Results 111 to 120 of about 5,431 (217)

Emerging applications of large language models in ecology and conservation science

Conservation Biology, EarlyView.
Abstract Large language models (LLMs) mark a major development in artificial intelligence, with potentially transformative implications for ecology and conservation science. Built on advanced deep‐learning architectures, these models can support a wide range of tasks. We reviewed emerging applications of LLMs, drawing on the wider scientific literature
Christos Mammides, Hao Gu, Thilina S. Nimalrathna, Naufal Rahman Avicena, Harris Papadopoulos, Ahimsa Campos‐Arceiz   +5 more
wiley   +1 more source

A Generic OCR Using Deep Siamese Convolution Neural Networks

, 2019
This paper presents a generic optical character recognition (OCR) system based on deep Siamese convolution neural networks (CNNs) and support vector machines (SVM). Supervised deep CNNs achieve high level of accuracy in classification tasks.
Sokar, Ghada, Hemayed, Elsayed E., Rehan, Mohamed   +2 more
core   +2 more sources

Machine learning approaches in automated infant General Movements Assessment: A scoping review

Developmental Medicine &Child Neurology, EarlyView.
Automated infant General Movements Assessment increasingly uses machine‐ and deep‐learning approaches to classify movement patterns and estimate cerebral palsy risk from video or sensor data. This scoping review highlights how dataset characteristics, recording environment, pose‐estimation accuracy, feature extraction, and model design influence system
Manpreet Kaur, Hamid Abbasi, Sîan A. Williams, Thor F. Besier, Angus J. C. McMorland   +4 more
wiley   +1 more source

Artificial Intelligence for Language Learning: A Systematic Review of its Design, Theoretical Foundations, Implementation, and Impact

International Journal of Applied Linguistics, EarlyView.
ABSTRACT Although the use of AI technologies (e.g., chatbots and automated writing evaluations (AWE)) has gained considerable attention in language learning fields in recent years, how AI technologies have been designed and implemented in language learning education, as well as their effectiveness, is understudied.
Shen Qiao, Michelle Mingyue Gu, Chaoqun Lu   +2 more
wiley   +1 more source

Investigation of English Language Learners’ Attitudes Toward Global Englishes and Global Englishes Language Teaching in Relation to Their Academic Interests and Experiences With English as a Global Lingua Franca

International Journal of Applied Linguistics, EarlyView.
ABSTRACT This study investigates Japanese university students’ attitudes toward Global Englishes (GE) and Global Englishes Language Teaching (GELT), focusing on how these attitudes are shaped by students’ academic interests and experiences using English as a lingua franca (ELF), both abroad and in domestic EFL contexts.
Natsuno Funada, Heath Rose
wiley   +1 more source

The Impact of AI‐Assisted L2 Learning on Learners’ Emotions: A Meta‐Analysis

International Journal of Applied Linguistics, EarlyView.
ABSTRACT The scope of research on AI‐assisted second/foreign language (L2/FL) learning has expanded beyond learning outcomes to focus on learners’ emotional experiences. Despite their proliferation, existing empirical findings are inconsistent, and few studies have systematically synthesized the impacts of AI‐assisted L2 learning on learners’ emotional
Jiaqi Jing, Xinjie Chen, Timothy Teo, Ziwei Xiao, Nandini Agarwal   +4 more
wiley   +1 more source

BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representation. [PDF]

ACS Omega, 2023
Paykan Heyrati M, Ghorbanali Z, Akbari M, Pishgahi G, Zare-Mirakabad F.   +4 more
europepmc   +1 more source

Siamese Hybrid Network Approach for Sentence Similarity

This paper presents a novel Siamese Hybrid Network approach, namely Siamese Bidirectional Long Short Memory with Convolutional Neural Network (SiBiLConv), for evaluating the similarity in natural language.
D.A.A. Deepal, P.R.S. De Silva, A.M.R.R. Bandara   +2 more
core   +1 more source

A Horse Race of Machine‐Learning Methods to Predict Banking Crises

International Finance, EarlyView.
ABSTRACT To examine if one machine‐learning model can consistently elucidate financial vulnerabilities, both over time and across levels of development, this paper applies 13 machine‐learning algorithms to evaluate comparative forecasting performance across several banking crises.
Emile du Plessis
wiley   +1 more source

Facial emotion recognition using deep Siamese neural networks: multi-classifier fusion for single-emotion and multi-emotion models across age groups

Journal of Big Data
This research examines facial expressions as social cues in online platforms, focusing on online learning and remote work. Our high-accuracy emotion recognition framework is designed for post-session evaluation, aiding teaching strategies and identifying
Tejas Rathod, Shruti Patil, Aniket K. Shahade, Prachi Kadam, Ambarish Kulkarni   +4 more
doaj   +1 more source