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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 ↗5986 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
DeepShield-IoT: A Hybrid AI and Lotka–Volterra Model for Efficient IoT Intrusion Detection Systems ↗
Internet of Things devices introduce significant security challenges caused by their heterogeneous and resource-constrained nature in many sectors, such as healthcare, industry, education, and agriculture. Intrusion detection systems (IDSs) serve a key role in identifying malicious activities in such environments; traditional approaches cannot often capture dynamic interactions and temporal correlations in network traffic. In this research, we propose a novel hybrid IDS technique utilizing Long Short-Term Memory (LSTM) networks in conjunction with Lotka–Volterra (LV) dynamic modeling. The LSTM…
- OpenAlex · Published · First archived
Accelerating ligand screening for tuberculosis: predicting binding energies with a 3D deep neural network ↗
Abstract The computational burden of molecular docking becomes prohibitive when receptor flexibility is explicitly considered, despite the accuracy gained in virtual screening workflows. To mitigate this limitation, we introduce a three-dimensional deep neural network designed to estimate binding energies before exhaustive docking across the complete fully flexible receptor ensemble. We target the InhA enzyme, a validated target of Mycobacterium tuberculosis , through the use of a fully flexible receptor model (with 20,000 poses) generated by computational simulation. The model leverages volum…
- OpenAlex · Published · First archived
Generative AI-Driven Discovery and Experimental Validation of a Novel EGFR Inhibitor for the Treatment of Pancreatic Cancer ↗
Pancreatic cancer remains a highly lethal malignancy with limited targeted therapeutic options, necessitating the development of effective EGFR-directed inhibitors. In this study, an integrated generative artificial intelligence (AI)-driven computational–experimental workflow was developed to identify a novel EGFR inhibitor, using a cannabigerol-derived scaffold as the initial seed. ConfGAN, variational autoencoders, generative adversarial networks, and reinforcement learning were employed to explore a chemical space exceeding 1.7 million compounds, followed by progressive prioritization using…
- OpenAlex · Published · First archived
AI-Empowered Urban Digital Twins: Integrating Geospatial and Legal Data for Spatial Planning and Governance ↗
Urban planning increasingly requires accessible ways to connect regulatory frameworks with clear spatial visualisation and machine-assisted guidance. However, the integration of legal and geospatial information into platforms that support decision-making, communication, and participation remains limited. In particular, local authorities lack an operational way to translate legal and regulatory provisions into spatially explicit permit systems, and existing Urban Digital Twin architectures rarely formalise this legal-geospatial link. This paper uses Urban Digital Twins (UDTs) and Artificial Int…
- OpenAlex · Published · First archived
Screening High-Efficiency Deep Eutectic Solvents for Oxidative-Extractive Desulfurization via a Machine Learning Framework to Analyze Intermolecular Interactions ↗
Oxidative-extractive desulfurization (OEDS) represents a highly efficient pathway for removing recalcitrant thiophenic sulfides from liquid fuels. Deep eutectic solvents (DESs) have emerged as promising green extractants due to their tunable multi-site non-covalent interactions and favorable environmental profile. However, exploring the vast chemical space of potential DES formulations demands rational, data-driven methodologies to identify molecular affinities while bypassing costly experimental trial-and-error. Herein, we report a novel hybrid algorithm termed PDI-DMPNN+SME. By coupling solv…
- OpenAlex · Published · First archived
Ensemble Shields: Optimizing DDoS Detection in IoT Networks Using Machine Learning ↗
DDoS (Distributed Denial-of-Service) attacks are significant cyber threats that affect network infrastructures and cause massive service disruption. DDoS attacks have become even more threatening, owing to the poor security of IoT devices. This paper proposes two ensemble approaches: (1) Optimized Ensemble Stacking Approach 4 (OESA-4) and (2) Optimized Ensemble Voting Approach 4 (OEVA-4). Both proposed approaches use four supervised machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGB), and Decision Tree (DT). OESA-4 combines the b…
- OpenAlex · Published · First archived
Non-Contact Thermometry of Silicon via Broadband Supercontinuum and Machine Learning ↗
A new approach to non-contact picosecond thermometry in silicon, which combines broadband supercontinuum spectroscopy with machine learning, is presented. The technique reconstructs temperature from absorption spectra with a mean absolute error below 0.8% and sub-nanosecond temporal resolution. Comprehensive evaluation of neural network architectures identifies an optimal configuration featuring an initial one-dimensional convolutional layer followed by residual blocks, which substantially outperforms the classical approximation approach and fully connected networks. The model demonstrates rob…
- OpenAlex · Published · First archived
Mitigating Hallucinations in Finance-Based Multi-Agent Large Language Model Systems ↗
Large language models are increasingly used for financial question answering, while they are prone to generating hallucinated content. In this research, we propose a multi-signal framework for hallucination detection and mitigation. Our framework combines six signals (entailment, semantic similarity, claim verification, numeric consistency, token overlap and confidence calibration techniques) to identify hallucination type and severity. We evaluated our framework using multiple language models across three financial QA benchmark datasets (FinQA, FinanceQA, and FinDER). Our proposed framework c…
- OpenAlex · Published · First archived
Design implications from research trends in generative AI for mathematics learning in higher education: A bibliometric study ↗
This study investigates the development and thematic evolution of research on generative artificial intelligence (AI) in higher-education mathematics through a bibliometric analysis of 256 Scopus-indexed publications from 1985 to 2025. Utilizing VOSviewer and Bibliometrix, the analysis examines publication and citation trends, research collaboration, influential contributors, and thematic structures within the field. The findings demonstrate a substantial increase in both publication volume and citation frequency, particularly in recent years, indicating heightened scholarly interest in genera…
- OpenAlex · Published · First archived
Artificial Intelligence-Guided Extracellular Vesicle Platforms for Diagnosis and Treatment of Chronic Kidney Disease: A Scoping Review and Future Directions ↗
Background and Objectives: Chronic kidney disease (CKD) affects roughly 788 million adults worldwide according to the Global Burden of Disease (GBD) 2023 estimate. Extracellular vesicles (EVs) are promising sources of biomarkers and therapeutic carriers, while artificial intelligence (AI) offers tools for high-dimensional EV analysis and computational design. Materials and Methods: We conducted a scoping review using a structured PubMed/MEDLINE search, run on 7 September 2026, and PRISMA-ScR reporting principles. Eligible studies applied AI/ML to EV-derived or closely related kidney-disease da…
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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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