Coronary Artery Disease (CAD) remains one of the leading causes of mortality worldwide and accounts for a great number of prolonged hospital stays, which increase the cost of healthcare and its resource consumption. This research presents Graph Neuro Boost, a new method of predicting hospital LOS for CAD patients based on hybrid feature selection and classification, and characterized by high accuracy of prediction. Using the MIMIC-III dataset, the method involves complete data preparation, analysis, and preprocessing. A Graph Neuro Feature Selector was built by combining the autoencoder-based embedded method and the filter method using graph theory and Kruskal’s algorithm. The method efficiently identified 16 critical features in just 2.68 seconds, surpassing traditional hybrid feature selection approaches. Those features were then optimized using Bayesian optimization together with XGBoost, implementing a balanced Expected Improvement and Upper Confidence Bound technique to reduce log loss. Graph Neuro Boost resulted in an accuracy of 98.57%. In addition to superior accuracy, Graph Neuro Boost demonstrated outstanding performance relative to baseline models on multiple evaluation metrics. The SHAP analysis was also employed to interpret feature contributions, improving model transparency and supporting clinical decision-making, patient management, and efficient healthcare resource allocation.
As technology advances, analyzing Global Positioning System (GPS) data is critical to improving location accuracy in product distribution. In this study, the geolocation accuracy of a distribution company in Istanbul was evaluated using 4,784,003 GPS records and prediction models, including machine learning, deep learning, ensemble and hybrid approaches. The models were tested with both one-dimensional (1D) and four-dimensional (4D) inputs to assess differences in performance. The eXtreme Gradient Boosting (XGB) model achieved the highest accuracy, and its performance improved significantly with SpatioTemporal (ST) feature augmentation. Novel attention-based models, including spatial attention and ST transformer, were proposed to better capture spatial and temporal dependencies. Federated Learning (FL) was integrated into the ST transformer to enable decentralised training and improve privacy. To test the generalisation of the model, the experiments were repeated with the PortugalGPS dataset. The results show that ST features significantly improve positioning accuracy and that machine learning models—especially XGB—outperform deep learning models on urban GPS prediction tasks. This study provides a scalable, privacy-preserving framework for improving geolocation accuracy in different cities and conditions. It also contributes to a better understanding of urban logistics by highlighting the role of GPS data in optimising location accuracy during distribution.
This study introduces a computational framework for analyzing the tonal structure and interactional dynamics of political discussion programs broadcast on Turkish television. Using a self-constructed dataset of televised debates featuring journalists, politicians, and commentators, we trained a CNN on MFCCs to classify speech segments into three tonal categories: sociable, discuss, and monotone. The model achieved strong performance, with an average classification accuracy of 92.3% across five fold cross-validation. To assess the role of individual speakers, we modeled participant influence using Ridge Regression with a CLR transformation, estimating how each participant contributed to the overall tonal composition of episodes. In addition, the study introduces several structural analyses of tonal behavior, including tonal trajectory analysis to observe how tone evolves throughout episodes and segment-to-segment transition modeling to capture sequential tone shifts. Furthermore, entropy-based diversity metrics are employed to assess tonal balance and variety across episodes. Together, these components offer a multi-dimensional view of televised political discourse, revealing both who shapes the tone and how tonal dynamics unfold over time. The findings contribute to computational media studies, political communication, and the analysis of broadcast interaction in polarized environments.
Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) decode neural intent but remain vulnerable to non-stationarity, low signal-to-noise ratios, complex spatiotemporal dynamics, and inter-trial variability. Existing deterministic deep models often fail to capture global temporal dependencies while preserving local spatial topology, limiting robustness and generalization. We propose the Variational Spatiotemporal Enhanced Transformer (VSTE-Transformer), an encoder–decoder framework for robust EEG recognition. It embeds variational inference into a Transformer, representing latent features as distributions to quantify uncertainty and reduce sensitivity to noise. A Spatiotemporal Enhancement Module combines localized graph attention, which models dynamic electrode correlations, with multi-scale dilated convolutions that capture rhythmic temporal contexts. A dual-task objective jointly optimizes classification and signal reconstruction, encouraging the latent manifold to preserve neurophysiologically meaningful structure. Experiments on BCI Competition IV-2a and OpenBMI under a unified within-subject, cross-session protocol show that VSTE-Transformer significantly outperforms state-of-the-art methods and remains more robust under severe noise corruption.
Modern cybersecurity infrastructure management is facing the challenge of an explosion in the number of machines and Key Performance Indicators(KPIs). As a fundamental data-mining technique, clustering can not only group data with similar characteristics but also provide informative insights that are critical for cybersecurity operations. In particular, the clustering of large-scale KPIs can significantly assist cybersecurity engineers in detecting system failures, reducing redundant alarms, and decreasing troubleshooting overhead-contributing to more effective threat detection and alert management. However, KPIs are reasonably long (that is, high dimensionality) because of the continuous collection of the monitoring system. For KPIs’ high dimensionality, existing KPIs clustering methods always have a trade-off between clustering quality and computational complexity. In this paper, we propose a novel method, SubCluster, which is a fast, accurate, and scalable approach for clustering large-scale KPIs with high quality. SubCluster aims to extract representative subsequences from numerous KPIs. In contrast to existing subsequence extraction-based clustering methods that compress the candidate set, SubCluster first conducts coarse-grained clustering with the nearest neighbor-based graph to obtain near-accurate groups from raw KPIs. SubCluster then attempts to extract representative subsequences for fine-grained clustering. Extensive experimental results on public time-series and large-scale KPIs datasets from the real world demonstrate the effectiveness and efficiency of SubCluster.
