Results 161 to 170 of about 212,585 (307)

Facial expression recognition for emotion perception: A comprehensive science mapping

Ibrain, EarlyView.
Facial expression recognition (FER) has emerged as a pivotal interdisciplinary research domain, bridging computer science, psychology, neuroscience, and medicine. By mapping the FER scientific knowledge graph, the study aimed to explore the technological evolution and forecast future application trends in this field.
Hou‐Ming Kan, Li‐Ping Chen, Yu Zhang, Hao‐Yuan Hong, Ying‐Ying Qin, Yu‐Guo Cui, Yu‐Bo Mao, Yan‐Zhi Cheng, Zhe Lu, Hong‐Yan Ni, Xiao‐Tong Ding   +10 more
wiley   +1 more source

Hydrogen Halide Gas Sensors: Active Materials, Operation Principles, and Emerging Technologies

Interdisciplinary Materials, EarlyView.
This review considers hydrogen halide (HX) gas sensors across functional materials and principles: acoustic, chemical, optical and nanophotonic. The strong acidity and reactivity of HX gases are discussed as constraints for stability and selectivity of these devices.
Xiuzhen Liu, Nikita Tukachev, Kehang Wang, Dmirty Zhirihin, Yaopeng Cheng, Pavel Krasnov, Mingzhao Song, Soslan Khubezhov, Wenping Yin, Emmanuel Stratakis, Sailing He, Andrey L. Rogach, Sergey Makarov   +12 more
wiley   +1 more source

Using Deep Learning Conditional Value‐at‐Risk Based Utility Function in Cryptocurrency Portfolio Optimisation

International Journal of Finance &Economics, EarlyView.
ABSTRACT One of the critical risks associated with cryptocurrency assets is the so‐called downside risk, or tail risk. Conditional Value‐at‐Risk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio.
Xinran Huang, Linzhi Tan, Haozhe Su, Jeremy Eng‐Tuck Cheah   +3 more
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

From Reactive to Proactive Volatility Modeling With Hemisphere Neural Networks

Journal of Applied Econometrics, EarlyView.
ABSTRACT We revisit maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context.
Philippe Goulet Coulombe, Mikael Frenette, Karin Klieber   +2 more
wiley   +1 more source

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Microscopy Research and Technique, EarlyView.
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel, Budheswar Dehury, Nirmal Mazumder   +5 more
wiley   +1 more source

Optical Coherence Tomography for High‐Precision Industrial Inspection in Industry 4.0: Advances, Challenges, and Future Trends

Laser &Photonics Reviews, EarlyView.
This review examines how optical coherence tomography transforms industrial inspection by delivering real‐time, micrometer‐resolution, depth‐resolved imaging. It surveys applications across display manufacturing, thin films, microelectronics, laser processing, and coatings, evaluates performance against conventional techniques, and highlights emerging ...
Nipun Shantha Kahatapitiya, Sm Abu Saleah, Daewoon Seong, Naresh Kumar Ravichandran, Sangyeob Han, Ruchire Eranga Wijesinghe, Mansik Jeon, Jeehyun Kim   +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

A critical review of RNN and LSTM variants in hydrological time series predictions

MethodsX
Muhammad Waqas, U. Humphries
semanticscholar   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source