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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 ↗5927 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 Decision-Centered Survey of Machine Learning for Routing in Ad Hoc Networks: MANETs, VANETs, and FANETs ↗
Machine learning is rapidly transforming routing research across mobile, vehicular, and flying ad hoc networks (MANETs, VANETs, and FANETs). However, cross-study comparison remains difficult because individual works focus on disparate objectives within inconsistent simulation environments. The existing reviews are often organized by algorithmic families, which include Q-learning, deep Q-networks, convolutional and graph networks, and multi-agent reinforcement learning. Consequently, such reviews clearly discuss the model used in a paper yet blur the object of study: the same algorithm may act…
- OpenAlex · Published · First archived
Dynamic Estimation of Representative Backsheet Temperature for Rooftop Photovoltaic Modules Under Extreme Heat: Thermal Inertia, Temporal Resolution, and Machine Learning ↗
Accurate module-temperature estimation supports assessment of rooftop photovoltaic (PV) systems, but minute-scale meteorological fluctuations and thermal memory challenge steady-state models. This study used 44,638 synchronized one-minute records from a rooftop PV platform in Chongqing, China, during August 2025. The representative backsheet temperature was the arithmetic mean of five simultaneously valid sensors. Physical, static XGBoost, and thermal-history-aware XGBoost models were compared. Lagged values, strictly prior rolling means, and increments were constructed uniformly for four mete…
- OpenAlex · Published · First archived
Feature Extraction Algorithm for Person Re-Identification Based on Artificial Intelligence ↗
Re-IDentification (Re-ID) of persons is significant in intelligent surveillance and security systems, where the consistency of feature extraction and prediction processes is essential to ensuring correct identity matches. Illumination, camera view, and visual quality differences often cause noise in pseudo-labels in unsupervised Person Re-ID tasks, which undermines the overall learning performance and enhances the quality of the image before feature extraction, creating a gap in the process that impacts the reliability of representations and prediction accuracy. An artificial intelligence (AI)…
- OpenAlex · Published · First archived
Ensemble transformer-based framework for predicting finger angles ↗
Abstract More than 50% of individuals with upper-limb amputation reject the use of prosthetic devices, primarily due to limitations in functionality, comfort, and intuitive control. Accurate estimation of finger joint angles is a key requirement for proportional prosthetic control, as it enables the generation of continuous and natural hand movements that more closely mimic biological hand function. However, real-time prediction of finger joint angles remains challenging because of the nonlinear relationship between electromyographic signals and joint motion, as well as the computational const…
- OpenAlex · Published · First archived
Clinical value of artificial intelligence computer-aided diagnosis system in lymph node ultrasonography ↗
This article explores the clinical value of the artificial intelligence computer-aided diagnosis system in the diagnosis of superficial lymph nodes. In this study, characteristic ultrasonic parameters of 185 cases of lymph nodes (including 110 benign and 75 malignant) confirmed by pathology were quantified by the superficial lymphadenopathy computer-aided diagnosis system, including margin, appearance, medulla proportion, medulla distribution, echogenicity, echogeneity, vascular density, and vascular pattern. A nonparametric rank sum test was used between the parameters. The sensitivity, speci…
- OpenAlex · Published · First archived
Advancing Biosensing Through Artificial Intelligence: Overcoming Conventional Limitations Towards Intelligent Diagnostic Systems ↗
Artificial intelligence (AI) is increasingly integrated with biosensors to improve signal processing, feature extraction, calibration, and clinical interpretation. However, analytical sensitivity and high internal validation accuracy do not necessarily translate into clinically useful or generalisable diagnostic performance. This review therefore reframes AI-enabled biosensing around clinical actionability rather than technological performance alone. We critically examine how AI can address signal noise, drift, matrix effects, and complex or high-dimensional biosensor outputs while evaluating…
- OpenAlex · Published · First archived
Automated and Explainable Machine Learning Framework for Predicting the Fracture Toughness of Fiber-Reinforced Ultra-High-Performance Concrete Subjected to Freeze–Thaw Damage ↗
The extended longevity of fiber-reinforced ultra-high-performance concrete (FR-UHPC) in frigid regions remains incompletely comprehended, especially concerning the deterioration of mode-I fracture toughness (KIC) due to repeated freeze–thaw cycles (FTCs). While prior investigations have predominantly concentrated on traditional mechanical attributes such as compressive, tensile, and flexural strengths, there is a scarcity of predictive frameworks that can precisely simulate the intricate nonlinear interactions between mixture composition, fiber features, porosity, and freeze–thaw damage impact…
- OpenAlex · Published · First archived
Predicting Angle Beam Ultrasonic Inspection Signals with a Physics-Informed Neural Network ↗
Simulation of angle beam ultrasonic inspection supports procedure qualification, probe design, and defect detectability studies, but conventional solvers require a conforming mesh for each configuration and return the solution only at mesh nodes and discrete time steps. Physics-Informed Neural Networks (PINNs) have been applied to ultrasonic wave propagation, but existing formulations solve the wave equation within the propagation medium alone and impose the excitation as a prescribed surface source. The quantity an inspector actually measures; the voltage returned at the transducer terminal;…
- OpenAlex · Published · First archived
Conflict-aware commonsense knowledge distillation with hypergraph reasoning for robust multimodal sentiment analysis ↗
Existing graph-structured multimodal sentiment analysis (MSA) methods generally rely on heuristic topology construction and shallow statistical correlation modeling, making them susceptible to spurious semantic cues and modality bias under implicit sarcasm, semantic reversal, and cross-modal conflict scenarios. To address these limitations, this paper proposes CKD-MHR, a robust framework based on conflict-aware commonsense knowledge distillation and population manifold hypergraph reasoning. Specifically, the framework leverages a multimodal large language model (MLLM) to construct a commonsens…
- OpenAlex · Published · First archived
Machine Learning and Deep Learning for Early Disease Detection Across Modalities: A Comprehensive Review of Methods, Applications, and Clinical Translation ↗
Early disease detection is not a single classification problem. It includes population screening, presymptomatic risk estimation, identification of early pathological change, and detection of disease before conventional clinical recognition. Machine learning and deep learning systems approach these targets through medical images, physiological signals, longitudinal health records, molecular measurements, wearable sensors, and multimodal combinations. This survey organizes the evidence around the complete decision pathway linking the clinical target, data generation process, preprocessing, repr…
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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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