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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 ↗5934 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
A Scalable Method for Validated Data Extraction from Electronic Health Records with Large Language Models ↗
Purpose/Background: Health care organizations increasingly require structured, patient-level clinical variables for treatment decisions, operational workflows, quality measurement, and clinical trial screening. Relevant information is often fragmented across heterogeneous electronic health record (EHR) systems, unstructured formats, and scanned documents. Large language models (LLMs) offer an opportunity to enhance medical record extraction, particularly in oncology where point-of-care structured coding rarely captures a patient’s full longitudinal clinical course. Methods: Two complementary L…
- OpenAlex · Published · First archived
LLM-supported self-diagnosis and self-treatment of familial Mediterranean fever: a case report ↗
Large language models (LLMs) are increasingly being used by patients to seek explanations for persistent or unexplained symptoms. However, their impact on real-world diagnostic pathways and patient behavior remains poorly understood. We report the case of a 34-year-old male of Egyptian origin with recurrent episodes of fever, severe migratory abdominal pain, arthralgia, myalgia, and intermittent erythematous skin lesions since early childhood. Despite almost 3 decades of extensive investigations, no unifying diagnosis had been established. During an iterative dialogue with the LLM DeepSeek in…
- OpenAlex · Published · First archived
A human-centric approach for responding to online soft hate speech: the effect of AI-based persuasion on the comment modification intention of commenters ↗
The development of digital technology has facilitated the 'democratisation' of the internet, but it has also provided opportunities for online hate speech, which can have negative effects on individuals, groups, and society. Therefore, addressing online hate speech is crucial. However, owing to the paradox between free speech and the regulation of hate speech, soft hate speech, which is morally unacceptable but not directly constrained by law, as hard hate speech is, can result in harmful consequences in more subtle forms. In response, this study proposes shifting the focus of online hate spee…
- OpenAlex · Published · First archived
Predictive modeling of a metabolism-linked cognitive neurodynamic system for Alzheimer’s disease using a radial basis scaled conjugate gradient neural network and electroencephalography-based multi-entropy analysis ↗
Abstract Aging, metabolic syndrome, and obesity significantly increase the risk of Alzheimer’s disease (AD). This study presents a fractional-order obesity-driven AD (OD-AD) model incorporating insulin resistance and simulated using a radial basis scale conjugate gradient neural network (RB-SCG-NN). The nine-compartment model describes glucose regulation, insulin action, intestinal beta-cell function, microglial response, amyloid- β accumulation, tau aggregation, neuronal damage, and cognitive decline. Numerical solutions are obtained using the modified Atangana–Baleanu–Caputo (mABC) fractiona…
- OpenAlex · Published · First archived
Fine-Grained Analysis of Bilingual Transportation Discourse on Reddit Using Large Language Models: A Quebec City Case Study ↗
Social media platforms generate large volumes of unsolicited textual content that reflect users’ perceptions of urban transportation. This study proposes a fine-grained annotation framework that classifies social media text into three constructs: attitude, intention, and reported behavior. Each statement is linked to a specific transportation mode and sentiment polarity. Two large language models (LLMs), GPT-5.5 and DeepSeek-Flash, were evaluated using few-shot in-context learning on 200 manually annotated English–French Reddit discussions from Quebec City, Canada. GPT-5.5 was selected based o…
- OpenAlex · Published · First archived
Machine Learning Augmented Detection of Trace Explosives on Surfaces Using Active Longwave Infrared Imaging ↗
There is a need for rapid non-contact detection of trace explosives on arbitrary surfaces for applications such as checkpoint screening and post-blast forensics. In this work, active longwave infrared imaging, using a tunable quantum cascade laser light source, is shown to be able to detect three common explosives on typical luggage material substrates at surface concentrations as low as 230 ng/cm 2 . Use of a random forest machine learning algorithm, trained using synthetically produced spectra, allows performance beyond predicted limitations of speckle noise. The random forest algorithm furt…
- OpenAlex · Published · First archived
Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training ↗
Although the spotlight on ecological efficiency in artificial intelligence (AI) research is intensifying, its integration remains underdeveloped. Addressing this gap is essential to align algorithmic performance with ecological responsibility and support the carbon neutrality targets of the Organization for Economic Cooperation and Development (OECD). This study proposes a methodology to measure CO 2 emissions and energy consumption during dataset encoding and training of Spiking Neural Networks (SNNs) in regression tasks. Using rate encoding with varied hyperparameters, we compare the origina…
- OpenAlex · Published · First archived
Machine Learning-Enabled Optimization for Next-Generation Wireless Networks: A Survey of Intelligent Resource Management in RIS-Assisted Systems ↗
In recent years, reconfigurable intelligent surfaces (RISs) have been proposed as a promising disruptive technology for future wireless communication systems. RISs enable unprecedented dynamic and programmable control of the electromagnetic waves by integrating software-defined metasurfaces into wireless environments. When smartly configured with the phase shifts of incident signals, RIS systems have the potential to improve spectral efficiency, energy efficiency, coverage, security, and other wireless metrics without the need for additional transmit power or active radio frequency chains. How…
- OpenAlex · Published · First archived
A quantum-secured explainable artificial intelligence framework with metaheuristic optimization for scalable cloud environments ↗
Abstract Cloud computing represents an essential platform for intelligent data processing, scalable service provision, and distributed computing structures. Unfortunately, existing cloud systems are still confronted with numerous cybersecurity issues, the absence of Explainable Artificial Intelligence (XAI), ineffective resource management, and scalability constraints, especially when it comes to new emerging quantum-based threats. Conventional cloud security instruments are based mostly on classical cryptography, meaning that they may become insecure in post-quantum conditions, while the exis…
- OpenAlex · Published · First archived
Severity prediction of traffic accidents with recent machine learning paradigms and SHAP ↗
Abstract Traffic-related fatalities are among few leading causes of death worldwide. Crash severity prediction of traffic crashes has become a widespread research subject in recent years. Severity outcomes of crashes are usually influenced by factors such as humans, vehicles, roadway, weather, environment, and their interactions. A better understanding of crash-contributing circumstances and crash severity risk predictors is vital for the timely and effective implementation of appropriate mitigation measures. In the literature, various statistical methods have been widely adopted for crash sev…
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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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