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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 ↗5908 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
Advancing LLM-based 5G traffic classification: a multi-layer evaluation of performance and reasoning reliability ↗
Abstract The rapid proliferation of 5G networks has enabled a wide range of data-intensive applications, including video streaming, online gaming, metaverse platforms, and real-time communication services. Understanding the behavior and characteristics of such heterogeneous traffic is essential for efficient network management, resource allocation, and quality of service (QoS) optimization. However, most existing studies rely on simulated or synthetic datasets, limiting their applicability to real-world scenarios. This paper presents a comprehensive data-driven analysis of 5G traffic using a l…
- OpenAlex · Published · First archived
AI Integration in Indonesian Education: Trends, Outcomes, and Challenges - A Comprehensive Review ↗
Background Global education trends show the rapid growth of Artificial Intelligence (AI) as one of the leading technologies worldwide, a shift increasingly reflected in Indonesian education. This study aims to provide a Systematic Literature Review (SLR) by mapping the research landscape, its representation in practice and policy, its impact on learning quality, and the barriers to AI integration in Indonesian education. Method Using the PRISMA protocol, 34 peer-reviewed articles indexed in the Scopus and Google Schoolar databases (2020–2026) were systematically selected and thematically analy…
- OpenAlex · Published · First archived
Deep Learning-Based Reconstruction and Representation Learning of Open-Hole Well Logs Using Machine Learning ↗
Well-log measurements provide essential information for lithological identification, petrophysical characterization, and reservoir interpretation but incomplete log intervals may occur because of technical, operational, or economic constraints. This study investigates machine-learning-based reconstruction of missing open-hole well-log curves using a depth-indexed dataset from a single well in the Bărbuncești oil and gas field, Romania. Seven predictors (DEPTH, AC, GR, RFOC, RILD, RILM, and SP) were used to estimate ten target properties (CALI, CGR, NPHI, NPOR, PEF, POTA, RHOB, SGR, THOR, and U…
- OpenAlex · Published · First archived
Real-time classification of waste polyethylene using terahertz and raman spectroscopy integrated machine learning ↗
Abstract Efficient polymer classification is vital to recycling, yet conventional methods often struggle to distinguish plastics within the same class because of their similar thermophysical properties. This study presents an in-operation machine-learning (ML) approach combining terahertz time-domain spectroscopy (THz-TDS) and Raman spectroscopy for rapid classification of industrial polyethylene (PE)-rich waste feedstocks. Spectral data from six industry-provided PE-rich materials with differences in additives and physical form were collected to build a dual-modality spectral library. Princip…
- OpenAlex · Published · First archived
Socioeconomic status and rural–urban inequalities in AI access and academic performance for engineering students from the TVET ↗
This study investigates socioeconomic and rural–urban disparities in access to artificial intelligence (AI) among engineering students enrolled following the TVET path in Bangladesh. We further analyse how socioeconomic status (SES) and geographic location affect access to AI tools and their impact on academic performance. Using primary survey data from diploma engineering students, the study applies a probit model to evaluate AI access and a multivariable linear regression model to assess the influence of AI usage on academic outcomes. Results demonstrate that students from higher-SES backgro…
- OpenAlex · Published · First archived
Artificial intelligence in veterinary diagnostic histopathology: Concepts, applications, and clinical implementation ↗
Artificial intelligence (AI) in human diagnostic histopathology has advanced rapidly, but clinical adoption has lagged behind research. Veterinary diagnostic pathology, being less regulated, could adopt AI faster, but the extent of its integration into diagnostic laboratories is unknown. This article combines a narrative introduction to key concepts, a scoping review of published applications, and an exploratory survey of diagnostic laboratories to provide an overview of AI in veterinary diagnostic histopathology. The narrative review highlights emerging approaches, including vision transforme…
- OpenAlex · Published · First archived
X-MAG: LLM-driven multi-agent framework for XML-based SysML model generation ↗
Model-Based Systems Engineering (MBSE) has become a fundamental methodology for managing the complexity of modern engineering systems. However, conventional MBSE modeling remains labor-intensive and requires substantial expertise in both systems engineering and domain-specific knowledge. Recent advances in large language models (LLMs) provide new opportunities for automating MBSE workflows, yet direct LLM-based model generation still faces critical challenges, including the difficulty of producing well-formed XML structures, limited support for mature standards such as SysML 1.6, and insuffici…
- OpenAlex · Published · First archived
A wearable device data-driven artificial intelligence framework for personalized training guidance and its application in physical education teaching ↗
Wearable sensing creates an opportunity to tailor physical education (PE), but a deployable system must reconcile heterogeneous sampling rates, individual physiological variation, safety constraints, and classroom-scale latency. This study presents a scenario-oriented, full-chain framework that links multi-source sensing, edge preprocessing, spatiotemporal feature fusion, multi-task assessment, and multi-objective Actor-Critic decision support. Its distinguishing design is the coupling of individual baseline calibration with explicit objectives for training effectiveness, injury-risk control,…
- OpenAlex · Published · First archived
Grounding language models with deterministic verifiers for fraud detection ↗
Abstract Financial fraud detection is complicated by severe class imbalance, temporal change, adversarial manipulation, and the need for evidence-grounded explanations. Although large language models can generate readable rationales, their claims may be unsupported when they are not independently checked against structured evidence. We propose VR-FraudNet, a five-stage framework combining a time-conditioned spectral graph encoder, LightGBM triage and threshold routing, a schema-constrained rationale language model, a fixed deterministic verifier, and an isotonic probability mixer with split-co…
- OpenAlex · Published · First archived
PNBind : Prediction of Nucleic Acid‐Binding Sites Using Protein Structure and Protein Language Models ↗
Accurate prediction of nucleic acid-binding sites is essential for understanding protein function and facilitating drug discovery. However, current computational methods are limited by single-layer language model representations, Cα point clouds that discard local-frame geometry, and feature fusion in which high-dimensional sequence embeddings overshadow weaker evolutionary and structural signals. Here, we present PNBind, a multimodal deep learning framework that leverages protein language models, evolutionary information, and three-dimensional structural features via a geometric vector percep…
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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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