Results 21 to 30 of about 1,103 (177)
Data Augmentation Methods for Enhancing Robustness in Text Classification Tasks
Algorithms, 2023 Text classification is widely studied in natural language processing (NLP). Deep learning models, including large pre-trained models like BERT and DistilBERT, have achieved impressive results in text classification tasks.
Huidong Tang +2 more
doaj +1 more source
Радіоелектронні і комп'ютерні системиThis study investigates the implementation of different sentiment analysis models, exploring their theoretical foundations, robust evaluation criteria, and significant findings, and integrating natural language processing methods to preprocess data ...
Chaimaa Benyamani +3 more
doaj +1 more source
Frontiers in Digital Health, 2022 BackgroundWhile message-based therapy has been shown to be effective in treating a range of mood disorders, it is critical to ensure that providers are meeting a consistently high standard of care over this medium.
James M. Zech +5 more
doaj +1 more source
Few-Shot Learning for Classifying Genuine and Bot Comments on YouTube Using Transformer Models
Journal of Applied Informatics and ComputingThis study aims to develop a comment classification system on the YouTube platform to distinguish between real accounts and bot accounts, addressing the challenge of limited labeled data through a few-shot learning approach.
Nahdah Fikriah Nst +2 more
doaj +1 more source
Integrating multimodal data and machine learning for entrepreneurship research
Strategic Entrepreneurship Journal, EarlyView.Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley +1 more source
Baselining Large Language Model Performance in Systems Engineering Using SysEngBench
Systems Engineering, EarlyView.ABSTRACT In the rapidly evolving field of artificial intelligence (AI), large language model s (LLMs) have demonstrated impressive capabilities in generating natural language. However, their proficiency in specialized domains, particularly in the field of systems engineering (SE), remains less explored and unquantified.
Ryan Bell +3 more
wiley +1 more source
Düzce Üniversitesi Bilim ve Teknoloji DergisiThe rapid proliferation of digital news sources today necessitates the effective analysis and classification of large-scale textual data. In this study, BERT (Bidirectional Encoder Representations from Transformers) and its derivatives — DistilBERT ...
Ahmet Albayrak +2 more
doaj +1 more source
Transformer‐Based Contextual Modeling for Predicting Calories From Recipes
Applied AI Letters, Volume 7, Issue 3, October 2026.A transformer‐based regression model with token‐level attention pooling is proposed for predicting calorie content directly from unstructured recipe text. By fine‐tuning RoBERTa in an end‐to‐end manner, attention is learned to be focused on calorie‐relevant tokens such as ingredients, fats, and cooking methods.
Md. Siam Ansary, Amina Brinto
wiley +1 more source
Foundations of Computing and Decision SciencesThe functional requirements (FRs) classification in software requirements classification (SRC) is a difficult task due to class imbalance, fine-grained subcategories, and semantic complexities. Existing Machine Learning (ML) and Deep Learning (DL) models
Kiani Azaz Ahmed
doaj +1 more source
Comparison of Online Gambling Promotion Detection Performance Using DistilBERT and DeBERTa Models
Journal of Applied Informatics and ComputingOnline gambling promotions on social media have become a serious concern in Indonesia, where perpetrators use ambiguous and disguised language to evade detection. This study compares two transformer-based models, DistilBERT and DeBERTa, in detecting such
Halim Meliana Pratama +2 more
doaj +1 more source