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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 ↗5515 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
Explainable artificial intelligence of things integrated spatio-temporal feature modeling for traffic congestion framework on intelligent urban mobility system ↗
Abstract Recent developments of Artificial Intelligence of Things (AIoT) are transforming several sectors like Industry 4.0, smart cities, intelligent healthcare, and smart transportation. The AIoT can be employed in urban mobility to enable real-time monitoring and intelligent decision-making in the transportation sector. Traffic congestion classification is a significant aspect of Intelligent Transportation Systems (ITS), as the rising urban population has contributed to severe congestion complexity that impacts air quality, road safety, and travel time. Accurate classification of congestion…
- OpenAlex · Published · First archived
Information-theoretic efficiency diagnosis of neural-network estimators for sub-THz intensity-only wall sensing ↗
Abstract Accurate estimation of building-material dielectric parameters from sub-terahertz penetration-loss spectra underpins the delineation of electromagnetic protection zones in 6G physical-layer security. At sub-THz frequencies only the intensity envelope is reliably observable, and under this constraint the Fisher information matrix for joint permittivity–thickness estimation is severely ill-conditioned at fixed incidence (condition numbers $$10^7$$ 10 7 – $$10^9$$ 10 9 ). We propose an efficiency-diagnostic framework that quantifies how closely a data-driven estimator approaches the Cram…
- OpenAlex · Published · First archived
Knowledge generation and evaluation for construction quality assessment using retrieval-augmented large language models ↗
To address the problems of delayed knowledge updating, inefficient information acquisition, and difficulty in cross-project knowledge reuse in civil engineering construction quality assessment, this paper proposes a retrieval-augmented generation (RAG)-based methodology for injecting domain knowledge into large language models (LLMs). Multi-source knowledge, including national standards, journal articles, textbooks, and engineering reports, was systematically collected, cleaned, semantically segmented, and encoded using the bge-large-zh-v1.5 model to build a Milvus vector database. A general-p…
- OpenAlex · Published · First archived
Spectrally selective structures for glass cover of silicon photovoltaic module to enhance its energy yield based on neural network surrogate-assisted genetic algorithm ↗
Strategically transmitting harvestable photons and reflecting non-useful photons are essential for photovoltaic (PV) efficiency improvement and temperature reduction. However, it remains a considerable challenge to develop spectrally selective thin-film structures that can maximize PV lifetime energy yield (EY) while maintaining structural simplicity and adequate manufacturability. Herein, a neural network surrogate-assisted genetic algorithm (NNSGA) framework is proposed to facilitate the design of spectrally selective structures. By incorporating spectral utilization weight (SUW) of PV modul…
- OpenAlex · Published · First archived
Artificial Intelligence in Virus Detection: From Metagenomic Discovery to Clinical Decision Support and Population Surveillance ↗
Artificial intelligence (AI) is increasingly central to modern virus detection, particularly as metagenomic next-generation sequencing (mNGS) expands diagnostic and surveillance capabilities beyond targeted assays. This paper reviews how AI supports virus detection across three interconnected levels. The metagenomic layer forms the technical core of the review, while the clinical and population sections examine how sequence-level AI outputs are translated into patient-level and public-health decision support. At the metagenomic level, AI addresses key bottlenecks caused by fragmented assemblie…
- OpenAlex · Published · First archived
From wetting-front coalescence to urban road collapse: interpretable machine learning of hydro-mechanical failure under coupled rainfall and pipeline leakage ↗
Abstract Urban ground collapse poses a severe threat to public safety, road operation and underground infrastructure in densely developed cities. This study investigates the hydro-mechanical failure mechanism of road collapse induced by coupled rainfall infiltration and pipeline leakage through physical model testing, finite element simulation and interpretable surrogate modelling. A validated numerical model incorporating saturation-dependent strength and stiffness degradation was used to generate 120 cases with different pipe burial depths, rainfall intensities and leakage pressures.The resu…
- OpenAlex · Published · First archived
LEVERAGING ICT AND ARTIFICIAL INTELLIGENCE IN STUDENT RECORDS MANAGEMENT IN NIGERIAN UNIVERSITIES ↗
This study focuses on the role of Information and Communication Technology (ICT) and Artificial Intelligence (AI) in student records management in Nigerian Universities. Student records are critical to the admission and registration process, to course enrolment, for the processing of examinations, to academic progression and graduation, to the production of student transcripts, to institutional decision making, and beyond. But in a lot of Nigerian universities, records retrieval, storage of files, loss of files, duplication of records, inaccurate data entry, lack of reliable electronic system…
- OpenAlex · Published · First archived
A Machine Learning Framework for Predicting Fracture Dip Angle in Shale Gas Reservoirs from Pulsed Neutron Logging Responses ↗
Accurate characterization of fracture angles is crucial for evaluating natural gas storage and transport capacity in shale gas reservoirs. Although numerical simulations based on neutron transport theory can model well log responses at different fracture angles, the process is computationally intensive and is not suitable for field applications. This study proposes a data-driven machine learning (ML) framework to rapidly and accurately predict fracture dip angles directly from pulsed neutron logging measurements. We generated a comprehensive dataset using a high-fidelity deterministic neutron…
- OpenAlex · Published · First archived
ARTIFICIAL INTELLIGENCE-BASED TOOLS AND ENGLISH LANGUAGE PROFICIENCY AMONG UNIVERSITY STUDENTS IN UZBEKISTAN: A PRISMA-GUIDED SYSTEMATIC REVIEW ↗
Artificial intelligence (AI) is rapidly reshaping English language education through generative chatbots, automated writing evaluation, intelligent tutoring systems, speech-recognition applications, adaptive learning environments, and AI-supported feedback. This PRISMA-guided systematic review synthesizes recent international and Uzbekistan-specific evidence on the effectiveness of AI-based tools in improving English language proficiency among university students. The review focuses on writing, speaking, reading, listening, vocabulary, feedback, motivation, engagement, and learner autonomy. Me…
- OpenAlex · Published · First archived
Physically Constrained Temporal Fusion Transformer with LLM-Assisted Hyperparameter Optimization for Multi-Scenario Energy Time Series Forecasting ↗
Accurate forecasting of battery state of health (SOH), wind power, and photovoltaic (PV) power is challenged by non-stationarity, multiscale dependencies, and physically implausible outputs. This study proposes a physically constrained Temporal Fusion Transformer (PC-TFT) that coordinates enhanced temporal representation, physical regularization, and LLM-assisted Bayesian optimization. Mogrifier-LSTM and a feature-enhanced attention module capture transient fluctuations and long-range dependencies, while spectral normalization stabilizes training under physical constraints. Scenario-specific d…
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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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