant isolates) through interpolation rather than simple duplication, thereby improving model generalization.</p><p dir="ltr">When applied to AMR prediction, SMOTE enhances the ability of classification models to accurately identify resistant <i>Escherichia coli</i> strains by balancing the dataset, ensuring that machine learning algorithms do not overlook rare resistance patterns. …”
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Image1_Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease.eps
Published 2022“…In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome’s composition are currently being explored. …”
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DataSheet_1_Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.pdf
Published 2022“…Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. …”