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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
NephroTox AI: an explainable machine learning framework with uncertainty quantification for drug-induced nephrotoxicity prediction ↗
Drug-induced kidney injury remains a major cause of clinical-stage drug attrition, yet computational nephrotoxicity prediction is hampered by fragmented datasets, opaque models lacking mechanistic interpretability, the absence of uncertainty quantification, and the lack of publicly available prediction platforms. Here, we curated a dataset of 2,157 compounds from three sources with deduplication and label harmonization, and benchmarked 28 model–descriptor combinations (7 algorithms × 4 descriptor types) to identify Random Forest with RDKit 2D descriptors as the best performer (validation AUC 9…
- OpenAlex · Published · First archived
Design and analysis of Adders for Next Gen Processors Using AI ↗
Abstract—Optimizing the arithmetic operators is extremely important when designing a fast-processing architecture.Carry Look-Ahead (CLA) adders are commonly used while designing Arithmetic Logic Units (ALUs) because of theirfast-processing capability. This paper introduces a new optimized design of CLA using the Gate Diffusion Input (GDI)concept combined with XOR function which uses fewer power in comparison to the conventional static CMOS design.Furthermore, the paper also proposes an AI/ML predictive model using the Python programming language for theprediction of power characteristics of CL…
- OpenAlex · Published · First archived
A critical assessment of the rise of agentic AI, its capabilities, task domains and open challenges ↗
The tremendous advancement in Large Language Models (LLMs) has triggered a fundamental reorientation in AI: from basic, single-prompt-based systems to agentic systems that can perceive the world, break down problems into multiple steps, use external tools, and continuously revise the generated response through a long-range dialogue. Although such a revolution is driven by four major technologies: instruction-following models, structured tool APIs, retrieval-augmented generation, and multi-agent orchestration systems, no holistic survey of this emergent field from a task-oriented unified point…
- OpenAlex · Published · First archived
Enhanced Swin Transformer Incorporating Multi-Head Self-Attention and Feature Refinement for Fetal Trimester Classification ↗
Background Analyzing fetal ultrasound images is important for estimating gestational age and classifying trimesters, which support fetal growth monitoring and clinical decision-making. Ultrasound imaging is widely used due to its safety, affordability, and non-invasive nature. However, manual interpretation is subject to inter-observer variability and requires significant clinical expertise, limiting its consistency, particularly in resource-constrained settings. Methods This study proposes an enhanced Swin Transformer-based framework for automated fetal trimester classification. The architect…
- OpenAlex · Published · First archived
LLM-agent-based urban trajectory prediction for behavior-aware and sustainable mobility modeling under geographical constraints ↗
Understanding and modeling urban mobility behaviors are essential for sustainable transportation planning and intelligent mobility management. However, existing trajectory prediction approaches largely rely on historical mobility patterns and task-specific training, limiting their ability to capture the contextual reasoning, heterogeneous behaviors, and adaptive decision-making processes underlying traveler movements. To address this gap, this study proposes TransAgent, an interpretable LLM-agent framework that leverages the semantic reasoning and contextual understanding capabilities of Large…
- OpenAlex · Published · First archived
Machine Learning-Based Electric Vehicle Remaining Range Prediction: Assessing the Relative Impact of Battery Status, Driving Context, and Environmental Conditions ↗
This study presents a comprehensive machine learning (ML) evaluation methodology, grounded in physical principles, for range estimation in battery-electric vehicles (BEVs). In a simulation environment consisting of 50 driving and 12 virtual vehicles, 2000 observations were generated by classifying 23 input variables into three main categories: battery and energy status (SOC, voltage, current, battery power, rolling energy consumption, SOH, battery temperature, regenerative power), driving and route context (speed, acceleration, cumulative distance, route gradient, traffic density, driving styl…
- OpenAlex · Published · First archived
Transferable artificial intelligence for viable but non-culturable foodborne pathogen detection: opportunities, challenges, and future directions ↗
ABSTRACT The viable but non-culturable (VBNC) state enables foodborne pathogens to evade culture-based detection while retaining virulence and resuscitation capacity. This minireview examines the intersection of artificial intelligence and VBNC research. We survey transferable artificial intelligence (AI) methods from adjacent microbiology domains, identify the scarcity of direct AI-VBNC studies and its root causes, and delineate critical challenges, including data scarcity, standardization, and model interpretability. We also construct a transferability framework mapping existing AI tools to…
- OpenAlex · Published · First archived
From Saliency Maps to Circuits: An Integrative Survey of Explainable AI and Mechanistic Interpretability ↗
Artificial intelligence (AI) models, particularly those based on deep learning, have achieved strong performance across a wide range of real-world applications. However, as models increase in scale and complexity, their behavior and internal decision-making processes become increasingly difficult to understand. Explainable artificial intelligence (XAI) has emerged to address this challenge by developing methods for making model predictions and internal mechanisms more transparent and intelligible. Despite substantial progress, the XAI landscape remains difficult to navigate due to methodologic…
- OpenAlex · Published · First archived
Explainable machine learning maps the efficiency-NOx trade-off of a hydrogen dual-fuel engine fuelled with a diesel–Mahua methyl ester-pine oil blend ↗
Abstract Hydrogen-enriched dual-fuel combustion offers a route to decarbonising compression-ignition (CI) engines, but its interaction with oxygenated ternary fuels remains poorly quantified. This study evaluates a Span 80-stabilised diesel-Mahua methyl ester (MME)-pine oil (PO) blend on a Kirloskar TV1 CI engine (5.2 kW, 1500 rpm, compression ratio 17.5:1). Twenty operating points for D70 + MME15 + PO15 were measured across five loads (20–100%) and four hydrogen rates (0, 5, 10 and 15 L min −1 , equivalent to a 0–15.4% hydrogen energy share at full load); the remaining sixty points of the des…
- OpenAlex · Published · First archived
Benchmarking Generative AI Models for Cybersecurity Vulnerability Identification Using 50 Curated Vulnerability Cases: An Exploratory Single-Pass Comparative Study of ChatGPT, Gemini, and Claude ↗
LLMs are being rapidly examined for cybersecurity applications, including identifying software vulnerabilities, conducting secure code reviews, and performing security analysis. Despite recent developments in Generative Artificial Intelligence (GenAI), there is a lack of empirical evidence on the relative performance of state-of-the-art commercial LLMs on discovering and categorizing software vulnerabilities. In this paper, we benchmarked the cybersecurity vulnerability-identification capabilities of ChatGPT, Gemini, and Claude on a dataset of fifty curated vulnerability cases from various Com…
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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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