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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 ↗5944 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
Prediction of water-conducting fracture zone height under repeated mining in close-distance coal seam groups using a BP neural network ↗
Accurate prediction of the height of the water-conducting fracture zone (WCFZ) is essential for assessing hydraulic connection between mining-induced fractures and overlying aquifers in close-distance coal seam groups. Repeated extraction differs from isolated single-seam mining because later disturbance acts on an overburden that has already experienced separation, fracturing, and partial compaction. Cumulative mining thickness and interlayer spacing may therefore exert coupled and strongly nonlinear influences on fracture-zone development. In this multi-project engineering-data study, 30 ano…
- OpenAlex · Published · First archived
Overcoming the Energy–Health Trade-Off in Smart Buildings: AI-Driven Dynamic Thresholding for Sustainable HVAC (Heating, Ventilation, and Air Conditioning) Actuation and Multi-Pollutant Management ↗
Modern smart buildings face the challenge of balancing energy-saving requirements with strict indoor air quality regulations. The aim of this research is to develop an intelligent, energy-efficient, and robust multi-pollutant forecasting and control framework that integrates hybrid LSTM–GRU forecasting with Proximal Policy Optimization (PPO)-based reinforcement learning to maintain indoor air quality while minimizing unnecessary energy consumption and mechanical actuation. Using Green Computing principles, the agent significantly reduces excessive mechanical energy use while dynamically and si…
- OpenAlex · Published · First archived
AI-powered smartphone platform for robust detection of alpha-fetoprotein via nanozyme-mediated dual-readout lateral flow immunoassay ↗
Traditional lateral flow immunoassay (LFIA) relies on subjective visual interpretation and shows limited quantitative capability. Existing smartphone-assisted LFIA image analysis methods enable image-based quantification, yet they suffer from result variability caused by inconsistent imaging distance, alignment and lighting, require manual processing that introduces subjective bias, and often rely on customized devices or controlled conditions, restricting practical deployment. Herein, a S martphone- Y ou Only Look Once (YOLO)- N anozyme E nhanced R egion of Interest (ROI) Grayscale assa y (SY…
- OpenAlex · Published · First archived
Learning to Route On-Chip: A Survey of Machine Learning-Based Routing in Networks-on-Chip and Its Hardware Overheads ↗
Network-on-Chip (NoC) fabrics are the dominant interconnect for many-core processors, yet widely used routing methods—dimension-order routing and turn-model-based adaptive variants—assume mostly local, low-variance congestion. Modern workloads generate dense and bursty traffic that breaks these assumptions, motivating data-driven routing. Following a PRISMA-ScR protocol across five databases (2015–2026), this survey reviews machine-learning-based NoC routing across a 48-study evidence base, where path selection is posed as a Markov Decision Process and addressed with reinforcement learning and…
- OpenAlex · Published · First archived
Q-NAV: AI-Assisted Quantum Navigation For GPS-Denied Autonomous Systems ↗
Systems for global navigation such as GPS are of great importance in enabling autonomous vehicles, drones, and robotic systems to navigate. Yet because these systems rely on external satellite signals, autonomous systems can be susceptible to signal obstruction, jamming, spoofing, and operation in areas where GPS is unavailable. Although conventional Inertial Navigation Systems (INS) can offer navigation independent of external signals, they suffer from sensor noise accumulation and drift, which leads to a gradual increase in localisation errors over time. This study presents a hybrid navigati…
- OpenAlex · Published · First archived
Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach ↗
Predicting student academic performance is of significant importance for enabling timely educational interventions and improving educational outcomes in secondary education. However, existing predictive models in educational data mining often face the dual challenges of limited interpretability and suboptimal performance on small to medium-sized datasets. This study introduces the Prior-Data Fitted Network (TabPFN) combined with SHapley Additive exPlanations (SHAP) to construct an interpretable machine learning framework for predicting student achievement. Utilizing a publicly available datase…
- OpenAlex · Published · First archived
Machine Learning–Based Demand Side Management for Industrial Load Scheduling with Solar Energy Integration ↗
As the effects of volatile electricity prices, network constraints, and the growing use of renewable energy sources on industrial facilities become more significant, the traditional load scheduling approach that uses fixed operating plans is being challenged, and this has led to reconsideration of how load scheduling is managed in light of the convergence and interplay of these factors. Conventional methods fail to take into account hourly price fluctuations and the time-dependent availability of solar energy, leading to unnecessary operational costs and inefficient energy use in many instance…
- OpenAlex · Published · First archived
A dynamic explainable AI framework for real-time intrusion detection and automated alert prioritization in security operation centers ↗
Abstract Security Operation Center (SOC) analysts face 50–100 security alerts per hour, leading to cognitive fatigue and delayed incident response. Existing intrusion detection systems (IDS) suffer from high false-positive rates and opaque black-box architectures that erode analyst trust and slow triage decisions. This paper proposes the Dynamic Explainable Framework (DEF), which couples a machine-learning detection backbone with inference-time SHAP and LIME explanations and an explanation-aware alert prioritization layer. DEF is evaluated on two benchmarks under an identical protocol: a strat…
- OpenAlex · Published · First archived
Establishing inherent deformation database for Q355 steel T-joints in stiffened panels by numerical approach and artificial neural network ↗
The typical welding joints in stiffened panels are generally subjected to different structural restraint conditions, and the welding processes applied to individual joints may also vary to some extent. Consequently, inherent deformations may differ among joints within a given structure, and it is difficult to acquire all the inherent deformation data for every joint. Therefore, the conventional inherent strain method faces a variation issue. This study focuses on the stiffened panel structures and takes low-alloy steel T-joints as the research object. By employing experiments and numerical sim…
- OpenAlex · Published · First archived
What are the knowledge, use, and perceived benefits and limitations of artificial intelligence chatbots among Italian nursing students? A cross-sectional multicentre study ↗
Exploring students’ knowledge, use, and perceptions of Artificial Intelligence (AI) chatbots may inform strategies in the educational context. However, limited evidence is available on Italian nursing students, leaving this field largely unexplored. A cross-sectional online survey was conducted, following the Checklist for Reporting Results of Internet E-Surveys, the Strengthening the Reporting of Observational Studies in Epidemiology, and the Consensus-Based Checklist for Reporting of Survey Studies. Eligible participants were students enrolled in the nursing program at three universities. Th…
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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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