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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 ↗5548 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
AICybOps: A Framework for Operationalizing Machine-Learning Models in Cybersecurity ↗
Machine learning is increasingly used in cybersecurity, for example, for anomaly and intrusion detection in microservice and Internet of Things environments. Guidance on securing the machine learning operations (MLOps) lifecycle, termed MLSecOps or SecMLOps, recommends continuous monitoring, a versioned model registry, and lifecycle integration of training, evaluation, and serving. This layer is typically built per deployment, as model-specific code rather than a shared framework. We present AICybOps, a framework that systematizes these practices so the operational features and security proper…
- OpenAlex · Published · First archived
Do-It-with-AI Framework for Invention: A Kundt’s Tube Case Study ↗
This exploratory case study examines the feasibility of integrating generative artificial intelligence (AI) into a low-cost Kundt’s tube activity in pre-service science teacher education. The proposed Do-It-With-AI framework comprises four phases: conceptual exploration, design development, troubleshooting, and extension to real-world applications. Scopus AI was used to support the literature-oriented conceptual exploration, while ChatGPT-4 and ChatGPT-5 supported the generation of design alternatives, troubleshooting hypotheses, and possible extensions. Three pre-service teachers constructed…
- OpenAlex · Published · First archived
Analysis and Calibration of DEM Parameters for Rock Materials Based on Interpretable Machine Learning ↗
The accuracy of discrete element method (DEM) simulations depends on the appropriate selection of microscopic parameters. However, conventional calibration using biaxial tests requires repeated adjustment of microscopic parameters until the macroscopic responses of numerical specimens match the target responses, making the process cumbersome and computationally demanding. In this regard, this study presents an interpretable, surrogate-assisted machine-learning framework for analyzing and calibrating DEM parameters of brittle rock materials represented by the Hertz–Mindlin bonded contact model.…
- OpenAlex · Published · First archived
Probabilistic machine learning framework for peripheral dimension optimization of five-phase induction motors ↗
This article proposes a probability-based Machine-learning (ML) framework for peripheral-dimension optimization of a Five-Phase Induction Motor (FPIM). The proposed approach investigates the influence of Inner Stator Diameter ( D ), Core Length ( L ), and L/D ratio on motor-performance parameters, including efficiency ( η ), power factor ( p f ), and magnetic flux density ( B a v ). Machine volume ( D 2 L ) is maintained constant. A dataset consisting of 1000 electromagnetic simulation cases was generated using detailed Finite Element Modelling (FEM) in ANSYS Maxwell. The generated dataset was…
- OpenAlex · Published · First archived
Prompt-Shape- and Prefix-Cache-Aware CPU/NPU Routing for Large Language Model Inference on Edge SoCs ↗
Large language model (LLM) inference on edge systems must exploit heterogeneous central processing unit (CPU) and neural processing unit (NPU) resources under tight memory and latency constraints. Because prefilling and autoregressive decoding have different computational characteristics, the fastest backend depends on the request shape and prefix-cache reuse. This work proposes request-level CPU/NPU routing for the RK3588 system on chip (SoC). Prompt-shape-aware routing (PSR) uses prompt length and generation budget to select a backend for no-cache requests, while prefix-cache-aware routing (…
- OpenAlex · Published · First archived
Jian'er Jiedu granule alleviates acute lung injury via NOD2/NF-κB/NLRP3 axis: Network pharmacology, machine learning, and experimental validation ↗
BACKGROUND: Acute lung injury (ALI) is a severe inflammatory disorder associated with high mortality. Jian'er Jiedu granule (JEJD), a formulation developed by Guangdong Provincial Hospital of Traditional Chinese Medicine, has shown promise in alleviating Omicron-related symptoms; however, its therapeutic effects on ALI remain unclear. PURPOSE: To explore the therapeutic effects and mechanisms of action of JEJD in ALI. METHODS: The active components and potential targets of JEJD were screened using TCMSP, TCMIP, TCMID, and Herb databases. ALI-related genes were obtained from GeneCards, OMIM, an…
- OpenAlex · Published · First archived
When Trust and Privacy Pull in Opposite Directions: Consumer Decision Making with Conversational AI ↗
Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study examines how consumers’ perceptions of information quality, personalization, explainability, and responsiveness are associated with purchase intention through their relationships with perceived usefulness, algorithmic trust, and privacy concern, while considering AI literacy as a boundary condition. Data from 588 Chinese online shoppers who completed a standardized shopping task through direct interaction with a conversational AI shopping assistant were analyzed using structural equation modeling. The results show that all…
- OpenAlex · Published · First archived
Predicting student success in open and distance learning: a comparative evaluation of machine learning models ↗
Purpose This study compares Logistic Regression, linear Support Vector Machine, Random Forest and Histogram Gradient Boosting for predicting four student outcomes in open and distance learning (ODL) from the first four weeks of study. Design/methodology/approach Using the Open University Learning Analytics Dataset, the analysis included 27,535 registrations from 24,841 students. Day-28 predictors comprised static characteristics, raw virtual learning environment (VLE) activity and engagement-focused behavioral features. Class-weighted models were evaluated using nested stratified group cross-v…
- OpenAlex · Published · First archived
Hybrid Transformer Architecture for Context-Aware Spam Email Classification ↗
Spam and phishing email remain a persistent cybersecurity threat and a primary delivery vector for credential theft, malware, and ransomware. Detection accuracies above 99% are routinely reported. Such figures are typically obtained on small, single-source, monolingual corpora in which near-duplicate campaign templates span the training and test partitions. This study re-examines what they measure. A corpus of 99,707 emails is assembled from four independent public collections spanning multiple languages, campaign-aware deduplication is applied before splitting, and nine models—classical, conv…
- OpenAlex · Published · First archived
Explainable Artificial Intelligence (XAI) for 3D Medical Imaging: A Scoping Review of Volumetric Attribution Methods ↗
Explainable artificial intelligence (XAI) methods developed for two-dimensional images may not preserve spatial context when applied to volumetric medical imaging. This scoping review map attribution methods for 3D medical images and examines how dimensionality is preserved across model inference, attribution generation, and visualization. Following PRISMA-ScR guidance, we searched IEEE Xplore, PubMed, ACM Digital Library, Semantic Scholar, and arXiv for studies published from January 2018 to May 2026. We mapped 32 selected studies by attribution method, imaging modality, clinical task, evalua…
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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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