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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 ↗5237 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
Secure Enterprise Deployment of Generative AI through LLMOps Governance and Hybrid Cloud Infrastructure ↗
The rapid adoption of generative artificial intelligence (AI) across enterprise environments has created new opportunities forautomation, knowledge management, intelligent decision-making, and customer service. However, deploying large languagemodels (LLMs) in enterprise settings introduces security, privacy, governance, and operational challenges associated with sensitivedata exposure, unauthorized model access, prompt injection, model supply-chain vulnerabilities, and inconsistent regulatorycompliance. Hybrid cloud infrastructure offers a flexible deployment architecture that combines privat…
- OpenAlex · Published · First archived
Design and Implementation of an Edge AI-Based Smart IoT System for Real-Time Environmental Monitoring and Intelligent Decision Making ↗
Environmental monitoring supports public health, industrial safety, agriculture, and sustainable development.Conventional IoT monitoring systems often transmit sensor readings to remote servers for analysis, creating dependence on network availability and potentially delaying alerts.This paper proposes an Edge Artificial Intelligence (Edge AI)-based smart Internet of Things system for real-time environmental monitoring and intelligent decision-making.The architecture integrates environmental sensors, an embedded edge controller, a lightweight machine learning model, wireless communication, and…
- OpenAlex · Published · First archived
Clinical validation of a morphometric artificial intelligence system for detecting osteoporotic vertebral compression fractures on plain radiographs ↗
Osteoporotic vertebral compression fractures (OVCFs) are a major cause of morbidity and mortality in aging populations, and plain radiography—the most available modality for initial assessment—is operator-dependent. This study evaluated the diagnostic performance and clinical utility of a deep learning-based morphometric artificial intelligence (AI) model for detecting OVCFs and quantifying vertebral compression rates on plain radiographs. A total of 100 patients (780 vertebral bodies; 685 normal, 95 fractured) with suspected vertebral compression fractures were retrospectively analyzed. All p…
- OpenAlex · Published · First archived
Machine Learning‐Assisted Optimization for Dynamic Supply Network Design in the Additive Manufacturing Platform ↗
ABSTRACT Distributed manufacturing enabled by additive manufacturing (AM) allows supply chains to rapidly adapt to changing market conditions through flexible facility relocation and capacity adjustment. However, existing studies largely overlook the logistical aspects of AM‐based supply chains and typically treat facility location and logistics decisions separately. This study addresses this gap by developing a novel AM supply network design model that simultaneously considers the dynamic relocation of AM facilities, the adjustment of production capacities, and delivery routing decisions over…
- OpenAlex · Published · First archived
A Fast Denoising Dataset-Generation Method for Ground Penetrating Radar Based on a Transformer Network Constrained by an Underground Clutter Physical Model ↗
Ground-penetrating radar (GPR) denoising requires large paired datasets that preserve weak target echoes while representing realistic underground clutter, but field measurements are difficult to label, and high-fidelity electromagnetic simulations are costly to scale. This paper proposes a fast denoising dataset-generation method based on a transformer network constrained by an underground clutter physical model. The method decomposes each generated B-scan into a noisy image, a clean target response, a target mask, and three physically interpretable clutter components: direct antenna coupling,…
- OpenAlex · Published · First archived
Gibbs sampling-enhanced foundation model for in-context characterization of multivariate geotechnical data from limited measurements ↗
Accurately characterizing the spatial variability of multivariate geotechnical properties from limited site investigation data remains a critical challenge in geotechnical engineering. Existing geostatistical and machine learning methods often rely on predefined model structures or large training datasets for modeling, failing to capture high-frequency spatial variations and preserve multivariate dependency structures under sparse observations. Recently, in-context learning (ICL) with foundation models has provided a new paradigm for geotechnical site characterization. However, geotechnical da…
- OpenAlex · Published · First archived
Quality-by-Design Framework for Evaluating AI-Driven Machine Translation in Regulatory Affairs ↗
Background/Objectives: Machine translation increasingly prepares multilingual product information for EU marketing authorisation, yet no established process exists to adapt or validate a system for the Summary of Product Characteristics (SmPC), whose sections differ in regulatory function, terminology, and clinical risk. This work develops such a process and a qualification procedure against the approved human translation. Methods: An EMA-derived ontology divides the SmPC into seven regulatory domains, each with a provisional ICH Q9 risk profile, following Quality-by-Design principles. A No La…
- OpenAlex · Published · First archived
Development and Model-in-the-Loop Validation of a Neural-Network Algorithm for the Detection and Isolation of Faults in PMSM Drives for UAV Propellers ↗
The increase in reliability and safety levels of electrical power devices is key to the transition toward aviation electrification. Safety-critical equipment, such as propulsion motors, must comply with stringent requirements to obtain airworthiness certification. Permanent Magnet Synchronous Motors (PMSMs) have emerged as a suitable solution for high-performance flight control actuation, with promising extension to propulsive functions. Nevertheless, the reliability of PMSM power electronics converters remains below certification requirements, so fault-tolerant capabilities are essential for…
- OpenAlex · Published · First archived
Toward explainable traffic crash liability analysis via neural-symbolic multimodal reasoning: Integrating visual perception with legal knowledge graphs ↗
Determining traffic crash liability relies on rigorous on-site evidence and legal provisions and involves substantial manual intervention and potential human error. However, intelligent traffic monitoring systems focus on detection and tracking and cannot provide structured, rule-grounded decision support for formal crash-liability analysis. Moreover, emerging multimodal large language models (MLLMs) suffer from object hallucination that distorts or omits physical evidence, and their opaque reasoning cannot produce explicit evidence-to-rule chains required for traceable and reviewable liabilit…
- OpenAlex · Published · First archived
LLM-Driven User Preference Topic Modeling for Design Decision Support ↗
Online reviews of home medical devices are noisy, heterogeneous, and uneven in information density, which makes it hard to turn the voice of the customer into engineering design decisions. This study proposes and validates a review-mining framework that couples rule-based data governance with a locally deployed large language model (LLM) and complex-network analysis. Taking home oxygen concentrators as the case, we collected 25,893 raw review entries from JD.com, Taobao, and Tmall. Because overlapping collection windows made 80.4% of these rows redundant (5084 unique review texts), a four-stag…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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