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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 ↗5927 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
AI powered handwritten answer script evaluation framework for automated assessment of text and figure based responses ↗
Manual evaluation of handwritten answer scripts is time-consuming and subjective, and grows increasingly impractical as enrolment rises; moreover, most existing automated grading systems support only typed text and cannot assess figure-based responses, leaving a substantial gap in educational technology. To address this problem, this research presents a computer-based evaluation framework for handwritten answer scripts containing both textual and graphical responses. Scripts are digitized through optical character recognition (OCR), and the recognized text is evaluated using four complementary…
- OpenAlex · Published · First archived
Understanding and optimizing xylose conversion to furfural for biomass valorization using experimental-machine learning methods ↗
Furfural is an important biobased platform chemical produced via the acid-catalyzed dehydration of xylose; however, its industrial selectivity remains constrained by competing degradation pathways. This study evaluates the catalytic performance of ZSM-5 zeolites, SO3H-functionalized biochar, and sulfonic acid-functionalized Material Institute Lavoisier (MIL)-101 (Chromium) in various solvent environments (water, toluene, and gamma-valerolactone (GVL)) to identify conditions that maximize xylose conversion and furfural selectivity. Pearson correlation coefficients were calculated to assess how…
- OpenAlex · Published · First archived
Preserving epistemic authenticity: process-oriented assessment in the age of generative AI ↗
The rapid advancement of generative artificial intelligence (AI) has introduced significant challenges for higher education (HE) assessment practices, particularly those relying on unsupervised written tasks. As AI systems become increasingly capable of producing sophisticated academic text, traditional product-oriented assessments face growing pressures to reliably capture ‘authentic’ student learning, reasoning and disciplinary engagement. In response, we argue for a shift towards more process-oriented assessment approaches that foreground how learners interpret, justify and construct unders…
- OpenAlex · Published · First archived
Intelligent Rehabilitation Systems Based on Big Data Analytics and Artificial Intelligence: A Systematic Review ↗
The world population is aging at an accelerating pace, and the rate of disability caused by chronic diseases and trauma is on the rise. The traditional rehabilitation model has some structural defects, such as subjective evaluation, homogeneity in treatment schemes, imbalance of resource allocation, and insufficient intervention accuracy. The deep integration of big data analysis, artificial intelligence (AI), edge computing, digital twins, federated learning and multimodal large models is promoting a shift in the rehabilitation system from an experience-driven paradigm to a data-driven paradi…
- OpenAlex · Published · First archived
Advances in machine learning applications for battery thermal management systems: modeling methods, optimization strategies, and future trends ↗
With the rapid development of renewable energy and energy storage technologies, battery systems are evolving toward high energy density and high integration. Thermal runaway has gradually become a key factor limiting system safety and reliability. The design of traditional battery thermal management systems has mainly relied on experimental testing and numerical simulation. However, high computational cost and long development cycles are often encountered in coupled analysis under multiple operating conditions and in the optimization of complex structures. In recent years, machine learning has…
- OpenAlex · Published · First archived
Integrating Facility Layout Planning, Discrete-Event Simulation, Machine Learning, and Immersive Visualization: A Comparative Study of Two Manufacturing SMEs ↗
Facility-layout decisions in small manufacturing firms are often evaluated with heterogeneous indicators, which complicates comparison and reproducibility. This study develops a traceable decision-support workflow and applies it to two manufacturing SME cases in Cotopaxi, Ecuador. Systematic Layout Planning and Guerchet were used as the common core, complemented by case-specific location and prioritization tools, three-dimensional discrete-event simulation in FlexSim, bounded surrogate-model screening, and Meta Quest 2-based immersive review. IPMATOAH’s model comparison increased output from 1…
- OpenAlex · Published · First archived
Exemption or backfire? The double-edged effect of AI attribution strategy on brand trust in digital content moral crises ↗
Human-AI collaboration has become central to brand content production, but AI's black-box nature obscures responsibility in digital content moral crises. When such content is perceived as violating widely shared social values, brands increasingly attribute such incidents to AI errors. Prior research has mainly emphasized the responsibility-exemption role of AI attribution strategy, offering limited insight into its moral risks, mixed consequences, and underlying mechanisms. Drawing on the fact-value model, this study conducts five scenario-based experiments to examine how AI attribution strate…
- OpenAlex · Published · First archived
AI-Driven Autonomous Data Center Management Framework for Enhancing Computing Efficiency in Saudi Arabia’s Digital Infrastructure ↗
Data centers are becoming strategic infrastructure for cloud services, artificial intelligence, digital government, finance, healthcare, and industrial platforms. Their growth, however, intensifies the coupled challenges of computing utilization, cooling energy, equipment reliability, service-level compliance, and carbon-aware operation. This review synthesizes recent peer-reviewed research on artificial intelligence for autonomous data center management and develops a Saudi-oriented framework for improving computing efficiency without treating energy optimization as an isolated facilities pro…
- OpenAlex · Published · First archived
Optimal Design of the Microwave Oven Magnetic Shunt Transformer Using Physics-Based Equivalent Circuit Model and GNN-Guided NSGA-III Method ↗
Magnetic shunt transformers (MSTs) used in microwave ovens are designed with deliberately high leakage inductance for current limiting and voltage regulation. This requirement couples the core dimensions, winding parameters, shunt geometry, material cost, and power losses. This paper presents an optimal design strategy in which every candidate is evaluated by a physics-based equivalent circuit model and a graph neural network (GNN)-guided non-dominated sorting genetic algorithm III (NSGA-III) optimizer. In the proposed framework, all candidate designs are evaluated for their electromagnetic pe…
- OpenAlex · Published · First archived
MechBBB: A Two-Stage Mechanism Informed Machine Learning Tool for Blood Brain Barrier Permeability Prediction ↗
Background/Objectives: Blood–brain barrier (BBB) permeability prediction is important for central nervous system drug discovery. Many models rely mainly on chemical structure, omit transport information, and may report optimistic performance when related compounds occur across training and test sets without adequate leakage control. We developed MechBBB, a two-stage, transport-informed machine-learning framework that adds learned efflux, influx, and passive-permeability scores to conventional molecular features and evaluates their added value under explicit leakage control. Methods: In Stage 1…
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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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