In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively.

Background: The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. Objective: The aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. Methods: We built predictive models using different subsets of factors, selected according to their importance in predicting patient classification. We then evaluated each independent model and also a combination of them, leading to a better predictive model. Results: Our data mining approach identified genetic patterns that escaped detection using conventional statistics. More specifically, the partial decision trees and ensemble models increased the classification accuracy of hepatitis C virus outcome compared with conventional methods. Conclusions: Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.

Accurate prediction of student success is important for improving retention and supporting academic decision-making. This study investigates the use of Graph Neural Networks (GNNs) for modelling academic progression across 13 university faculties, focusing on how structured trajectory representations can capture temporal and relational dependencies in student records. Conventional machine learning approaches frequently rely on aggregated tabular indicators, which may obscure the temporal ordering and relational structure of academic trajectories. We propose a graph-based representation, termed the Academic Trajectory Graph (ATG), which encodes semester-to-semester course transitions and temporal relationships between enrollments. To evaluate its effectiveness, graph-based approaches (DGCNN, GCN, and node2vec-based embeddings) are compared with conventional machine learning models trained on aggregated tabular indicators. Experimental results show that aggregated academic indicators provide strong predictive performance, confirming the importance of cumulative progression signals. Among the graph-based approaches, DGCNN achieved the most consistent performance across faculties, reaching AUC values up to 0.988, accuracy up to 0.945, and F1-scores above 0.94 in several faculties. Although traditional tabular models generally achieved the strongest overall predictive performance, the ATG preserves temporal and relational information that is not captured by flat feature vectors, enabling trajectory-level analysis of academic pathways. The results indicate that graph-based trajectory modelling may provide additional structural insights in faculties exhibiting heterogeneous academic progression patterns, while offering competitive predictive performance. These findings highlight both the potential and the limitations of graph-based approaches for modelling structured educational data, suggesting that their effectiveness depends on the structural diversity of the underlying academic domain.

BACKGROUND The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. OBJECTIVE The aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. METHODS We built predictive models using different subsets of factors, selected according to their importance in predicting patient classification. We then evaluated each independent model and also a combination of them, leading to a better predictive model. RESULTS Our data mining approach identified genetic patterns that escaped detection using conventional statistics. More specifically, the partial decision trees and ensemble models increased the classification accuracy of hepatitis C virus outcome compared with conventional methods. CONCLUSIONS Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.