Algorithms

Accurately assessing a patient’s likelihood of developing cardiovascular conditions is essential for proper case classification and for ensuring timely, targeted medical intervention. To address this need, the present study employs a carefully optimized machine learning framework to predict such risks within cardiology settings. A hybrid architecture is proposed that combines convolutional neural networks (CNNs) with cutting-edge gradient boosting classifiers, namely CatBoost and LightGBM, whose performance is further enhanced by metaheuristic optimization. The system adopts a two-layer design capable of capturing complex data structures while supporting accurate classification of cardiac patients and their risk of developing cardiovascular disease. Extensive evaluation on real-world data confirms the framework’s effectiveness for binary classification, with the best models reaching an accuracy of slightly over 92%. To complement predictive performance, explainable AI methods were applied to clarify model decisions, yielding practical insights that can guide future data collection strategies and improve diagnostic precision.

Three-dimensional geological modeling is a fundamental technology for reconstructing subsurface geological structures and plays an important role in resource exploration, disaster prediction, and engineering construction. With increasing energy demand and growing environmental safety challenges, accurate characterization of the morphology and physical properties of subsurface strata has become essential for the efficient development of underground space. Machine learning-based three-dimensional geological modeling methods using borehole data reformulate the modeling process as a stratum classification task, thereby reducing manual intervention and improving the level of automation in geological modeling. In this process, the classification of stratigraphic spatial points is a key step, as its accuracy directly influences the quality of the resulting geological body model. However, traditional algorithms typically rely solely on spatial coordinate features to determine stratum affiliation. Such a single-feature-driven approach has limited capability in representing the true morphology of subsurface strata. To address this limitation, this paper proposes a stratum classification method based on Vertical Alignment–Horizontal Distance Weighting (VA-HDW), which is designed to capture spatial correlations between strata and boreholes. On this basis, a specialized neural network model, termed the Generalized Borehole Autoregressive Neural Network (GBARNN), is designed and trained to improve the classification performance of stratigraphic spatial points, thereby contributing to improved three-dimensional geological body modeling quality.

In this study, the evaluation and ranking of competencies in traditional and agile project management were examined using a structured Multi-Criteria Decision-Making (MCDM) algorithm. To determine the most important competency group, a direct assessment method by experts was employed. The Analytic Hierarchy Process method extended with triangular fuzzy sets (FAHP) was used to determine the criteria weights applied for ranking the specific competencies within the most important groups. For ranking competencies within these key groups, the Technique for Order Preference by Similarity to Ideal Solution method extended with triangular fuzzy sets (FTOPSIS) was applied. The same algorithmic procedure was carried out for both traditional and agile project management approaches, in a case study conducted across four companies in the automotive industry. The study showed that, in traditional project management, the most important competency group is related to organizational and managerial skills and competencies. On the other hand, in agile project management, the most important competency group refers to contextual skills and competencies. Furthermore, within the traditional approach, the most significant specific competency is project goal orientation, while in the agile approach, the most significant specific competency is customer and stakeholder orientation.