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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 ↗5944 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
A Drug-Referenced Machine-Learning Framework for Prioritizing Korean Herbal Formulations in Hypertension Using Linear SVM and Graph Convolutional Networks ↗
Hypertension is regulated by interacting molecular systems, providing a rationale for network-based screening of multicomponent therapeutic candidates. Grounded in the conceptual framework of network pharmacology, we developed a hypertension-focused computational framework to prioritize insurance-covered Korean herbal formulations using protein–protein interaction networks derived from marketed antihypertensive and comparator drugs. Thirty reference-drug graphs (15 antihypertensive and 15 comparator drugs) were evaluated using four molecular classifiers under repeated group-aware nested cross-…
- OpenAlex · Published · First archived
Hybrid Deep-Machine Learning for Butterfly Species Image Classification ↗
Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed u…
- OpenAlex · Published · First archived
Consumer vulnerability in artificial intelligence-driven marketing: A paradox theory lens ↗
Artificial intelligence (AI) is transforming marketing through hyper-personalization, predictive analytics and enhanced customer experience, yet its ethical implications for consumer vulnerability remain undertheorized. We apply a paradox theory lens to identify four tensions in AI-driven marketing: Diversity (included versus excluded), Data (safeguarded versus exploited), Dignity (empowered versus dependent), and Development (informed versus misinformed). We advance a conceptual framework situating these tensions within key boundary conditions (consumer agency, marketer intentionality, and AI…
- OpenAlex · Published · First archived
SentiMatrix: Parameter-Efficient Fine-Tuning of Encoder-Based Transformers for Multidimensional Sentiment Analysis ↗
Abstract Transformer-based architectures have established state-of-the-art benchmarks across Natural Language Processing (NLP) tasks; however, the computational overhead of full fine-tuning remains a significant barrier to scalable deployment. This paper presents SentiMatrix, a systematic evaluation of Low-Rank Adaptation (LoRA)-based Parameter-Efficient Fine-Tuning (PEFT) across four sentiment analysis paradigms: Intent-based, Aspect-based, Fine-grained, and Emotion detection, spanning seven benchmark datasets under a consistent three-stage protocol: inference using task-specific pretrained b…
- OpenAlex · Published · First archived
Examining customer usage intention of AI assisted chatbots in e-commerce market through customer satisfaction and trust ↗
Artificial intelligence (AI) chatbots, powered by large data models, have become an essential tool for improving user experience and are increasingly involved in providing customer assistance and recovering from service failures. This study investigates how AI chatbots are used in the United Kingdom (UK) e-commerce sector, with a particular emphasis on how they affect user engagement and service recovery. Drawing upon Expectation Confirmation Model (ECM) and Technology Acceptance Model (TAM), a research framework is developed to investigate how anthropomorphism cues, perceived ease of use (PEO…
- OpenAlex · Published · First archived
An artificial intelligence driven adaptive learning system for enhancing student engagement and academic achievement in STEM education ↗
Students’ resistance to learning in Science, Technology, Engineering, and Mathematics (STEM) subjects, as well as academic disparities among students and insufficient individualized instructional support in STEM subjects, remain challenges for STEM education. The use of artificial intelligence (AI) to create adaptive learning systems emerges as a promising solution for anticipating learners’ needs and informing timely educational decision-making. This study proposes an adaptive learning system based on AI algorithms for predicting student engagement, forecasting academic performance, recommend…
- OpenAlex · Published · First archived
Machine Learning-Based Prediction and Interpretation of Hot-Tearing Susceptibility in Al–Zn–Mg Alloys for Direct-Chill Casting Process Optimization ↗
Hot tearing limits the yield of Al–Zn–Mg billets produced by direct-chill casting. Its governing factors span composition, cooling conditions and the state of the partially solidified metal, and they interact; several quantities required by physics-based criteria cannot be measured during casting, and the factors are seldom observed together at plant scale. Machine learning was therefore used to establish, from variables that are recorded in production, which factors govern hot tearing when composition and process conditions vary simultaneously. Hot tearing was assessed in 108 pilot-scale bill…
- OpenAlex · Published · First archived
Investigation of Word Recognition Methods for Japanese Fingerspelling Using Large Language Models ↗
The development of automatic fingerspelling recognition systems is critical for facilitating seamless communication for the deaf and hard-of-hearing community. Historically, recognition technologies relied on wearable sensors, such as data gloves, inertial measurement units (IMUs), and surface electromyography (sEMG), which provided stable kinematic data but suffered from high hardware costs and user discomfort. Non-invasive vision-based sensing has emerged to eliminate wearable overhead but is subject to severe physical bottlenecks: continuous high-speed movements inevitably introduce motion…
- OpenAlex · Published · First archived
Representation Control for Large Language Models: Survey and Research Challenges ↗
Large language models (LLMs) are capable of completing a variety of tasks, but remain unpredictable and intractable. Representation Control (RepControl) seeks to resolve this problem through targeted interventions that modify high-level representations of concepts such as honesty, harmfulness or power-seeking. We formalize the goals and methods of RepControl to present a cohesive picture of work in this emerging field, focusing on techniques that steer model behavior by manipulating internal activations at inference time. We compare these control methods with alternative approaches, such as pr…
- OpenAlex · Published · First archived
DR-GEM enables self-supervised machine learning for single-cell embeddings and annotations ↗
Abstract Dimensionality reduction and clustering are instrumental to single-cell and spatial genomics data analysis. Here we show that existing methods tend to fit oversampled classes and miss more unique signals and patterns, such as rare cell types and states. We find that the problem in such cases is not primarily data scarcity per se, but the model equating abundance with importance. Addressing this, we present “Distributionally Robust and latent Group-AwarE consensus Machine learning” or DR-GEM, a self-supervised meta-algorithm that uses the reconstruction error to reorient the model atte…
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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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