Results 61 to 70 of about 125,940 (271)

"Possible Definitions of an ’A Priori’ Granule\ud in General Rough Set Theory" by A. Mani [PDF]

, 2009
We introduce an abstract framework for general rough set theory from a mereological perspective and consider possible concepts of ’a priori’ granules and granulation in the same.
A, MANI
core  

Artificial Intelligence‐Guided Total Mesorectal Excision: Development of a Deep Learning Model to Identify the Pelvic Fascial Plane Anatomy

Annals of Gastroenterological Surgery, EarlyView.
AI‐based segmentation of fascial planes during total mesorectal excision. Ground truth and predicted boundaries are compared for mesorectal fascia (blue), parietal pelvic fascia (red), and the holy plane (white). ABSTRACT Aim Total mesorectal excision entails dissection of the “holy plane” without damaging the mesorectal and parietal pelvic fasciae. We
Yuta Suzuki, Daichi Kitaguchi, Norihito Kosugi, Hiro Hasegawa, Nobuyoshi Takeshita, Yusuke Kinugasa, Masaaki Ito   +6 more
wiley   +1 more source

Knowledge Engineering in Search Engines [PDF]

, 2012
With large amounts of information being exchanged on the Internet, search engines have become the most popular tools for helping users to search and filter this information.
Lin, Yun-Chieh
core   +1 more source

In Situ Graph Reasoning and Knowledge Expansion Using Graph‐PRefLexOR

Advanced Intelligent Discovery, EarlyView.
Graph‐PRefLexOR is a novel framework that enhances language models with in situ graph reasoning, symbolic abstraction, and recursive refinement. By integrating graph‐based representations into generative tasks, the approach enables interpretable, multistep reasoning.
Markus J. Buehler
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
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

The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers

Advanced Intelligent Discovery, EarlyView.
We assess the maturity and integration readiness of key methodologies for Materials Acceleration Platforms, highlighting the need for dynamic workflow managers. Demonstrating this, we integrate PerQueue into a color‐mixing robot, showing how flexible orchestration improves coordination and optimization.
Simon K. Steensen, Patrick W. V. Butler, Jesper R. Pedersen, Nis Fisker‐Bødker, Tejs Vegge, Jin Hyun Chang, Ivano E. Castelli   +6 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +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

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source