بدائل البحث:
modelling optimization » model optimization (توسيع البحث), routing optimization (توسيع البحث), competing optimization (توسيع البحث)
codon optimization » wolf optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
binary game » binary image (توسيع البحث)
modelling optimization » model optimization (توسيع البحث), routing optimization (توسيع البحث), competing optimization (توسيع البحث)
codon optimization » wolf optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
binary game » binary image (توسيع البحث)
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141
Supplementary file 1_Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning.docx
منشور في 2025"…Objective<p>This study utilizes real-world data from primary membranous nephropathy (PMN) patients to preliminarily develop a venous thromboembolism (VTE) risk prediction model with machine learning. …"
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142
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Inconsistency concept for a triad (2, 5, 3).
منشور في 2025"…The proposed regeneration method emulates three primary phases of a biological process: identifying the most damaged areas (by identifying inconsistencies in the pairwise comparison matrix), cell proliferation (filling in missing data), and stabilization (optimization of global consistency). …"
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146
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Proposed method approach.
منشور في 2024"…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …"
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148
Descriptive statistics.
منشور في 2024"…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …"
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149
ResNeXt101 training and results.
منشور في 2024"…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …"
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150
Architecture of ConvNet.
منشور في 2024"…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …"
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151
Comparison of state-of-the-art method.
منشور في 2024"…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …"
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152
Proposed ResNeXt101 operational flow.
منشور في 2024"…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …"
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153
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Internal architecture of the SPAM-XAI model.
منشور في 2024"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"
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SPAM-XAI compared with previous models.
منشور في 2024"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"
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Image 2_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png
منشور في 2024"…</p>Conclusions<p>This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.…"
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159
Image 1_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png
منشور في 2024"…</p>Conclusions<p>This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.…"
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160
Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx
منشور في 2022"…An Affymetrix HTA2.0 array and qRT-PCR were applied to screen new specific lncRNA markers for TB in individual nucleated cells from host peripheral blood. A ML algorithm was established to combine the patients’ EHR information and lncRNA data via logistic regression models and nomogram visualization to differentiate PTB from suspected patients of the selection cohort.…"