Results 51 to 60 of about 51,982 (249)

Modern Approaches to Detect and Classify Comment Toxicity Using Neural Networks

Моделирование и анализ информационных систем, 2020
The growth of popularity of online platforms which allow users to communicate with each other, share opinions about various events, and leave comments boosted the development of natural language processing algorithms. Tens of millions of messages per day
Sergey V. Morzhov
doaj   +1 more source

Construction of a Feedback Comment Analysis Model for Evaluation of Endoscopic Surgical Skill

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background Surgical education and skill assessments are important in improving surgical skills. However, instructors' comments tend to be complex and unorganized, with varying content and categories. This study aimed to develop a natural language processing (NLP) model to automatically classify feedback comments on surgical procedures and ...
Shusaku Iwai, Shin Takenaka, Daichi Kitaguchi, Hiroki Matsuzaki, Kei Nakajima, Yuya Takabe, Nobuyoshi Takeshita, Masaaki Ito   +7 more
wiley   +1 more source

Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature

Advanced Intelligent Discovery, EarlyView.
Named entity recognition pipeline for knowledge extraction from scientific literature. Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which propels the
Bowen Zheng, Grace X. Gu
wiley   +1 more source

BATRACIO: BAsic, TRAnslational, Clinical, Research Phase Identification in BiOmedical Publications

IEEE Access
The increasing interest from research agencies, governments, and universities in understanding research funding and prioritising research efforts has highlighted the need for reliable and efficient methods for exploring research portfolios. In biomedical
Nicolau Duran-Silva, Jorge Carrillo-de-Albornoz, Laura Plaza, Sara Ricardo, Francesco A. Massucci, Sonia Veiga, Arnau Ramos-Prats   +6 more
doaj   +1 more source

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 more
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Natural Language Processing and Complex Network based Tourism Social Big Data Analysis [PDF]

SHS Web of Conferences
Unlike traditional research methods such as questionnaire surveys and individual interviews, this article proposes a big data based analytical framework for tourism research.
Yin Lijie
doaj   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Towards Improving Selective Prediction Ability of NLP Systems [PDF]

, 2020
Neeraj Varshney, Swaroop Mishra, Chitta Baral   +2 more
openalex   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source