Results 51 to 60 of about 329 (133)

Deep Neural Networks for Refining Vertical Modeling of Global Tropospheric Delay

Geophysical Research Letters, Volume 52, Issue 2, 28 January 2025.
Abstract Kinematic airborne platforms are becoming increasingly vital for Earth observation. They highlight the critical need for accurate tropospheric delay corrections across varying altitudes, especially as most existing models are limited to Earth's surface.
Peng Yuan, Kyriakos Balidakis, Jungang Wang, Pengfei Xia, Jian Wang, Mingyuan Zhang, Weiping Jiang, Harald Schuh, Jens Wickert, Zhiguo Deng   +9 more
wiley   +1 more source

How the technologies behind self‐driving cars, social networks, ChatGPT, and DALL‐E2 are changing structural biology

BioEssays, Volume 47, Issue 1, January 2025.
Deep learning, a brain inspired field of computer science, revolutionizes structural biology. I summarize how convolutional neural networks (CNNs), large language models (LLMs), denoising diffusion probabilistic models (DDPMs)/Noise conditional score networks (NCSNs), and graph neural networks (GNNs) have impacted protein structure prediction, inverse ...
Matthias Bochtler
wiley   +1 more source

An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors [PDF]

Geoscientific Model Development
Atmospheric pressure and zenith wet delay (ZWD) are essential for global navigation satellite system (GNSS) tropospheric correction and precipitable water vapor (PWV) retrieval. As the development progresses of real-time GNSS kinematic technology, moving
C. Jiang, C. Jiang, C. Jiang, X. Gao, H. Zhu, S. Wang, S. Liu, S. Chen, G. Liu   +8 more
doaj   +1 more source

Utilizing large language models for EFL essay grading: An examination of reliability and validity in rubric‐based assessments

British Journal of Educational Technology, Volume 56, Issue 1, Page 150-166, January 2025.
Abstract This study investigates the validity and reliability of generative large language models (LLMs), specifically ChatGPT and Google's Bard, in grading student essays in higher education based on an analytical grading rubric. A total of 15 experienced English as a foreign language (EFL) instructors and two LLMs were asked to evaluate three student
Fatih Yavuz, Özgür Çelik, Gamze Yavaş Çelik   +2 more
wiley   +1 more source

Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain, Volume 4, Issue S1, Page 706-724, December 2024.
We propose the conceptual model called PrivChatGPT, a privacy‐preserving model for LLMs that consists of two main components, that is, preserving user privacy during the data curation/pre‐processing together with preserving private context and the private training process for large‐scale data.
Imdad Ullah, Najm Hassan, Sukhpal Singh Gill, Basem Suleiman, Tariq Ahamed Ahanger, Zawar Shah, Junaid Qadir, Salil S. Kanhere   +7 more
wiley   +1 more source

Be Sure to Use the Same Writing Style: Applying Authorship Verification on Large-Language-Model-Generated Texts

Applied Sciences
Recently, there have been significant advances and wide-scale use of generative AI in natural language generation. Models such as OpenAI’s GPT3 and Meta’s LLaMA are widely used in chatbots, to summarize documents, and to generate creative content.
Janith Weerasinghe, Ovendra Seepersaud, Genesis Smothers, Julia Jose, Rachel Greenstadt   +4 more
doaj   +1 more source

Comparative analysis of paraphrasing performance of ChatGPT, GPT‐3, and T5 language models using a new ChatGPT generated dataset: ParaGPT

Expert Systems, Volume 41, Issue 11, November 2024.
Abstract Paraphrase generation is a fundamental natural language processing (NLP) task that refers to the process of generating a well‐formed and coherent output sentence that exhibits both syntactic and/or lexical diversity from the input sentence, while simultaneously ensuring that the semantic similarity between the two sentences is preserved ...
Meltem Kurt Pehlivanoğlu, Robera Tadesse Gobosho, Muhammad Abdan Syakura, Vimal Shanmuganathan, Luis de‐la‐Fuente‐Valentín   +4 more
wiley   +1 more source

GFZTD: A Multimodal Fusion-Driven 3-D Tropospheric Delay Prediction Model Coupling Self-Attention and ConvLSTM

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Tropospheric delay, for which water vapor is a major cause, is a significant source of error in the global navigation satellite system. This article presents the gray figure-based zenith tropospheric delay prediction (GFZTD) model, which is built on ...
Yixin Zhu, Zhimin Sha, Pengzhi Wei, Shirong Ye, Pengfei Xia, Fangxin Hu   +5 more
doaj   +1 more source

Higher accuracy estimation of the weighted mean temperature (tm) with the aid of machine learning and NWP model

All Earth
An accurate calculation of weighted mean temperature (Tm) is highly critical for global navigation satellite system (GNSS) based precipitable water vapour retrieval.
Mingyun Hu, Junyu Li, Chaolong Yao, Feijuan Li, Lilong Liu, Liangke Huang, Yuxin Wang   +6 more
doaj   +1 more source

A Self-Supervised Uncertainty-Hybrid Machine Learning Approach for Modeling Tropospheric Zenith Wet Delay

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Zenith wet delay (ZWD) is one of the major sources of errors in the Global Navigation Satellite System (GNSS) positioning. Existing machine learning (ML) based ZWD models have limitations in their generalization capabilities and in modeling the high ...
Mohamed H. Sharouda, Weixing Zhang, Zhixiang Mo, Mohamed M. Elisy, Hongxing Sun, Mohamed Hosny, Yidong Lou   +6 more
doaj   +1 more source