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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 ↗5534 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
Uncertainty-aware prediction of asphalt pavement roughness using probabilistic machine learning ↗
Accurate prediction of asphalt pavement deterioration is critical for data-driven infrastructure asset management. However, conventional data-driven models commonly provide deterministic estimates, offering limited insight into prediction confidence. This limitation restricts their application in risk-aware decision-making, where understanding the reliability of each prediction is essential. This study addresses this challenge by developing an uncertainty-aware framework for predicting pavement roughness using data from the Long-Term Pavement Performance program. A curated dataset of 1,246 tes…
- OpenAlex · Published · First archived
Image of the City in the Age of Large Language Models: ChatGPT as a New Urban Observer ↗
This research examines how city images of Istanbul’s Historic Peninsula are evolving with artificial intelligence and expanding user-generated content. City image refers to a mental representation of urban structure, and imageability to the ease of forming it. Using Lynch’s five-element framework (paths, edges, districts, nodes, landmarks), the study compares a classical verbal cognitive map based on 32 interviews with built-environment academics (noting possible sample bias) with ChatGPT’s reading of 210 citizen reviews from TripAdvisor and Ekşi Sözlük. A structured, multi-step prompting work…
- OpenAlex · Published · First archived
Leveraging Artificial Intelligence to Appraise Social Media Narratives on Breast Reduction Procedures: A Snapshot Analysis ↗
Background: Social media platforms increasingly influence patient perceptions regarding reconstructive procedures.This study evaluated the characteristics, quality, engagement patterns, and user sentiment associated with breast reduction-related content on TikTok, while assessing the performance of artificial intelligence (AI) in content and sentiment analysis.Methods: A cross-sectional analysis of 140 TikTok videos on breast reduction was conducted.Engagement metrics, including likes, comments, shares, saves, and a weighted engagement index, were collected.The modified DISCERN (mDISCERN) and…
- OpenAlex · Published · First archived
Fractional two-temperature dual-phase-lag bioheat transfer under electromagnetic heating: An analytical-physics-informed neural network framework ↗
This study develops a hybrid semi-analytical and physics-informed neural-network framework for transient thermoelastic heat transfer in biological tissue subjected to electromagnetic heating. The formulation combines a fractional two-temperature dual-phase-lag bioheat model with thermoelastic coupling, blood perfusion, temperature-dependent metabolic heat generation, and an effective Maxwell–Ohm Joule-heating source. The governing equations are solved in the Laplace domain and numerically inverted to generate reference temperature, displacement, and stress fields, which are subsequently used t…
- OpenAlex · Published · First archived
An Efficient Graph Neural Network‐Based Routing Optimization Mechanism for Congestion‐Aware Software‐Defined Networks ↗
ABSTRACT Software‐Defined Networking (SDN) has developed as a suitable solution for complex networks with a flexible and programmable networking paradigm. SDN infrastructure is facing critical challenges in efficient routing under dynamic and congestion‐prone conditions. This research paper proposes an efficient Improved Graph Neural Network (IGNN)‐based routing optimization mechanism designed for achieving congestion‐aware traffic management in SDN environments. The proposed model uses the inherent graph structure of network topology by representing switches as nodes and communication links a…
- OpenAlex · Published · First archived
Enhancing network science with large language models and federated artificial intelligence ↗
Abstract Sixth generation (6G) networks require intelligent and privacy-preserving solutions to handle traffic complexity, resource allocation, and security challenges. While Large Language Models (LLMs) offer advanced reasoning and Federated Artificial Intelligence (FAI) enables distributed learning without directly sharing raw data, their combined application in large-scale network science remains limited. Key issues include non-IID data, decision latency, and limited robustness of centralized models. To address these challenges, this work introduces FALCON-6G (Federated AI and LLM-Oriented…
- OpenAlex · Published · First archived
AI ‐Powered Surveillance Systems: A Comprehensive Review of Technologies, Applications, Challenges, and Deployment Considerations ↗
ABSTRACT Artificial intelligence (AI) has rapidly transformed smart surveillance systems, significantly leveraging technologies such as deep learning, computer vision, and adaptive monitoring. Driven by the need for real‐time object detection, behavioral analysis, and automated alerting, AI‐powered surveillance is increasingly vital for enhancing public safety, operational efficiency, and decision‐making in fields such as security, health‐care, transportation, and environmental monitoring. The growing societal impact and complexity of surveillance necessitate a comprehensive examination of fou…
- OpenAlex · Published · First archived
Transforming the Landscape of Drug Innovation in AI Era: AlphaFold 3 and Beyond ↗
ABSTRACT The traditional drug discovery and development (DDD) pipeline has long been characterized by inefficiency and high costs. The rapid evolution of artificial intelligence (AI), especially AI‐based toolkits for protein structure and interaction prediction, exemplified by AlphaFold 3 (AF3), promises to reshape the landscape of drug innovation. AF3 advances structural biology by predicting protein structures and their interactions with a range of biomolecules, including nucleic acids, small‐molecule ligands, and ions, potentially changing the conventional DDD paradigm, although prediction…
- OpenAlex · Published · First archived
Bearing capacity and safety factors of ring foundations in spatially variable soils: a hybrid FELA-machine learning approach ↗
Abstract Ring foundations efficiently support axisymmetric structures like silos, storage tanks, chimneys, and cooling towers with reduced concrete volume compared to solid circular foundations. Their ultimate bearing capacity is strongly affected by asymmetric loading and spatial variability in soil properties. This study presents a hybrid machine learning framework to predict normalized bearing capacity factors and safety factors for ring foundations in spatially variable cohesive-frictional (c-φ) soils. Datasets were generated via finite element limit analysis (FELA) in OptumG2, incorporati…
- OpenAlex · Published · First archived
Enhancing parametric and causal reasoning through a formula-augmented language model approach for belt grinding knowledge assistance ↗
Abrasive belt grinding is a flexible surface finishing process in which precision machining of geometrically complex components often involves coupled parameters and contact mechanisms. Retrieval-augmented large language models (LLMs) have shown promise in domain-specific knowledge questioning. However, their capabilities in numerical computation and mechanism-oriented reasoning remain limited in scenarios involving coupled parameters. This study introduces a structural enhancement mechanism based on formula-group embedding. It integrates formula retrieval, task classification, structured prom…
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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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