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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 ↗5456 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
Machine Learning of Echocardiographic Left Ventricular Myocardial Radiomics for Identification of Transthyretin Amyloid Cardiomyopathy ↗
BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an important cause of heart failure (HF) with increased left ventricular (LV) wall thickness that can be challenging to distinguish from other LV hypertrophic processes. Echocardiographic myocardial radiomic texture analysis may identify imperceptible amyloid-induced structural changes. OBJECTIVES: The study aims to develop and validate an echocardiography-based machine learning model using myocardial radiomics to identify ATTR-CM. METHODS: In this retrospective and prospective multicenter study, 854 echocardiographic scans were ana…
- OpenAlex · Published · First archived
Generative AI-Guided Versus Human-Guided Learning from Errors in Higher Education: A Comparison of Student Perceptions ↗
Generative artificial intelligence (GenAI) is increasingly used to support programming education, but its use in learning-from-errors activities has received limited research attention. This quasi-experimental study compared AI-guided and human-guided support in a structured learning-from-errors programming activity, focusing on students’ perceptions and their variability. A total of 59 college students completed the JavaScript error-detection and correction activity under AI-guided or human-guided support. Following the activity, students completed a questionnaire measuring perceived learning…
- OpenAlex · Published · First archived
A multiscale physics-informed neural network with RVE homogenization using isogeometric analysis for hygrothermal-electro-mechanical coupled analysis of piezoelectric composites ↗
Physics-informed neural networks have shown potential for coupled field analysis, but their application to heterogeneous piezoelectric composites under combined thermal and moisture effects remains limited because the effective properties depend on the microstructure and the residuals of the coupled governing equations may differ substantially in scale. This paper develops a multiscale method for hygrothermal-electro-mechanical coupled analysis of piezoelectric composites. At the microscale, a representative volume element of a 1–3 piezoelectric composite is constructed, in which the cylindric…
- OpenAlex · Published · First archived
Short-Term Photovoltaic Power Forecasting Based on a CEEMDAN-PE-ISSA-TCN-Transformer Framework ↗
This study proposes a CEEMDAN-PE-ISSA-TCN-Transformer framework for short-term photovoltaic (PV) power forecasting. Six pre-specified meteorological variables were retained, and their Pearson correlation coefficients with PV power were calculated using the training interval only. The power sequence was decomposed into high-frequency, medium-frequency, and low-frequency reconstructed components using CEEMDAN and permutation entropy (PE). A branch-specific residual TCN-self-attention forecasting network was constructed for each component, and an improved sparrow search algorithm (ISSA) was used…
- OpenAlex · Published · First archived
Scenic spot crowd flow prediction: Combining large language model semantic understanding and spatio-temporal network ↗
Accurate forecasting of tourist flows is crucial for urban planning, transportation management, and overcrowding prevention. Tourism demand is highly sensitive to external stimuli, often leading to abrupt and irregular fluctuations that are difficult to quantify using existing methods. To address this issue, we propose an event-aware dynamic spatiotemporal network (EAD-STN) that integrates large language model embeddings with dynamic spatiotemporal graph modeling. External factors are encoded into high-dimensional representations to capture their impacts on visitor demand. The model further in…
- OpenAlex · Published · First archived
Can Zero-Shot Large Language Models Be Trusted for Credit-Risk Prediction? An Explainability Audit Against LightGBM on Loan Default Data ↗
Zero-shot large language models (LLMs) offer a low-barrier route to credit-risk prediction, but regulated use requires evidence on discrimination, probability reliability, and auditable decision patterns. We compare four open-source LLMs (Gemma-2-9B, Llama-3.2-3B, Mistral-7B-v0.3, and Qwen2.5-7B) with LightGBM on 396,030 U.S. LendingClub loans issued from 2007 to 2016. Treating loans as independent and identically distributed observations from this pooled cross-sectional population, we evaluate every model on the same random 20% holdout. All models receive the same 21 structured predictors and…
- OpenAlex · Published · First archived
Hantavirus species detection from viral genome sequences using explainable artificial intelligence techniques ↗
Hantaviruses are important zoonotic pathogens associated with severe human diseases, including hemorrhagic fever with renal syndrome and hantavirus cardiopulmonary syndrome. The World Health Organization (WHO) has recognized the importance of strengthening hantavirus surveillance and early detection because of their public health impact. Nevertheless, Artificial Intelligence (AI)-based approaches for hantavirus species identification from viral genome sequences remain largely unexplored. This study developed and evaluated an explainable AI-based approach for hantavirus species identification u…
- OpenAlex · Published · First archived
Machine Learning in DED-Arc: A Critical Review ↗
Directed Energy Deposition (DED) utilizing an electric arc (DED-Arc) for additive manufacturing of metallic components has emerged as a promising manufacturing technology across aerospace, automotive and construction industries, due to its inherently high deposition rates, large build volume capabilities and low equipment acquisition and material costs. However, its high process complexity and susceptibility to geometric and metallurgical defects have motivated AI and Machine Learning technologies as viable alternatives for process modelling, complementing analytical and numerical approaches w…
- OpenAlex · Published · First archived
A hybrid framework of LLM-based interaction generation and adaptive sampler for recommendation enhancement ↗
Data augmentation (DA) is a critical technique for addressing data sparsity. Existing DA methods suffer from several limitations: extensive parameter tuning, dependency on specific types of data, and the tightly coupled DA network in the backbone recommendation model. Consequently, there remains a notable absence of universal data augmentation frameworks capable of adapting to diverse recommendation tasks and backbone models. To address these issues, we propose a hybrid framework to mitigate data sparsity via DA in a more flexible and universal paradigm. Notably, we disentangle the data augmen…
- OpenAlex · Published · First archived
One Voice, One Impression: Why Customers Perceive the Service Quality of AI Voice Bots Holistically and What It Means for Electronic Commerce ↗
AI voice bots are a standard channel for post-purchase service in electronic commerce, yet conversational-agent research has centered on text chatbots, and whether the quality dimensions of a voice agent are perceived as separate remains open. This study argues that customers experience the agent as one unified impression, because a voice-first encounter is a continuous spoken dialogue without a visual interface, so customers respond to the agent as a single social actor. Perceived quality is theorized as a higher-order construct, building on information systems success theory, technology acce…
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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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