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Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer
Published 2024-12-01Subjects: Get full text
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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study
Published 2025-01-01“…After feature selection, machine learning models were trained. Finally, we further analyzed the relationship between the hippocampal functional radiomic features and clinical measures, to explore the clinical significance of these features.Results: The combined radiomic features model logistic regression algorithm superior performance in distinguishing cognitively impaired patients from LBLP (AUC = 0.970, accuracy = 92.3%, sensitivity = 92.3%, specificity = 92.3%) compared to the other models. …”
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Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis
Published 2024-03-01Subjects: Get full text
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Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity
Published 2025-01-01Subjects: “…machine learning…”
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Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometerZenodo
Published 2025-02-01“…All scanning was performed on dried and sieved (<2 mm) soil samples. Machine learning predictive models were developed for soil organic carbon (SOC), pH, bulk density (BD), carbonate (CaCO3), exchangeable potassium (Ex. …”
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FArSS: Fast and Efficient Semantic Question Similarity in Arabic
Published 2025-01-01Subjects: Get full text
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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches
Published 2025-02-01“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. …”
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Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning
Published 2022-06-01“…Objective To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning.Design and setting This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019.Participants A total of 475 older men aged 73 years from the VASCOL dataset.Measures Self-perceived health was measured using the first item of the Short Form 12. …”
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Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis
Published 2025-01-01“…Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy.Methods PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. …”
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