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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5969 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Evaluating the diagnostic accuracy of artificial intelligence-based systems for malaria detection in Africa: a systematic review and meta-analysis protocol ↗
Malaria remains a major public health challenge in Africa, with 249 million cases and 608,000 deaths reported globally in 2022, predominantly affecting sub-Saharan Africa. While microscopy is the diagnostic gold standard, it is labour-intensive and expertise-dependent. Artificial intelligence (AI)-based systems, particularly convolutional neural networks, offer promising alternatives for automated malaria detection with potential advantages in accuracy, efficiency, and scalability. This systematic review protocol outlines methods to evaluate the diagnostic accuracy of AI-based systems compared…
- OpenAlex · Published · First archived
Artificial intelligence for stability assessment and early-warning in converter-dominated power systems: A unified review and risk-envelope framework ↗
Converter-dominated power systems with high penetration of inverter-based resources exhibit fast, nonlinear, and multi-timescale dynamics that introduce new stability modes and operational risks beyond the scope of traditional assessment techniques. In recent years, artificial intelligence and machine learning methods have been widely applied to small-signal, transient, voltage, and frequency stability prediction using simulation data and wide-area measurements. Although many studies report high predictive accuracy, the literature remains fragmented across stability categories, data regimes, a…
- OpenAlex · Published · First archived
Electrical impedance tomography beyond the icu: wearable architectures and AI under translational constraints ↗
Electrical impedance tomography (EIT) enables real-time functional imaging using non-ionizing currents and compact, low-cost hardware, but its clinical footprint remains largely confined to ICU lung monitoring. Advances in wearable architectures and artificial intelligence (AI) are driving EIT toward point-of-care applications, yet translation remains limited by technical and validation barriers. This review synthesizes progress in hardware, image reconstruction, and AI-enabled analysis, and outlines a roadmap for advancing EIT toward scalable multi-organ imaging.
- OpenAlex · Published · First archived
Assessing workplace relocation impacts on travel time reliability: an activity-based modeling framework with machine learning-based trip duration prediction ↗
Rapid and often unplanned urban growth in African secondary cities is placing increasing pressure on transport systems, resulting not only in congestion but also in unstable and unreliable travel times. In cities such as Bahir Dar, Ethiopia, strong spatial concentration of employment and services produces directional travel demand and uneven network performance. However, African urban transport studies rarely combine behaviorally detailed simulation with data-driven predictive analysis to evaluate land-use restructuring strategies. This study combines Activity-Based Modeling (ABM) and Machine…
- OpenAlex · Published · First archived
Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation ↗
In tightly cooperative manipulation tasks, robotic manipulators must follow collision‐free and coordinated trajectories. Existing multiagent learning frameworks often rely on centralized planners that provide strong coordination but fail to scale with larger teams. Alternatively, decentralized approaches offer better scalability but typically lack communication, which limits their ability to achieve highly cooperative behaviors. To address this gap, this paper proposes a graph neural network (GNN)‐based framework for scalable multiagent reinforcement learning (RL). In our formulation, each man…
- OpenAlex · Published · First archived
Quantifying the plausibility gap in generative AI for surgical video generation with expert assessment ↗
Generative artificial intelligence (AI) shows promise for physical simulation, yet its clinical application remains critically unexplored. We present SurgVeo, a pioneering expert-guided benchmark paired with the Surgical Plausibility Pyramid framework. Evaluating the Veo-3 and Wan2.2 models reveals a consistent plausibility gap: despite high visual fidelity, the majority of errors are surgical logic failures rather than visual quality deficits. By exposing this reliance on visual synthesis, this study provides a roadmap for developing surgical world models.
- OpenAlex · Published · First archived
Evaluating Large‐Language Models in Bioinformatics Applications ↗
ABSTRACT Large language models (LLMs) have significantly revolutionized natural language processing through their strong capabilities in text generation and reasoning. Yet, their applicability to bioinformatics applications remains largely unexplored. Here, we systematically evaluate state‐of‐the‐art LLMs across six representative task domains: drug–drug interaction prediction, antimicrobial and anticancer peptide identification, molecular optimization, gene and protein named entity recognition, single‐cell type annotation, and bioinformatics question answering. Our results show that general‐p…
- OpenAlex · Published · First archived
Kinetic and thermodynamic study of faba bean hulls pyrolysis with machine learning-based bio-oil modeling ↗
Abstract The growing global demand for sustainable, low-carbon energy has intensified research into biomass pyrolysis as a promising route for biofuel production. This study provides a comprehensive investigation of the kinetic and thermodynamic behavior of faba bean hulls, an underutilized agricultural byproduct, to evaluate their potential for bio-oil generation. Thermogravimetric analysis (TGA) was conducted at heating rates of 10, 15, and 20 °C/min to determine the kinetic triplet and thermodynamic parameters. A multi-model iso-conversional approach, including Friedman (FR), Flynn–Wall–Oza…
- OpenAlex · Published · First archived
Development and validation of a parsimonious AI-based mortality risk score for heart failure ↗
Abstract Risk stratification in heart failure (HF) supports clinical decisions, yet existing tools face adoption barriers: conventional scores show modest discrimination and depend on specialised tests (i.e., echocardiography), while artificial intelligence (AI) models require rich longitudinal data and infrastructure. Both approaches face challenges in workflow integration, interpretability, and demonstrating clinical utility. Using 373,389 adults with HF from the UK Clinical Practice Research Datalink (CPRD) Aurum, we developed and validated an AI-based risk model for all-cause mortality and…
- OpenAlex · Published · First archived
A review of deep learning transformer variants used in solar PV forecasting ↗
Abstract Solar power has become one of the most important renewable energy sources as it is sustainable, its prices are reducing, and it could contribute to the transition to clean energy across the world. Nevertheless, the inherently intermittent nature of solar PV output due to the rapidly fluctuating weather conditions demands a very serious need to have accurate forecasting systems. The current reviews are primarily over traditional deep learning or statistical models, creating a knowledge gap on how transformer-based architectures intend to separate long-term dependencies and nonlinear be…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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