بدائل البحث:
processing detection » protein detection (توسيع البحث), phishing detection (توسيع البحث)
warm optimization » swarm optimization (توسيع البحث), art optimization (توسيع البحث), whale optimization (توسيع البحث)
data processing » image processing (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
binary base » binary mask (توسيع البحث), ciliary base (توسيع البحث), binary image (توسيع البحث)
base warm » based arm (توسيع البحث), bare ward (توسيع البحث)
processing detection » protein detection (توسيع البحث), phishing detection (توسيع البحث)
warm optimization » swarm optimization (توسيع البحث), art optimization (توسيع البحث), whale optimization (توسيع البحث)
data processing » image processing (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
binary base » binary mask (توسيع البحث), ciliary base (توسيع البحث), binary image (توسيع البحث)
base warm » based arm (توسيع البحث), bare ward (توسيع البحث)
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Joint Detection of Change Points in Multichannel Single-Molecule Measurements
منشور في 2021الموضوعات: -
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Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things
منشور في 2025"…In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …"
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Algoritmo de detección de odio en español (Algorithm for detection of hate speech in Spanish)
منشور في 2024"…</li></ul><h2>Training Process</h2><h3>Pre-workout</h3><ul><li>Batch size: 16</li><li>Epochs: 5</li><li>Learning rate: 2e-5 with 10% warmup steps</li><li>Early stopping with patience=2</li></ul><h3>Fine-tuning</h3><ul><li>Batch size: 128</li><li>Epochs: 5</li><li>Learning rate: 2e-5 with 10% warmup steps</li><li>Early stopping with patience=2</li><li>Custom metrics:</li><li>Recall for non-hate class</li><li>Precision for hate class</li><li>F1-score (weighted)</li><li>AUC-PR</li><li>Recall at precision=0.9 (non-hate)</li><li>Precision at recall=0.9 (hate)</li></ul><h2>Evaluation Metrics</h2><p dir="ltr">The model is evaluated using:</p><ul><li>Macro recall, precision, and F1-score</li><li>One-vs-Rest AUC</li><li>Accuracy</li><li>Metrics by class</li><li>Confusion matrix</li></ul><h2>Requirements</h2><p dir="ltr">The following Python packages are required (see requirements.txt for the full list):</p><ul><li>TensorFlow</li><li>Transformers</li><li>scikit-learn</li><li>pandas</li><li>datasets</li><li>matplotlib</li><li>seaborn</li></ul><h2>Usage</h2><p dir="ltr">The model expects input data with the following specifications:</p><ol><li><b>Data Format</b>:</li></ol><ul><li>CSV file or Pandas DataFrame</li><li>Mandatory column name: <code>text</code> (type string)</li><li>Optional column name: <code>label</code> (type integer, 0 or 1) if available for evaluation</li></ul><ol><li><b>Text Preprocessing</b>:</li></ol><ul><li>Text will be automatically converted to lowercase during processing</li><li>Maximum length: 128 tokens (longer texts will be truncated)</li><li>Special characters, URLs, and emojis must remain in the text (the tokenizer handles these)</li></ul><ol><li><b>Label Encoding</b>:</li></ol><ul><li><code>0</code> = No hateful content (including neutral/positive content)</li><li>1 = Hate speech</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at:Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …"
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Data set constituents.
منشور في 2023"…The high performance of the algorithm at both centers shows that the calibration process is efficient. …"
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Results of the model on test sets 1 and 2.
منشور في 2023"…The high performance of the algorithm at both centers shows that the calibration process is efficient. …"
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Scanners and staining methods.
منشور في 2023"…The high performance of the algorithm at both centers shows that the calibration process is efficient. …"
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GSE96058 information.
منشور في 2024"…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …"
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The performance of classifiers.
منشور في 2024"…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …"
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Related studies on IDS using deep learning.
منشور في 2024"…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …"
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The architecture of the BI-LSTM model.
منشور في 2024"…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …"
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Comparison of accuracy and DR on UNSW-NB15.
منشور في 2024"…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …"
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Comparison of DR and FPR of UNSW-NB15.
منشور في 2024"…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …"