Results 131 to 140 of about 176,660 (282)

Hybrid FSO/RF Networks with Neural Prediction of RSSI and Weather

Acta Electrotechnica et Informatica
This paper investigates neural network models for predicting weather parameters and received signal strength indicator (RSSI) to enable adaptive handover in hybrid free space optics (FSO)/radio frequency (RF) systems.
Liščinská Zuzana, Ovseník L’uboš, Oravec Jakub   +2 more
doaj   +1 more source

Energy Consumption and CO2 Emissions Forecasting of Transport Sector Using Machine Learning

Energy Science &Engineering, EarlyView.
The transport sector accounts for approximately one‐quarter of Iran's final energy consumption. The energy demand in this sector has the least variation, with petroleum products accounting for more than 85% of the demand. Furthermore, the accelerated growth of energy consumption and the sector's reliance on fossil fuels, which are the main cause of ...
Amir Hossein Akbari, Seyyed Mohammad Hashemi, Meisam Farajollahi   +2 more
wiley   +1 more source

Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now

Journal of Forecasting, EarlyView.
ABSTRACT We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante.
Beomseok Seo, Hyungbae Cho, Dongjae Lee
wiley   +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

Deep learning‐based prediction of cervical lymph node metastasis and genetic alterations from whole‐slide images of thyroid cancer frozen sections

Interdisciplinary Medicine, EarlyView.
Deep learning models accurately predict cervical lymph node metastasis and key genetic mutations (BRAF/TERT) directly from thyroid cancer frozen sections. This AI‐driven pipeline provides a rapid real‐time tool to guide intraoperative surgical decisions, helping to optimize surgical extent and prevent both over‐ and under‐treatment without the need for
Mingxing Qiu, Jinbang Li, Shujing Guo, Danyi Li, Chengyu Lu, Chenglong Zhao, Jiankun He, Hanxi Wang, Qiao Bai, Aihetaimu Aimaier, Zhijian Cen, Rui Mao, Zimo Ye, Jiaxue Zang, Zhining Zhuo, Lijuan Qu, Yueping Liu, Jing Cui, Xiaohong Yao, Xiuwu Bian, Li Liang   +20 more
wiley   +1 more source

Residual Neural Network Compensation of Temperature‐Induced Gamma Shift in OLED Display

Journal of the Society for Information Display, EarlyView.
The figure summarizes the proposed white‐only sensing framework and highlights its key advantages in improving color accuracy and reducing calibration time ABSTRACT Organic light‐emitting diode (OLED) displays exhibit temperature‐induced chromatic shifts and luminance‐dependent nonlinear responses, causing CIEDE2000 color difference (ΔE) and local ...
Ching‐Hsiang Hsu, Mang Ou‐Yang
wiley   +1 more source

Attention as an RNN

The advent of Transformers marked a significant breakthrough in sequence modelling, providing a highly performant architecture capable of leveraging GPU parallelism. However, Transformers are computationally expensive at inference time, limiting their applications, particularly in low-resource settings (e.g., mobile and embedded devices).
Feng, Leo, Tung, Frederick, Hajimirsadeghi, Hossein, Ahmed, Mohamed Osama, Bengio, Yoshua, Mori, Greg   +5 more
openaire   +2 more sources

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

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation

Organ Medicine, EarlyView.
This review evaluates how machine learning, multimodal integration, and generative AI optimize kidney transplant outcomes. These tools enable superior prediction and personalized therapy but face hurdles in data volume, generalizability, and ethics. Future clinical adoption depends on continued innovation and multidisciplinary collaboration to overcome
Maoxin Liao, Cheng Yang
wiley   +1 more source