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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 ↗5231 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
Operating-zone identification of an auxiliary electrode electrostatic roll separator for ilmenite recovery using explainable machine learning ↗
Ilmenite recovery from heavy mineral sand processing rejects was investigated using an auxiliary electrode electrostatic roll separator (AEERS). The AEERS incorporates an independently positioned lower electrode beneath the grounded rotating drum, which is intended to extend electric field control into the particle discharge region relative to a conventional high-tension roll separator. A representative reject sample containing 11.97% ilmenite and 66.20% garnet, with minor amounts of rutile, zircon and sillimanite, was characterised and used as feed. A six-factor Central Composite Design (CCD)…
- OpenAlex · Published · First archived
Developing a generative AI chatbot to support students’ higher-order thinking in STEM education ↗
ABSTRACT Higher-order thinking is a key component of science, technology, engineering, mathematics (STEM) reasoning and supports students’ ability to reflect on their ideas, make connections, and generalize across contexts. We report a new approach to the use of generative AI in STEM education, in which we are developing a chatbot that aims to engage students in higher-order thinking, rather than giving instructional content. We describe a proof of concept test of whether interaction with our generative artificial intelligence chatbot could elicit students’ higher-order thinking talk (HOTT) in…
- OpenAlex · Published · First archived
Course-embedded AI-augmented teaching is associated with engagement and performance in software education ↗
AI-augmented teaching may reduce feedback delays in software courses, but semester-long classroom evidence remains limited. This course-level quasi-experimental study evaluated a course-embedded AI-augmented teaching (AIT) model in an undergraduate Python course. Two intact classes participated: 60 students in the AIT class used a large language model teaching assistant restricted to course-specific materials and instructional rules for question answering, code diagnosis, personalized recommendations, and teacher monitoring, whereas 60 students in the conventional-instruction class received th…
- OpenAlex · Published · First archived
A Hybrid Framework Combining Stylometric, Semantic, and SBERT Features for AI-Generated Text Detection ↗
The rapid advancement of Large Language Models (LLMs) has greatly improved the ability of artificial intelligence systems to generate human-like text, making AI-generated text detection more challenging. This study introduces a hybrid framework that merges stylometric and semantic features with Sentence-BERT (SBERT) embeddings to enhance classification performance and robustness. For evaluation, experiments used the AI vs Human Text Dataset. Documents under 50 words were excluded to ensure data quality. The final dataset was balanced, with 100,000 documents evenly split between AI-generated an…
- OpenAlex · Published · First archived
Comparing Global and Segment-Aware Machine Learning Models for Used Car Price Prediction ↗
Background: Used car prices are influenced by model year, mileage, fuel type, transmission, engine size, engine power and market position. This study compares global and segment-aware machine learning approaches for used car price prediction using the CarDekho dataset from Kaggle. Methods: After 167 exact duplicate extras were removed, 15,244 records were evaluated. The primary analysis used ten sealed 80/20 outer splits. Segment topology was selected by three-fold cross-validation within each outer-training set, and fixed CatBoost/XGBoost expert profiles were combined through an XGBoost routi…
- OpenAlex · Published · First archived
Dielectric and Measurement-Domain Feature Fusion for Machine Learning-Based Broadband Microwave Sensing: Application to Ethanol Concentration Estimation ↗
This study investigates the predictive capability of different microwave-derived feature types for machine learning-based ethanol concentration estimation in water–ethanol mixtures. Eleven mixtures were prepared at 10 wt.% concentration intervals, and cyclic measurements were performed over the 2–6 GHz frequency range to construct an experimental dataset. The input variables included conventional dielectric parameters together with reflection-based measurement features. Three ensemble regression models, namely Random Forest, Extra Trees, and XGBoost, were evaluated using group-wise 5-fold cros…
- OpenAlex · Published · First archived
A Machine Learning Approach to Predict Listeria monocytogenes Growth by Integrating FSSP and PMP Simulations: A Focus on t100 as a Possible Endpoint Parameters for Predictive Models ↗
Listeria monocytogenes is one of the most challenging foodborne pathogens for its psychrotrophic nature as well as the ability to grow under a wide range of environmental conditions; thus, food producers and regulatory agencies always search for new strategies to reduce the risk. In this context, predictive microbiology represents a valuable tool for supporting risk assessment and food safety management. This study investigated the time required for a 2-log increase in L. monocytogenes population (t100) as an integrated time-to-threshold endpoint and developed a simulation-derived modeling fra…
- OpenAlex · Published · First archived
LLM-Orchestrated Evasion of Tabular NIDS and GAN Augmentation for Multimodal Detection ↗
Network Intrusion Detection Systems (NIDSs) based on machine learning have demonstrated strong performance against known attack patterns. However, their robustness against adaptive evasion strategies orchestrated by modern Large Language Models (LLMs) remains largely unexplored. This study presents a reproducible framework for evaluating traffic-space evasion of a tabular network intrusion detection baseline by an instruction-constrained open-weight LLM and separately examines the effect of GAN-based augmentation on a multimodal detector’s clean-traffic performance. The proposed methodology em…
- OpenAlex · Published · First archived
Adaptive Rogers’ Ratio-Based Transformer Fault Diagnosis: A New Hybrid Fungal Growth Optimizer with Teaching-Learing-Based Optimization (FGO–TLBO) Metaheuristic for Explainable Intelligent Optimization Framework ↗
Accurate identification of transformer insulation faults is essential for reliable power-system operation, and dissolved gas analysis (DGA) remains an important diagnostic tool. However, conventional Rogers’ Ratio interpretation relies on fixed decision boundaries that may not represent overlapping gas patterns and measurement variability. This study proposes an explainable Hybrid FGO–TLBO framework that adaptively calibrates four Rogers’ Ratio decision boundaries while preserving the original rule-based diagnostic logic. The study uses 500 real DGA records representing six classes: Normal, Pa…
- OpenAlex · Published · First archived
Non‐Perturbative Bacterial Identification Directly From Solid Agar Plates Using Raman Micro‐Spectroscopy and Machine Learning ↗
Raman spectroscopy is a promising tool for microbial identification, yet its implementation in microbiology and clinical workflow is still restricted due to the accompanying additional preparation required to focus on microbial signals. Here, we demonstrate Raman-based bacterial identification directly from unopened, inverted agar plates, the same conditions used during incubation. Our approach enabled sensitive identification using two Escherichia coli variants, differing only in green fluorescent protein (GFP) expression, across diverse media and substrate material conditions, despite the in…
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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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