Hyperparameter Optimization of Non-linear Machine Learning Models Using Bi-level Data-Driven Optimization.

Tracheostomy weaning in patients with severe acquired brain injury: External validation of machine learning models.

To forecast DON concentrations in marginal seas of China by machine learning models.

Machine learning models for predicting readmission after stroke: A systematic review and meta-analysis.

This study aimed to develop and internally validate machine-learning (ML) models that exploit routine electronic medical record (EMR) data to identify recent SI in Chinese in-patients with MDD. A retrospective cohort of 721 in-patients with major depressive disorder (MDD), including 399 with suicidal ideation (SI-positive), was recruited from the Fourth People's Hospital of Hefei between January 2020 and August 2023. The dataset was stratified into training (70%) and test (30%) sets. All preprocessing steps (median imputation and Z-score normalization) and Boruta feature selection were performed exclusively on the training set using R software (version 4.4.2), with multicollinearity removed for variables with a variance inflation factor (VIF)>5 or a pairwise Pearson correlation coefficient |r|>0.75. Six machine learning algorithms-random forest (RF), logistic regression (LR), LightGBM, support vector machine (SVM), K-nearest neighbor (KNN), and XGBoost-were trained using GridSearchCV combined with 10-fold stratified cross-validation, with model fine-tuning via the class_weight='balanced' parameter. Model performance was evaluated on the independent test set using multiple metrics, including the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (PR-AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHAP analysis, implemented in Python (version 3.12), was used to enhance model interpretability. Stratified subgroup analysis (stratified by sex and age) and sensitivity analysis (comparing the optimal RF model with the traditional LR baseline model via DeLong's test) were conducted to verify the robustness and superiority of the proposed model. Random Forest achieved the best discrimination (AUC 0.857) and maintained stable discrimination across sexes (AUC=0.843 in females and 0.822 in males), demonstrating higher sensitivity in females (0.883) and higher specificity in males (0.900).Top risk features: Compared with SI-negative patients, SI-positive patients were predominantly male (74.4% vs. 53.7%), married (80.7% vs. 63.4%), and had a lower educational level (83.7% vs. 29.5% without higher education). Furthermore, both their current age and age at depression onset were significantly greater (all P<0.001). ML models, especially Random Forest, can effectively identify recent SI risk in Chinese MDD patients using readily available clinical data.

Predicting CT-based coronal plane knee phenotype parameters using imageless navigation and machine learning.

Machine Learning Models Used to Predict Abdominal Aortic Aneurysm Growth and Rupture: A Systematic Review and Critical Appraisal.

Development of a highly accurate machine learning model for the differential diagnosis of ameloblastoma and odontogenic keratocyst on computed tomography images.

Multi-scale cotton yield prediction using machine learning and Sentinel-2 imagery

Optimizing adjuvant therapy selection with machine learning after transoral surgery in HPV-related oropharyngeal cancer.

Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

Developing risk-decision rules for aeration in wastewater treatment plants using copula-enhanced machine learning with feature smoothing.

Development and internal validation of an explainable machine learning model for predicting textbook outcome after free flap reconstruction in oral cancer.

Adversarial attacks present significant risks to machine learning (ML) systems, exploiting model vulnerabilities and threatening the integrity, security, and trustworthiness of applications across multiple sectors. This paper provides a comprehensive review of adversarial attack types—white box, black box, and other type of attacks—and examines tailored attacks and defense mechanisms across domains such as Internet of Things (IoT), healthcare, industrial control systems, autonomous vehicles, speech recognition, natural language processing (NLP), finance, and Large Language Models (LLMs). Each domain introduces unique adversarial challenges and demands specific countermeasures, from anomaly detection to adversarial training and robust model architectures. By systematically categorizing both attack methodologies and defense strategies, this survey offers a holistic understanding of adversarial dynamics across fields, highlighting critical areas for further research and the development of resilient, cross-domain ML defenses. • Comprehensive Analysis: Reviews adversarial attacks across multiple data types and their impact on machine learning models. • Taxonomy Development: Proposes a structured taxonomy of adversarial attacks aligned with the MITRE ATLAS framework. • Vulnerability Identification: Identifies vulnerabilities across data modalities exploited by adversarial attacks. • Attack Categorization: Categorizes adversarial attack techniques across different data types and ML systems. • Domain-Specific Taxonomy: Examines adversarial attacks across domains including IoT, healthcare, NLP, speech, and LLMs.

Machine learning-based prediction of antibiotic resistance gene distribution in agricultural soils under different climate change scenarios.

Multimodal fusion of speech and PHQ-9 for machine learning-based adolescent depression screening.

Copper stress responses in Scenedesmus obliquus-Bacillus subtilis Consortia: Machine Learning-Based prediction of copper removal.

A multimodal fNIRS-based machine learning model for symptom assessment and treatment response prediction in schizophrenia.

Hybrid physics-informed machine learning and nanobiosensing strategies for precision liver cancer diagnostics.

Prediction of biomass and nutritional quality of tropical pastures using multispectral analysis and machine learning models