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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 ↗5376 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 Cloud Platform for Smart City Traffic Monitoring and Analysis Using Multi-Dimensional Big Data and Deep Learning Models ↗
Rapid urbanization and persistent road-safety challenges have increased the demand for intelligent traffic management systems capable of supporting traffic monitoring, incident analysis, and short-term forecasting. Existing artificial intelligence solutions frequently address these functions through separate, task-specific applications, making it difficult for traffic operators to access heterogeneous analytical services within a common operational environment. This study presents a cloud-based, multi-source traffic intelligence platform that incorporates independent analytical services for CC…
- OpenAlex · Published · First archived
Time-dependent rheology of seawater-mixed cement paste: Role of binder characteristics revealed by experiments and machine learning ↗
This study investigates how the characteristics of fly ash (FA), ground granulated blast-furnace slag (GGBS), and silica fume (SF) affect the time-dependent rheology of seawater-mixed cement pastes. Single-, binary-, and ternary-blended binders were prepared at a fixed water-to-binder ratio of 0.40. Fluidity, dynamic yield stress, and plastic viscosity were measured at 5, 35, and 65 min, together with water film thickness (WFT), wet packing density, and early hydration heat. FA and GGBS generally reduced dynamic yield stress, whereas SF increased both dynamic yield stress and plastic viscosity…
- OpenAlex · Published · First archived
Temporally Robust Machine-Learning-Assisted Impedance Line-Shape Analysis for Quantitative QCM Biosensing with DNA Hybridization Validation ↗
Liquid-phase quartz crystal microbalance (QCM) biosensors are commonly quantified using scalar readouts such as frequency shift, dissipation, or bandwidth. Although useful, these descriptors discard resonance line-shape morphology that carries additional information for quantitative inference. Here, we present a machine-learning-assisted impedance line-shape workflow for temporally robust quantitative 10 MHz QCM biosensing. A passive microfluidic mixer generated controlled glycerol–water concentration gradients under constant total flow while complex impedance spectra were acquired in real tim…
- OpenAlex · Published · First archived
Advanced In Vitro Platforms for Experimental Liver Research: From Organoids and Liver‐on‐Chip to Omics and Artificial Intelligence ↗
Hepatocytes perform essential physiological functions in the human body. Gaining more profound insight into the liver structure, diseases, and physiology by using in vitro models can help to propose more relevant clinical applications. Conventional in vitro liver models, such as two-dimensional (2D) cell cultures, fail to adequately replicate the hepatic microenvironment, even when improved by adding additional cell types and extracellular matrix (ECM). These models do not address all necessary key components to mimic in vivo conditions, particularly multicellularity, specific ECM, and proper…
- OpenAlex · Published · First archived
A Limit of AI, I: A Discrete Information-Theoretic Proof of the Certainty–Scope Conjecture ↗
Abstract Floridi’s Certainty–Scope Conjecture holds that no artificial intelligence system can be simultaneously reliable on every input and broad enough to cover the full richness of unstructured data: reliability and breadth are in structural tension, imposed by the finite information-processing capacity of any mechanism implementing intelligence. The Conjecture has until now been a philosophical thesis without a proof. This article supplies one in the finite-classification regime—the simplest non-trivial setting in which the Conjecture admits an exact statement, a sharp converse, and a matc…
- OpenAlex · Published · First archived
Foundation Models and Vision-Language Models in Medical Imaging: A Review ↗
Foundation models (FMs) and vision-language models (VLMs) have emerged as a transformative paradigm in artificial intelligence, reshaping the landscape of medical image analysis and clinical decision support. Building upon large-scale pretraining on heterogeneous datasets, these models exhibit unprecedented generalization and zero-shot transfer capabilities, effectively overcoming the perennial bottleneck of annotated data scarcity that has historically constrained deep learning applications in healthcare. This review paper presents the state of the art in FMs and VLMs applied to medical imagi…
- OpenAlex · Published · First archived
Decoding Biofilm Complexity: Artificial Intelligence, Advanced Imaging, and Multi-Omics for Precision Antibiofilm Strategies ↗
Microbial biofilms are highly structured multicellular communities that exhibit remarkable tolerance to antimicrobial agents and host defenses, contributing to persistent infections, industrial biofouling, food contamination, and environmental challenges. Conventional microbiological approaches have greatly advanced our understanding of biofilm formation but remain insufficient to resolve the spatial, molecular, and functional heterogeneity that underpins biofilm resilience. This review discusses how recent advances in high-resolution imaging, multi-omics technologies, single-cell and spatiall…
- OpenAlex · Published · First archived
Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach ↗
Hollyhock (Alcea rosea) is an important ornamental and medicinal plant. This study aimed to evaluate the genetic diversity and population structure of 28 endemic genotypes collected from various regions of Türkiye using an integrated approach combining morphological, biochemical, and molecular markers. Significant morphological variation was observed, and image analysis techniques were employed to classify flower and leaf colors. Biochemical assays revealed substantial diversity in bioactive compound levels, with total phenolic content ranging from 0.18 to 4.65 mg GAE/kg. Antioxidant activitie…
- OpenAlex · Published · First archived
AI-Based Network Intrusion Detection System Using Machine Learning and Explainable AI ↗
Network intrusion detection is an important cybersecurity function because network traffic can contain benign activity as well as malicious behavior. This paper presents an AI-based Network Intrusion Detection System (AI-NIDS) that combines live packet capture, flow generation, machine-learning classification, explainable artificial intelligence, and web-based visualization. The prototype is implemented in an Ubuntu virtual machine hosted through VirtualBox. Scapy captures traffic visible on the virtual network interface, and related packets are grouped into flows. Four features that can be de…
- OpenAlex · Published · First archived
ReViT-RF: A hybrid ResNet-50–Vision Transformer with a Radiomic Feature Fusion framework for lung tumor classification and TNM-based stage prediction using CT images ↗
Lung cancer is one of the leading causes of cancer-related mortality worldwide, emphasizing the need for accurate, automated, and clinically interpretable diagnostic systems. Although existing deep learning (DL) models have achieved promising performance in classifying lung nodules as benign or malignant, most approaches are limited to binary classification and provide insufficient support for detailed tumor characterization and TNM staging. Furthermore, conventional convolutional neural networks (CNNs) are effective in extracting local morphological features but often fail to capture long-ran…
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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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