Results 151 to 160 of about 30,189 (295)

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 more
wiley   +1 more source

Forecasting Fossil Energy Price Dynamics with Deep Learning: Implications for Global Energy Security and Financial Stability

Algorithms
This study investigates the application of advanced deep learning models to forecast fossil energy prices, a critical factor influencing global economic stability.
Bilal Ahmed Memon
doaj   +1 more source

Research progress on the depth of anesthesia monitoring based on the electroencephalogram

Ibrain, Volume 11, Issue 1, Page 32-43, Spring 2025.
Electroencephalogram (EEG) can noninvasive, continuous, and real‐time monitor the state of brain electrical activity, and the monitoring of EEG can reflect changes in the depth of anesthesia (DOA). The development of artificial intelligence can enable anesthesiologists to extract, analyze, and quantify DOA from complex EEG data.
Xiaolan He, Tingting Li, Xiao Wang
wiley   +1 more source

Solid–Liquid Triboelectric Nanogenerators as Physicochemical Encoders for Intelligent Liquid Recognition

Interdisciplinary Materials, EarlyView.
Solid–liquid triboelectric nanogenerators are conceptualized as dynamic physicochemical encoders that encode intrinsic liquid properties into distinguishable triboelectric fingerprints. This review provides a unified framework for these platforms, covering sensing mechanisms in droplet impact, continuous flow, and immersion modes.
Mingrui Wang, Yuanzhe Liang, Lining Zhang, Tian Tang, Kean C. Aw, Kai Qian, Ziyi Dai, Bingpu Zhou, Lihua Tang   +8 more
wiley   +1 more source

Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes

iMeta, EarlyView.
Nanopore direct RNA sequencing advances transcriptomics by capturing full‐length transcripts and multiple RNA modifications; this review details its principles, workflows, tools, applications, challenges, and future research potential. Abstract Nanopore direct RNA sequencing (DRS) has transformed transcriptomics by enabling single‐molecule, long‐read ...
Tianyuan Zhang, Jia Li, Chao Tang, You Wu, Hao Wu, Xi‐Tong Zhu, Ziyang Luo, Hang Qin, Lishan Ding, Yu Zeng, Shiou Yih Lee, Xiaotao Shen, Shiwen Gao, Zhaoyang Tian, Qian Tang, Mian Li, Muhammad Tahir Ul Qamar, Yang Dong, Komivi Dossa, Yaxuan Zhang, Hu Chen, Sanqi An, Xiang Yu, Lu Chen, Dingjie Wang, Shengli Li, Ling‐Ling Chen, Yanqiang Li   +27 more
wiley   +1 more source

Increasing interpretability using a fuzzy-embedded recurrent neural network (FE-RNN) with its application in stock ETF trading

, 2021
Deep learning has been a recent breakthrough that has enabled predictions and modelling to be very accurate. These predictions and modelling tools were once used to help us understand our data and serve as a tool to make a judgement.
Tan, James Chee Min
core  

Application of Principal Component Analysis and Probabilistic Neural Networks in Ferralsols Recovery Evaluation Through Planting of Mabea Fistulifera and Eucalyptus Urograndis

Land Degradation &Development, EarlyView.
ABSTRACT This study presents an innovative assessment model for analyzing the evolution of degraded soils subjected to different reclamation strategies. The proposal combines statistical and artificial intelligence tools to jointly integrate multiple physical and chemical soil properties, allowing for a more synthetic view of the processes.
Melissa Alexandre Santos, Borja Velázquez‐Martí, Jorge João Delfim, Laura Silva Nantes, Jose Augusto Liberato de Souza, Gabriel Augusto da Silva Lunardelli, Dayara Vivian Alvares, Carolina dos Santos Batista Bonini   +7 more
wiley   +1 more source

A Review of Advances in Composite Materials, Structural Optimization, and Machine Learning for Wind Turbine Blades: Challenges and Future Perspectives

Polymer Composites, EarlyView.
Overview of the holistic engineering lifecycle and core research pillars for wind turbine blades. ABSTRACT This paper reviews recent advancements across the lifecycle of wind turbine blades, focusing on three interconnected areas: advanced composites, structural optimization, and machine learning (ML) diagnostics. In materials, we highlight progress in
Kemal Hasirci, Denizhan Yavas, Alaeddin Burak Irez   +2 more
wiley   +1 more source

Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN). [PDF]

Health Inf Sci Syst, 2019
Moitra D, Mandal RK.
europepmc   +1 more source

Artificial intelligence, equity, and pediatric neurodevelopmental disorders: A scoping review of clinical practice applications

Pediatric Investigation, EarlyView.
Artificial intelligence (AI) is being explored to support diagnosis and care for pediatric neurodevelopmental disorders, yet most tools remain in early stages of development. This scoping review identifies limited external validation, narrow population representation, and sparse equity considerations, underscoring the need for inclusive, clinically ...
Florida Uzoaru, Obinna O. Oleribe, Ucheoma Nwaozuru, Parichart Sabado   +3 more
wiley   +1 more source