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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
Independent evaluation of machine learning and deep learning models for breast cancer detection ↗
Breast cancer is one of the leading causes of cancer-related mortality worldwide, and despite advances in clinical diagnosis, challenges such as inconclusive imaging results and inter-observer variability highlight the need for complementary computational approaches to support early and accurate detection. In this study, we provide an overview of several machine learning (ML) and deep learning (DL) algorithms used for breast cancer detection with structured clinical data and imaging data. We applied ML models to structured clinical data which included Wisconsin Breast Cancer Dataset, CSAW-CC d…
- OpenAlex · Published · First archived
Intelligent Agents Based on Large Language Models in Transport Management: A Comparative Analysis of Approaches to Development and Implementation ↗
The paper addresses the “information gap” between objective telemetry data and primary documentation in logistics and road construction, which arises from the growing volume of unstructured information and a shortage of qualified personnel. Two projects aimed at creating intelligent agents to bridge this gap in transport management are examined. Based on a comparative analysis of two implemented solutions, the study identifies common architectural principles. The first project (technical proposal stage) develops an AI‑powered dispatcher assistant that automatically collects and matches request…
- OpenAlex · Published · First archived
Fine-GrainedEnergy Assessment of Vision Transformer Operators on Hybrid SoC Platforms: A CPU, iGPU, and NPU Case Study ↗
As CPUs alone are often insufficiently efficient for running Vision Transformers (ViTs), contemporary computing platforms, particularly edge systems, increasingly adopt heterogeneous architectures that combine CPUs with GPUs and Neural Processing Units (NPUs). This paper presents a detailed energy assessment of individual ViT functions running on a modern hybrid processor, the AMD Ryzen AI 9 HX 370, which features a CPU, an integrated GPU (iGPU), and a dedicated Neural Processing Unit (NPU). Unlike macro-level benchmarks, our analysis takes a fine-grained approach by focusing on the most widel…
- OpenAlex · Published · First archived
ARTIFICIAL INTELLIGENCE-ENHANCED LIQUID BIOPSY IN CANCER MONITORING: FROM TREATMENT RESPONSE TO EARLY DETECTION OF DISEASE RECURRENCE ↗
Liquid biopsy has emerged as an important minimally invasive technology in contemporary oncology, enabling repeated assessment of tumor-derived molecular information through blood and other biological fluids. Circulating tumor DNA (ctDNA), circulating tumor cells, cell-free DNA methylation patterns, extracellular vesicles, circulating RNA and fragmentomic signatures may provide dynamic information about tumor burden, treatment response, molecular residual disease and cancer recurrence. However, liquid biopsy generates complex, multidimensional datasets in which tumor-derived signals may be ext…
- OpenAlex · Published · First archived
Ontology-Driven Agents Skills for Selection of Building Datasets with various specifications ↗
Selecting the correct dataset among Taiwan's nine building specifications requires knowledge of production lineage, legal basis, completeness constraints, and spatial-unit semantics that are rarely represented in machine-readable form, leaving large language model (LLM) agents prone to structurally unsuitable recommendations. This study proposes a framework that integrates OWL ontology reasoning with an agent skills architecture to address this gap. A four-dimensional attribute framework first characterizes the nine datasets across data identification, spatiotemporal characteristics, productio…
- OpenAlex · Published · First archived
Hybrid Bi-LSTM and Deep Reinforcement Learning for AI-Native Predictive Mobility Management in High-Mobility 5G/6G Networks ↗
Reliable mobility management is critical for high-mobility 5G/6G applications such as smart cities, intelligent transportation systems (ITS), and connected vehicles, where reactive 3GPP handover mechanisms become unreliable as user equipment (UE) velocity increases. This paper proposes a hybrid Bidirectional LSTM (Bi-LSTM) and Deep Reinforcement Learning (DRL) framework for AI-native predictive handover management: a Bi-LSTM encoder extracts a knowledge embedding and forecasts future network observations, forming a predictive state that a Dueling Double DQN (Dueling DDQN) with prioritized expe…
- OpenAlex · Published · First archived
A tri-axial MFL GADF representation and axis-weighted Swin transformer approach for pipeline defect identification ↗
Magnetic flux leakage (MFL) measurements contain coupled directional responses that are affected by defect morphology, magnetisation conditions, sensor position, and measurement noise. Consequently, conventional handcrafted-feature methods and single-axis deep-learning models may not fully exploit the complementary information contained in tri-axial MFL signals. This study proposes AW-SwinT-Cal, a pipeline-defect classification framework combining per-axis Gramian Angular Difference Field (GADF) encoding, a shared Swin Transformer backbone, global axis-weighted feature fusion, test-time augmen…
- OpenAlex · Published · First archived
Investigating The Relationship Between Hand Sketch Quality and AI-Assisted Concept Render Quality in Product Design ↗
Abstract Sketching is seen as a key part of the early-stage design process, enabling designers to communicate conceptual ideas to one another and to external stakeholders. The introduction of image-generative AI opens up new possibilities for AI-assisted sketching, which may be a useful tool for designers. In the present study, undergraduate product design students received introductory training in an image-generative AI tool, Vizcom, which uses a multimodal text- and sketch-based input to create more photorealistic rendered outputs. The purpose of the study was to characterize the relationshi…
- OpenAlex · Published · First archived
Toward Sustainable AI in Digital Financial Services: A Hybrid Data-Grounded Architecture for Reducing Generative Model Dependency ↗
AI assistants are increasingly integrated into digital financial services; however, relying on generative language models for response generation can introduce challenges related to response reliability, data exposure, and resource requirements. This study presents a hybrid data-grounded architecture that selectively uses generative AI while maintaining reliable natural language interaction. The proposed task-oriented system combines semantic intent matching, slot and entity extraction, reconciliation, and response routing. Instead of using the generative model to describe every response, the…
- OpenAlex · Published · First archived
A multidimensional trustworthiness evaluation framework for AI-enabled clinical decision support: a HER2 breast cancer case study ↗
Abstract Machine learning models are increasingly being incorporated into AI-enabled decision-support systems, particularly in high-stakes domains such as healthcare. However, evaluation of such systems continues to rely predominantly on predictive performance metrics, which provide only a partial assessment of whether an AI-based decision-support system can be considered trustworthy for consequential decision-making. Recent advances in trustworthy artificial intelligence (AI) emphasize that characteristics such as explainability, reliability, robustness, and domain relevance should also be co…
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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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