February 2026

Cardiovascular disease is the primary cause of death globally, necessitating early identification, precise risk classification, and dependable decision-support technologies. The advent of large language models (LLMs) provides new zero-shot and few-shot reasoning capabilities, even though machine learning (ML) algorithms, especially ensemble approaches like Random Forest, XGBoost, LightGBM, and CatBoost, are excellent at modeling complex, non-linear patient data and routinely beat logistic regression. This research predicts cardiovascular disease using a merged dataset of 1,190 patient records, comparing traditional machine […]

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Machine unlearning, which enables a model to forget specific data, is crucial for ensuring data privacy and model reliability. However, its effectiveness can be severely undermined in real-world scenarios where models learn unintended biases from spurious correlations within the data. This paper investigates the unique challenges of unlearning from such biased models. We identify a novel phenomenon we term “shortcut unlearning," where models exhibit an “easy to learn, yet hard to forget" tendency. Specifically, models struggle to forget […]

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Accurately predicting short-term traffic demand is critical for intelligent transportation systems. While deep learning models achieve strong performance under stationary conditions, their accuracy often degrades significantly when faced with distribution shifts caused by external events or evolving urban dynamics. Frequent model retraining to adapt to such changes incurs prohibitive computational costs, especially for large-scale or foundation models. To address this challenge, we propose FORESEE (Forecasting Online with Residual Smoothing and Ensemble Experts), a lightweight online adaptation framework that […]

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Telecom networks may soon begin adjusting themselves in real time, as operators test systems that allow AI agents to manage traffic and service quality. AI may soon be making operational decisions. This week, Nokia and AWS presented a new network slicing system that uses AI agents to monitor network conditions and adjust resources automatically. The setup is being tested by telecom operators du in the United Arab Emirates and Orange in Europe and Africa, according to a joint […]

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Industrial pouring processes operate under highly dynamic conditions where small deviations can lead to defects, scrap, and production losses. Although modern foundries are equipped with multiple sensors and visual inspection systems, most monitoring approaches remain fragmented, unimodal, and difficult to interpret. Furthermore, annotated anomalous samples in industrial settings are scarce, hindering the development of traditional methods. As a result, many critical pouring anomalies are detected too late or lack sufficient contextual information for effective decision making. In this […]

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In this paper, we investigate the algebraic structures of homomorphisms and derivations, which play a significant role in the fields of physics and engineering sciences. The main focus of this study is on the perturbation of homomorphism and derivation structures associated with certain bidimensional Additive-Quadratic functional equation in normed triple Lie (3-Lie) systems, within the concept of Ulam-stability. By employing the fixed point theorem, we establish several required theorems, followed by specific and relevant corollaries that highlight the […]

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In industrial environments, robust Temporal Action Localization (TAL) is essential; however, frequent occlusions often compromise the reliability of skeletal data, leading to negative transfer in multimodal fusion. To address this challenge, we propose a Gated Skeleton Refinement Module (Gated SRM) that explicitly incorporates Open-Pose confidence scores into the network architecture. By applying these scores as a logarithmic bias within a self-attention mechanism, our method achieves soft suppression—dynamically attenuating the attention weights assigned to unreliable joints—before adaptively fusing the […]

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Cross-silo federated learning allows multiple organizations to collaboratively train machine learning models without sharing raw data, but client updates can still leak sensitive information through inference attacks. Secure aggregation protects privacy by hiding individual updates, yet it complicates contribution evaluation, which is critical for fair rewards and detecting low-quality or malicious participants. Existing marginal-contribution methods, such as the Shapley value, are incompatible with secure aggregation, and practical alternatives, such as Leave-One-Out, are crude and rely on self-evaluation. We […]

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Vehicular Ad Hoc Networks (VANETs) are essential to intelligent transportation systems (ITS), enabling secure, real-time communication among vehicles and infrastructure. However, their decentralized and dynamic nature makes them vulnerable to threats such as Sybil attacks, message forgery, replay attacks, and Denial-of-Service (DoS). This paper presents VANETGuard, a lightweight scalable trust management system that enhances security and scalability in 5G-enabled smart vehicular networks. The proposed system integrates entropy-based anomaly detection, Bayesian inference for adaptive trust scoring, and a lightweight […]

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State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about their interpretability and robustness. Here, we build on a recently proposed image reconstruction method, which is based on embedding data-driven information into a model-based convolutional dictionary regularization via neural network-inferred spatially adaptive sparsity level maps. By means of improved network design and dedicated training strategies, we extend the method to achieve filter-permutation invariance as well as the possibility to change the […]

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