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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 ↗5969 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
Failure Pressure of Corroded-Pipeline Prediction—Artificial-Intelligence Model with FEM and Experimental Data ↗
This research work aims to implement empirical models for predicting the failure pressure of a pipeline corroded with a single defect and internal pressure applications. For this purpose, five approaches were used: two artificial intelligence (AI) models with experimental data, a finite-element model (FEM), and two analytical models (ASME B31G modified and DNV-RP-F101). The two proposed artificial models were implemented and optimized in order to find their best performance. The first AI method is an artificial neural network with multilayer perceptron (ANN-MLP), and the second is a support ve…
- OpenAlex · Published · First archived
Malware Detection by Machine Learning based on the LightGBM Model ↗
Wannacry has grown to be considered one of the biggest threats affecting computer security over the past ten years, which has prompted continuous research on detection and mitigation techniques. The EMBER 2018 dataset, which contains sample WPE files categorized as harmless and harmful, is used to synthesize research findings and data in order to build an efficient machine learning model for malware identification. The pre-processed data is sent into a deep learning system for training. The machine learning model was built using dense and conventional dropout layers to reduce constraints on re…
- OpenAlex · Published · First archived
AI-Based Image Enhancement Improves Detection of Interconnect Defects in Microelectronic Packaging ↗
Abstract Traditional failure-analysis (FA) methods face growing challenges due to the increasing miniaturization and complexity of modern semiconductor devices. In particular, the detection of cracks in the extreme low K layers of flip-chips, utilized in advanced packaging architectures at the back end of line (BEOL), is challenging. To improve detectability of these so-called “white bumps”, we propose an AI-based image-enhancement workflow, specifically tailored to improve scanning acoustic microscopy (SAM) images. By applying a deep CNN with skip connections and network in network (DCSCN) mo…
- OpenAlex · Published · First archived
Multi-sensor fault diagnosis of automated guided vehicles in container terminals using MCNN-SEGate-Transformer ↗
Automated guided vehicles (AGVs) provide horizontal transport between quay-side and yard-side systems in automated container terminals. Reliable fault diagnosis is important for maintaining transport continuity and supporting targeted maintenance. However, operational multi-sensor monitoring data are high-dimensional, contain short-term temporal variations, and include variables with unequal diagnostic relevance. Sensor responses may also overlap across fault types. This study develops MCNN-SEGate-Transformer, a multivariate time-series model for AGV fault diagnosis, in which MCNN denotes a mu…
- OpenAlex · Published · First archived
Hybrid machine learning model for building heating demand forecasting using autoencoder and deep extreme learning ↗
Space heating load is the main energy consumption driver for residential buildings, and its estimation is critical for the efficient and economic operation of energy systems. In this paper, nine different Artificial Intelligence (AI)-based Machine Learning (ML) models — Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Elastic Net (ENet), Extreme Learning Machine (ELM), Convolutional Neural Networks (CNN), Decision Tree (DT), Histogram-based Gradient Boosting (HGB), Long Short-Term Memory (LSTM) and a hybrid Autoencoder and Deep Extreme Learning Machine (AE + DELM) —…
- OpenAlex · Published · First archived
Artificial intelligence, the knowledge problem, and academic judgment: an Austrian perspective ↗
Recent advances in generative artificial intelligence have renewed claims that university teaching and research can be largely automated. This article argues that such claims confuse information processing with judgment. Drawing on Hayek’s theory of dispersed and tacit knowledge, Huerta de Soto’s analysis of computation and entrepreneurial discovery, and the theory of judgment under Knightian uncertainty, it explains why greater computational capacity does not eliminate the knowledge problem. Contemporary AI can retrieve, recombine, and generate large volumes of context-sensitive information,…
- OpenAlex · Published · First archived
Agency, Cognition and Mathematical Learning in Agentic Human–AI Ecosystems ↗
Background The rise of artificial intelligence systems that can make their own goals plan several steps and take initiative in tasks that involve people is changing how students learn math. Earlier technology was mostly a tool thatwaited for instructions. Currently, agentic AI systems are becoming increasingly popular. They share the initiative to negotiate goals and help manage parts of learning with the student. This change raises several questions. How is agency shared? What does this mean for thinking? How good is the math understanding that comes from the long-term collaboration between h…
- OpenAlex · Published · First archived
Shadow AI in organizations: A distribution-based financial risk analysis for governance and secure AI adoption ↗
Shadow artificial intelligence (AI), referring to the unapproved use of AI tools within organizations, is emerging as a governance challenge that extends beyond traditional shadow information technology (IT) by introducing cybersecurity, compliance, and financial risks. As generative AI tools become increasingly accessible in work environments, employees may use such systems outside formal organizational controls, creating hidden exposure pathways that are difficult to monitor and manage. This study proposes the SAIR (Shadow AI Risk) model, a quantitative risk assessment framework that builds…
- OpenAlex · Published · First archived
Artificial intelligence for two-dimensional AEC drawing analysis: Systematic and critical review ↗
Interpretation of two-dimensional architecture, engineering, and construction (AEC) drawings remains labor-intensive, while research is fragmented across methods, datasets, and domains. This paper combines systematic review, bibliometric analysis, and critical synthesis of 105 publications from 2015 to February 5, 2026. Task analysis yielded 162 assignments from 97 studies, with low-level perception accounting for 69.8%, compared with 18.5% for intermediate and 11.7% for high-level tasks. Convolutional neural networks remained dominant, while transformer-based and hybrid methods increased afte…
- OpenAlex · Published · First archived
EFFECT OF AI-DRIVEN ADAPTIVE LEARNING PLATFORM ON SECONDARY SCHOOL STUDENTS' RETENTION IN CHEMISTRY IN ANAMBRA STATE ↗
Abstract This study investigated the effect of an Artificial Intelligence-Driven Adaptive Learning Platform (AI-DALP) on secondary school students’ retention in Chemistry within the Onitsha Education Zone of Anambra State, Nigeria. The study specifically assessed the difference in delayed retention between students taught using AI-DALP and those taught using the Conventional Teaching Method (CTM) after controlling for immediate posttest performance, as well as whether retention differed by gender within the adaptive learning environment. Guided by two research questions and two null hypotheses…
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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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