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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Z. Kucukakcali

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4 papers indexed here

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Open access Sep 2026

Tree-Based Classification of COVID-19 Using NanoString Whole-Blood Immune-Response Profiles: Comparison of Full-Dataset and LOOCV-Embedded Feature Selection

Background: Whole-blood transcriptomic profiling can capture systemic immune-response alterations associated with COVID-19 and may support host-response-based classification. However, evidence regarding the discriminatory value of targeted immune-gene panels remains limited, and in small, high-dimensional datasets, the...

Z. Yilmaz, Z. Kucukakcali, Sami Akbulut · 0 citations
Open access 2026

Beyond Binary Classification: A Comprehensive Survival Analysis and Calibration Assessment of the Heart Failure Clinical Records Datase

Background: The Heart Failure Clinical Records dataset (n = 299) is among the most extensively reused benchmark datasets in clinical machine learning, yet the overwhelming majority of published analyses treat patient mortality as a static binary classification target and rarely test the statistical assumptions underlyi...

I. Cicek, Z. Kucukakcali · 0 citations
Open access Sep 2026

Tree-Based Machine Learning for Diagnostic Classification of Dengue Fever Using Routine Hematological Parameters: A Secondary Analysis of a Publicly Available Dataset

Tree-based machine-learning models for dengue classification showed moderate discrimination, with high sensitivity but limited specificity, and yielded numerically higher AUROCs and lower Brier scores than the primary SMOTE-trained LR within this internal-validation framework.

Z. Yilmaz, Z. Kucukakcali, Sami Akbulut · 0 citations
Open access 2026

An Interpretable XGBoost Model for Diabetes Prediction: Nested Cross-Validation, Calibration, and SHAP Analysis

Under a leakage-controlled, unbiased evaluation, XGBoost provided moderate but trustworthy discrimination together with well-calibrated probabilities for diabetes prediction, while SHAP confirmed clinically plausible predictors.

Z. Kucukakcali, I. Cicek · 0 citations

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