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Equatorial plasma bubbles (EPBs) represent areas of reduced electron density that develop within Earth’s ionosphere as a result of Rayleigh–Taylor instability. These formations can trigger signal scintillation, which induces signal degradation and navigation positioning inaccuracies. EPBs can be identified via imagery gathered by All-Sky Imager (ASI) networks. The present research introduces an inexpensive, automated technique for identifying EPBs within ASI datasets, suitable for real-time monitoring as well as categorization tasks. This approach couples Two-Dimensional Principal Component Analysis (2DPCA) and Recursive Feature Elimination (RFE) alongside a Random Forest machine learning classifier to construct an Explainable Artificial Intelligence (XAI) framework. This framework extracts image characteristics to automate EPB detection using minimal dimensionality, yielding a compact and exceptionally rapid-to-train model designed to discern EPBs in recorded ASI frames. A collection of 2458 images, categorized into "Event" and "Empty" classes, was assembled to construct the database. This image repository was partitioned randomly into two distinct portions: an 80% training dataset and a 20% testing dataset. The developed XAI framework achieved a marginally better detection accuracy relative to the conventional 2DPCA approach while maintaining a notably smaller structural footprint. Additionally, the execution of the proposed framework was assessed and contrasted against alternative deep learning baselines (ResNet18, Inception-V3, VGG16, and VGG19) under identical environmental conditions.

The escalating complexity of malicious digital activities highlights the necessity for dependable and transparent intrusion detection systems (IDS) to preserve network integrity. Although machine learning frameworks like Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (K-NN), and XGBoost demonstrate exceptional capabilities in identifying malicious behavior, their inner mechanics grow increasingly opaque as structural complexity and predictive precision expand, creating challenges for high-stakes safety operations. Local Interpretable Model-agnostic Explanations (LIME) is frequently deployed to mitigate this issue; nevertheless, its dependency on Gaussian distributions when generating data variations often fails to capture the non-linear and disproportionate traits of frameworks like CIC-IDS-2018. To overcome these limitations, we introduce an adapted LIME variation strategy utilizing Weibull, Gamma, Beta, and Pareto distributions to more accurately represent the attributes of network telemetry. Our framework enhances the consistency of diverse machine learning systems optimized on CIC-IDS repositories, yielding more constructive and dependable rationales for predictive outputs. These proposed algorithmic adjustments yield a gain in explanatory fidelity of up to 78% relative to the standard normal distribution framework. Variations rooted in the Pareto distribution deliver the most optimal outcomes. Across all evaluated distributions, the Pareto framework consistently generated the peak explanation fidelity and consistency, notably for K-NN (R^2 = 0.9971, S = 0.9907) and DT (R^2 = 0.9267, S = 0.9797). This demonstrates that heavy-tailed distributions align effectively with actual network operational trends, minimizing the discrepancy in attribute significance justifications and rendering them more resilient.

This research investigates and refines the MINDWALC node classification algorithm, specifically targeting its capacity to extract human-interpretable decision trees from knowledge graph (KG) architectures. We propose techniques to tailor MINDWALC for environments where the knowledge graph is bifurcated into internal background knowledge (domain-specific data) and instance knowledge (specific case data). While the core logic of MINDWALC—utilizing discriminative graph walks as features—is preserved, we introduce two primary enhancements. First, we implement relation-tail merging to empower the algorithm to process relation-modified nodes. Second, we propose flexible walking depths that, when combined with the original MINDWALC strategy, improve the detection of commonalities between node instances. Our findings suggest that these flexible walks can markedly increase classification accuracy in hierarchical or incomplete tree-like graphs, though performance varies on more heterogeneous KG structures. Ultimately, this work demonstrates that our refinements significantly boost MINDWALC’s utility in tree-structured environments, allowing the system to use background knowledge to bridge gaps in instance data in a transparent way. Initial evaluations on medical toy datasets suggest these optimizations could refine clinical decision-making, particularly in sectors requiring explainable artificial intelligence.

Among the most critical and fatal medical conditions are brain malignancies, making early identification vital for enhancing survival rates. Currently, magnetic resonance imaging (MRI) stands as the premier diagnostic tool for early detection because of its exceptional capability to capture soft tissue details. Nevertheless, relying solely on human interpretation of brain MRI scans introduces risks of oversight, which are frequently exacerbated by the fatigue and varying expertise of radiologists. To mitigate these limitations, computer-aided diagnosis (CAD) systems have taken on an increasingly vital role. Leveraging state-of-the-art computer vision methodologies like deep learning, these systems generate highly precise diagnoses from clinical imagery, thereby boosting both precision and consistency in clinical settings. This study introduces an innovative CAD architecture designed for multi-class classification of brain malignancies. The system evaluates six pre-trained deep learning networks as its foundational architecture, integrating robust data preprocessing and augmentation protocols to optimize computational throughput. To counter the opaque "black box" nature of deep learning architectures and improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to illuminate the network's deductive steps when identifying anomalies in MRI data. Furthermore, an accessible, web-based Brain Tumor Detection System was constructed using the Streamlit framework, highlighting its utility for clinical environments and offering meaningful support to medical professionals. All experimental simulations were carried out using an open benchmark repository, proving that the introduced system delivers state-of-the-art diagnostic capabilities, achieving accuracy levels near 99% with the ResNet-50, Xception, and InceptionV3 architectures.

Global emergency departments and urgent care facilities regularly experience prolonged waiting periods that surpass patient expectations, primarily driven by unexpected spikes in patient volumes. The resulting delays caused by insufficient staffing during these high-demand intervals have been associated with detrimental clinical consequences. Prior investigations into predicting patient volumes have predominantly relied on traditional statistical approaches. Furthermore, these efforts have mainly focused on short-range predictions, which offer minimal utility for the long-term scheduling of medical staff. This paper contributes to a growing domain of research aimed at investigating the capacity of machine learning frameworks to produce precise patient volume projections. Utilizing a decade's worth of data from two major urgent care centers, this study constructs long-range patient flow predictions extending up to one quarter in advance by evaluating a selection of modern, high-performing algorithms. A unique aspect of this research is the integration of eXplainable Artificial Intelligence (XAI) frameworks, specifically LIME and Shapley, to conduct a comprehensive assessment of internal model dynamics that are typically obscured. These diagnostic instruments allowed us to examine how effectively the predictive frameworks adjusted to volatile demand shifts induced by COVID-19 pandemic lockdowns, while also determining the most significant predictor variables to gain deep operational insights. The empirical findings indicate that an innovative ensemble combining gradient boosting techniques with advanced univariate frameworks like Prophet yielded the most precise and dependable performance overall. On average, this methodology produced a 16%–30% improvement over the current internal forecasting techniques utilized for predicting daily patient volumes 90 days into the future.