The article proposes a hybrid cascaded neuro-fuzzy system for classifying defects in renewable energy facilities based on multisensor data. The system architecture implements a sequence of computational levels (from sensor acquisition and preprocessing to result integration) and combines a compact feature representation with adaptive decision-making logic. The formation of a compact feature space is carried out using a modified convolutional neural network, after which the initial classification is performed using the hypersector FLVQ method. In cases of increased uncertainty, the expert modules Fuzzy BSB and the modified Wang–Mendel method are activated, which ensures robustness and explainability of the results. Experimental studies have shown that the proposed system provides consistently high Accuracy values (over 94%), balanced Precision and Recall indicators for all defect classes. Analysis of the discrepancy matrix showed that the main errors occur between the “erosion” and “corrosion” classes, which is explained by the similarity of their textural characteristics. The results obtained confirm the effectiveness of the cascade architecture and its feasibility for practical application in automated monitoring systems for renewable energy facilities.
This study presents a method for detecting ship coating quality using a portable Vis/NIR spectroscopy system combined with machine learning. To improve accuracy, we integrated spectral transformation (Nippy), feature selection methods (PSO and GA), and ensemble learning models. The experiments involved four coating quality levels, producing 148 spectral samples. Results show that the proposed approach consistently outperforms single baseline models and traditional feature selection methods such as PCA and IFS. The best performance was achieved by combining Nippy with PSO, where the LDA algorithm reached 99.33% accuracy, while GA also showed strong results with both single and ensemble models. We also examined ensemble results at different stages of preprocessing and feature selection, showing that the ensemble maintained stable performance throughout the process. These findings demonstrate that the integration of spectral transformation and metaheuristic feature selection can enhance model robustness, providing more reliable and accurate coating quality detection for maritime applications.
The algorithm used in the feature selection process is Hybrid Binary Grey Wolf Optimizer-Binary Sine Cosine Algorithm (HBGWO-BSCA). The dataset used for the HBGWO-BSCA is Parkinson's. This dataset is used because Parkinson's disease is one of the most frequently discussed diseases throughout the world. The aim of this paper is to find out what features are used in the process of predicting Parkinson's disease. The HBGWO-BSC feature selection algorithm was proven to be able to increase the accuracy value of the KNN classification algorithm by 92%, while the HGWO-SCA only obtains an accuracy of 88%. The value of the HBGWO-BSCA is higher than that of HGWO-SCA because the relationship between features selected with the HBGWO BSCA is more accurate than that of the Hybrid Grey Wolf Optimizer-Sine Cosine Algorithm (HGWO-SCA). This proves that the HBGWO-BSC feature selection algorithm obtains the highest accuracy, precision, recall and F1-Score values compared to the HGWO-SCA. The HBGWO-BSCA in feature selection uses parameters N and alpha with parameter value ranges N= 9-11 and alpha=2-8. HBGWO-BSCA is an algorithm used for the feature selection process in datasets that already have labels or classes.
The goal of the paper is to create a mathematical model for optimizing the composition of an IT project team, which would take into account the need to maximize the technical skills of candidates, their ability to perform the necessary roles, considering a set of assessments of these factors, as well as the agreeableness and conscientiousness of its members. The model should take into account the limitations on team members' competencies, their working hours, and labor costs. An approach to solving the problem is proposed, which involves presenting the requirements for candidates' competencies and assessing their competencies and abilities to perform specific roles in the project using fuzzy sets. At the same time, assessments of competencies and abilities to perform certain roles are represented by trapezoidal fuzzy intervals. A method is proposed for defuzzifying the problem by calculating the value of the membership function of the requirement at a point equal to the lower modal value of the fuzzy interval describing the properties of the candidates. The proposed task may not have a solution if the candidates' competencies do not meet the constraints. The model allows finding the optimal extension of the set of feasible solutions by training candidates. An example of solving the task of forming an IT team is considered.
In today's competitive market, effective customer segmentation is essential for businesses to refine marketing strategies, optimize resources, and enhance customer satisfaction. This study introduces the Weather-Responsive Segmentation (WRS) framework by integrating autonomous K-means clustering with Principal Component Analysis (PCA) while incorporating real-time meteorological data. Our approach achieves 27.8% higher segmentation accuracy (Silhouette Score: 0.524 vs 0.410) and 23.4% improved campaign conversion rates compared to traditional demographic-only methods. Traditional segmentation relies primarily on demographic and behavioral data, but our method adds a new dimension by considering weather-related factors that significantly impact consumer behavior. Using K-means, we identified four distinct customer segments and applied PCA for data visualization and dimensionality reduction. The inclusion of meteorological variables enhances segmentation accuracy and relevance, providing businesses with more actionable insights. By incorporating external environmental factors, this approach offers a deeper understanding of customer groups and enables more precise, data-driven marketing strategies. This research contributes to the field by demonstrating the added value of weather-based segmentation in consumer analysis, offering businesses a novel perspective to optimize marketing efforts and improve decision-making.
