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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 ↗5965 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
Data-driven and feature-sifting based state-of-health estimation for li-ion batteries using KPCA and NRBO-optimized transformer-LSTM hybrid model ↗
Electrochemical energy storage technologies, such as lithium-ion batteries, are widely used in various scenarios, but their capacity, lifespan, and state-of-health (SOH) are susceptible to numerous influencing factors. To improve the accuracy of state-of-health (SOH) prediction for lithium-ion battery cells, this paper builds up a feature-based analysis framework, and proposes an enhanced predicting model, which integrates the principles of incremental capacity analysis (ICA) and differential voltage analysis (DVA), and the ideas of kernel principal component analysis (KPCA), and Pearson corre…
- OpenAlex · Published · First archived
Predictive modeling of urban residential power consumption: an explainable AI approach to the Kenyan prepaid grid ↗
Accurate electricity demand forecasting is important for utility planning and energy management, particularly in rapidly urbanizing regions where prepaid metering plays an increasingly important role in residential electricity service delivery. Because prepaid residential demand exhibits strong temporal persistence and distinct behavioural characteristics, forecasting approaches developed in other electricity-market settings may not transfer directly. This study develops an explainable forecasting framework for the Kenyan prepaid electricity sector to assess whether machine-learning complexity…
- OpenAlex · Published · First archived
Examining the role of a structured AI-supported intervention in developing EFL students’ academic writing skills in Mozambican higher education ↗
Abstract Artificial Intelligence (AI) is reshaping educational practices and redefining how academic writing is taught, learned, and assessed. This study explores the role of a structured AI-supported writing intervention on English as a Foreign Language (EFL) students’ academic writing performance and examines lecturers’ awareness of, and alignment with, emerging AI trends in a Mozambican public university. Employing an Exploratory Sequential Case Study-Mixed Methods design, data were collected through documents analyses, interviews, an intervention, and observations. Participants included ei…
- OpenAlex · Published · First archived
Integrating Experimental Durability Assessment and Explainable Artificial Intelligence for Sustainable UHPC in Smart Construction Applications ↗
Digitalisation and explainable artificial intelligence (XAI) will reshape sustainable materials engineering by enabling data-driven design and performance prediction for smart infrastructure. In this study, experimental durability assessment and machine learning techniques were combined to investigate silica fume (SF) and ground granulated blast-furnace slag (GGBFS)-based UHPC subjected to normal curing (NC), steam curing (SC), and wrapped curing (WC). Mechanical, durability, and mineralogical characterisation tests were performed, including compressive strength, the Rapid Chloride Penetration…
- OpenAlex · Published · First archived
Graphene-enhanced dual-gate electro-optic micro-ring modulator: An optical neural network design ↗
To improve integrated photonic systems for optical computing applications, the ability to control both the amplitude and phase of microring modulator transmission is a key capability for advanced photonic integrated circuits. In this work, we present a novel dual-gate microring modulator that integrates a Z-shaped silicon PN junction with a graphene overlay, offering enhanced electro-optic tunability beyond conventional single-gate designs. The device consists of two electrically coupled active regions, one PN junction and the other incorporating graphene, allowing modulation of both the real…
- OpenAlex · Published · First archived
Research on English grammatical structure parsing method based on transformer and GCN ↗
English grammatical structure parsing is a core task in natural language processing, playing a crucial role in machine translation, information extraction, and other applications. This paper proposes an innovative model, TG-Parser, that combines a Transformer and a graph convolutional network (GCN). It significantly improves the accuracy of English grammar parsing by combining the global semantic modeling capabilities of the Transformer and the structural relationship capture advantages of the GCN. First, a word-level context representation is obtained using a Transformer encoder; second, a fu…
- OpenAlex · Published · First archived
ChipGuard-AI: A Hardware-Assisted Unified Framework for Adversarial and Side-Channel Resilience in Consumer Electronics ↗
Consumer electronics powered by on-device artificial intelligence face a structurally coupled dual threat: adversarial attacks that manipulate model outputs, and side-channel attacks that exploit physical implementation characteristics to extract cryptographic keys and neural network weights. Prior work addresses these threats independently, leaving exploitable gaps when both attack vectors are applied in a coordinated manner. This paper presents ChipGuard-AI, a hardware-assisted unified security framework that co-designs adversarial input detection, multi-modal side-channel countermeasures, a…
- OpenAlex · Published · First archived
A Review of Performance of Machine Learning and Deep Learning Models in Multiscale Spam Detection:An Analysis of Feature Engineering Impact Across Email, SMS, and Social Media ↗
The increase in spam in email, mobile (SMS), and social media networks requires the constant review of effective detection methods. This paper is a comparative evaluation of the classical Machine Learning (ML) models (Naive Bayes, Logistic Regression, SVM) and the models aimed at advanced Deep Learning (DL LSTM, BERT) and Ensemble Learning models, in terms of their performance in three different communication platforms. The most important element of this study is the analysis of feature engineering methods, such as regular TF-IDF and the use of own vocabulary-based vectorization. Findings have…
- OpenAlex · Published · First archived
THE ROLE OF ARTIFICIAL INTELLIGENCE IN STUDENTS' ENGLISH LANGUAGE LEARNING ↗
This article examines the role of artificial intelligence (AI) in improving students’ English language learning. The rapid development of digital technologies has created new opportunities for foreign language education, particularly through AI-based applications, intelligent tutoring systems, chatbots, automated writing assistants, speech-recognition tools, and adaptive learning platforms. The study discusses how these technologies can support the development of students’ speaking, writing, reading, listening, vocabulary, and grammar skills. Particular attention is given to personalized learn…
- OpenAlex · Published · First archived
Vulnerabilities and Defenses in Audio-Visual Attacks: A Survey From Audio to Multimodal Models ↗
With the rapid development of multimodal large language models (MLLMs) and the cost of large amounts of data and resources, researchers are more inclined to use public datasets and fine-tune open-source MLLMs to achieve excellent performance in audiovisual related tasks. While this trend accelerates progress, it also introduces security risks. Empirical studies demonstrate that the latest MLLMs can be manipulated to generate harmful content through crafted inputs, including adversarial perturbations and malicious queries, bypassing internal safeguards. To gain a deeper comprehension of the inh…
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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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