Results 161 to 170 of about 10,655,437 (309)

Natural Language Processing for Groundwater Insights. [PDF]

Ground Water
Christenson C, McCoy K.
europepmc   +1 more source

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source

Natural language processing to develop a standardized lexicon for precision medicine in oncology. [PDF]

J Manag Care Spec Pharm
Wang T, Tsai K, Kheloussi SS, McCormick D, Pawloski PA.   +4 more
europepmc   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Natural language processing to build a computable phenotype library for adults with congenital heart disease. [PDF]

Int J Med Inform
Thomas S, Dawson A, Chaudhry H, Ahmad S, Ding X, Hummel SA, Leone DM, Vanam R, Weingarten AJ, Farber-Eger E, Shaffer LL, Frischhertz BP, Clemmons SS, Ghelani SJ, Reyes FB, Wu TC, Wu DTY, Opotowsky AR, Miller TA.   +18 more
europepmc   +1 more source

Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction

Advanced Robotics Research, EarlyView.
Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith, Sunny Amatya, Seyed Yousef Soltanian, Jonathan Bush, Wenlong Zhang   +4 more
wiley   +1 more source

Natural Language Processing Framework in Early Detection of Amyloidosis: The ALARM Study. [PDF]

JACC Adv
Altibi AM, Elman MR, Butler J, Arora A, Baldwin N, Priest EL, Masri A.   +6 more
europepmc   +1 more source

VIBE : topic-driven temporal adaptation for twitter classification [PDF]

The 2023 Conference on Empirical Methods in Natural Language Processing, December 6-10, 2023, Singapore202407 bcwhVersion of ...
Li, J, Li, W, Zhang, Y
core  

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

Advanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu   +3 more
wiley   +1 more source

Using natural language processing to track negative emotions in the daily lives of adolescents. [PDF]

J Psychopathol Clin Sci
Fisher H, Jaffe N, Pidvirny K, Tierney AO, Pizzagalli DA, Webb CA.   +5 more
europepmc   +1 more source