ABSTRACT With the rapid growth of artificial intelligence, machine learning has emerged as a useful tool in the construction industry for optimizing materials and predicting concrete properties. This study explores the use of steel fiber and recycled demolition waste (DW) to improve concrete’s mechanical properties. The main objective is to predict the compressive strength (CS) of DW-modified fiber-reinforced concrete (FRC) by assessing the effects of cement, RHA, fiber content, natural and demolished waste aggregates, water, and superplasticizer dosages. Seven mix designs with varying fiber and DW levels were experimentally tested. Machine learning methods, including adaptive boosting (ADB), extreme gradient boosting (XGB), random forest (RF), and stacking models (XGB-ADB, XGB-RF), were applied to analyze these variables’ impact on CS. A dataset of 405 points was compiled from literature via a systematic review. The hybrid XGB-RF and XGB models showed the best performance with R2 values of 0.849 and 0.845, respectively. SHAP analysis identified cement, water, and superplasticizer as key factors affecting CS. Experimental validation supported the modeling results and the development of a graphical user interface. The novelty lies in integrating hybrid ML, explainable analysis, and experimental validation to predict the CS of the modified concrete and support mix design through a user-friendly GUI.

Classification of DNA secondary structures by combining multiple spectral techniques with machine learning.

Machine learning-based prediction of three-year mortality in elderly inpatients with coronary artery disease combined with heart failure.

Screening for depressive symptoms in primary and secondary school students based on speech features: A one-year longitudinal study from Jiangsu, China.

Symptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods

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GUARD: Enabling fair gaming through the gameplay analysis using machine learning methods and expert knowledge

Identifying the optimal predictors for adolescent mental and physical health using machine learning methods.

A machine learning model for predicting complete virological response in chronic hepatitis B patients receiving first-line nucleos(t)ide analogues therapy.

Mechanistic insights into marine-derived PDE6D inhibitors disrupting prenyl-binding to modulate leukemia-associated RAS trafficking.

Machine learning for the prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

Comprehensive review of heart disease prediction: A comparative study from 2019 onwards.

Subcutaneous tissue structural feature identification using unsupervised machine learning.

State of charge estimation for lithium-ion batteries integrating machine learning and filter methods

Hybrid prediction framework for total nitrogen in freshwater lake watershed based on the northern goshawk optimization and multiple machine learning methods: An integrated strategy from data cleaning to model optimization

Comparative analysis of machine learning methods for correcting uranium loading measurement errors in pebble-bed HTGR spherical fuel elements

Performance evaluation of machine learning methods in determining sea level trends based on tide gauge and satellite altimetry data: A case study of Australian coasts

Machine learning-based prediction and quantification of OCD surgery and pedigree effects on racehorse performance.

Supervised machine learning methods for short-term prediction of a sudden cardiac death from electrocardiogram

BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition characterized by inattention, impulsivity, and hyperactivity. Traditional diagnosis relies on clinical evaluation, which is time-consuming and subjective. Electroencephalography (EEG) signals provide an objective alternative, and machine learning methods can improve their diagnostic utility. AIM To develop an explainable EEG-based model for ADHD detection by integrating a novel combination ternary pattern (CTP) feature extractor with twin wavelet transform (TWT) for multilevel signal analysis, and to evaluate its effectiveness in providing accurate, channel-wise, and fusion-based classification results for objective and rapid ADHD diagnosis. METHODS A new EEG dataset containing more than 7000 segments from 137 ADHD patients and 150 controls was studied. A novel feature engineering framework was developed, combining a new CTP extractor with statistical features. A multilevel feature extraction structure was designed using a newly proposed TWT for signal decomposition. Extracted features were reduced to the most informative 263 using neighborhood component analysis. Channel-wise classification was performed with k-nearest neighbors, followed by iterative majority voting across 20 EEG channels. RESULTS Single-channel analysis achieved up to 99.12% accuracy. By applying majority voting, overall classification accuracy increased to 99.97%, with similarly high sensitivity and specificity. CONCLUSION Our study introduces a large ADHD EEG dataset and a novel model integrating TWT and CTP. The model provides highly accurate, channel-wise, and fusion-based results, offering a promising objective tool for rapid ADHD diagnosis.