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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 ↗5919 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
Predicting cybersecurity knowledge and behavior among Somaliland university students using machine learning ↗
This study assesses cybersecurity knowledge and online behaviors among university students in Somaliland. Data were collected from 1,026 participants through a structured survey to evaluate cybersecurity awareness, digital practices, and factors associated with safe online behavior. Although more than 85% of students recognized the importance of cybersecurity, risky practices were commonly reported, including password reuse (65%), lack of two-factor authentication use (48%), and interaction with suspicious or phishing links (30%). To identify the key determinants of cybersecurity awareness, th…
- OpenAlex · Published · First archived
Low‐Crosstalk Silicon Photonic Switch Fabrics for AI Datacenters and High‐Capacity Optical Interconnects: Device‐Level Origins and Mitigation ↗
ABSTRACT Silicon photonic switch fabrics are increasingly attractive for AI datacenters and high‐capacity optical interconnects, where optical circuit switching is being explored to support bandwidth‐intensive and dynamically reconfigurable connectivity. However, scaling silicon photonic switches to large port counts is fundamentally constrained by accumulated crosstalk, insertion loss, and device‐level non‐idealities. Suppressing crosstalk through additional architectural complexity can, in turn, compromise integration density and scalability. This article establishes a device‐to‐link‐to‐syst…
- OpenAlex · Published · First archived
Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study ↗
Timely detection is crucial to improve outcomes in patients with cardiac amyloidosis (CA) by initiation of guideline-directed disease-modifying treatments. Although confirmatory bone scintigraphy is highly accurate for CA detection, identifying at-risk patients for referral remains challenging. This study aimed to develop and validate a machine learning model, Amylo-Detect , using structured multimodal electronic health record (EHR) data to guide referrals for confirmatory scintigraphy and monoclonal protein testing. Consecutive all-comer patients (n = 11,616) referred for bone scintigraphy at…
- OpenAlex · Published · First archived
Human-centred Artificial Intelligence and Learning Analytics in emerging higher education: a critical scoping review ↗
Artificial Intelligence (AI) and Learning Analytics (LA) are increasingly being adopted by higher education institutions (HEIs) to support student learning, institutional decision-making, and student success. However, most published evidence originates from high-income countries, and comparatively little is known about how these technologies are implemented within the institutional, governance, and socio-cultural contexts of emerging economies. This critical scoping review synthesises the current evidence on AI and LA implementation across emerging higher education systems, with particular att…
- OpenAlex · Published · First archived
Scenario-Based Assessment of Earth-Dam Stability Using Open Geospatial Data and Machine Learning: A Case Study of the Sherubay-Nur Reservoir, Kazakhstan ↗
This article presents an integrated computational framework for scenario-based stability assessment of earth dams using open-access satellite data (Sentinel-2, Landsat 8/9), aerial imagery captured by UAVs, the FABDEM digital elevation model, and machine learning methods. The approach enables water surface detection, reservoir area calculation, water level reconstruction, and subsequent stability assessment via limit equilibrium modelling (Rocscience Slide2D). Tested on the Sherubay-Nur Reservoir (Kazakhstan), the method established a morphometric relationship between water level (534.20–536.8…
- OpenAlex · Published · First archived
A Hybrid Ligand- and Structure-Based Drug Design Invoked through Square Attention in Protein–Ligand Transformer ↗
Abstract Computer-aided molecular design based on evaluation of protein–ligand (PL) binding affinity is important for accelerating drug discovery. A deep-learning model for natural language processing (NLP) involving an attention mechanism (i.e., Transformer) is expected to improve the PL-affinity evaluation along with clarifying amino acid residues and ligand atoms that are responsible for molecular interactions. However, the original attention (i.e., a scaled dot-product with row-wise Softmax normalization) is an adapted method for relationships between homogeneous inputs (e.g., two sentence…
- OpenAlex · Published · First archived
An improved multi-fidelity neural network for surrogate modeling of mechanical responses of irregularly shaped granular materials ↗
High-resolution discrete element method (DEM) simulations are essential for accurately capturing the mechanical behavior of irregularly shaped granular materials; however, their high computational cost limits practical applications in large-scale engineering analyses. Data-driven surrogate models alleviate this burden, while multi-fidelity learning further enhances data efficiency by incorporating inexpensive low-fidelity (LF) simulations. Nevertheless, existing multi-fidelity approaches remain constrained by the limited parameter-space coverage of scarce high-fidelity (HF) samples. This study…
- OpenAlex · Published · First archived
CrashSenseAI: latency-aware edge AI system for real-time traffic accident detection and notification ↗
Road accidents remain a critical public safety issue, where detection often depends on witnesses calling authorities, leading to delays of several minutes before police and ambulances are dispatched. This study presents CrashSenseAI (CSAI), an Edge artificial intelligence (Edge AI) accident detection and notification system built on You Only Look Once version 8 (YOLOv8) and YOLOv12 models. Using a labeled dataset of accident and non-accident images, we trained Nano, Small, and Medium variants with batch sizes of 16 and 32 to evaluate accuracy–latency trade-offs. Results show YOLOv12n achieved…
- OpenAlex · Published · First archived
Classification of high-risk paratransit drivers using ensemble machine learning ↗
Abstract In many developing cities, paratransit is the layer of the transport system that absorbs the demand the formal network cannot serve, including during disruptions, which makes the dependability of this informal fleet a practical component of urban transport resilience. That dependability rests almost entirely on individual drivers, yet driver-level risk in the sector remains poorly characterized by conventional statistical approaches. This study develops and validates machine learning classification models to identify high-risk paratransit drivers in Gazipur, Bangladesh. Data were coll…
- OpenAlex · Published · First archived
CRISPR dependency profiles distinguish pediatric from adult cancer cell lines via explainable machine learning: Identification of GPX4 and proliferative genes as therapeutic targets ↗
Abstract Pediatric and adult cancers diverge profoundly in their molecular architecture, yet whether these differences extend to genome-scale genetic dependencies remains poorly characterized, with prior studies examining single subtypes or individual algorithms rather than a systematic, interpretable comparison. To address this gap, we tested whether Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 loss-of-function dependency profiles can discriminate pediatric from adult cancer cell lines, and applied explainable artificial intelligence (XAI) to identify the genes driv…
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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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