Incorporating sparse model machine learning in designing cultural heritage landscapes
Managing, protecting, and the evolutionary development of historical landscapes require
robust frameworks and processes for forming datasets and advanced decision support tools …
robust frameworks and processes for forming datasets and advanced decision support tools …
Survey of transfer learning approaches in the machine learning of digital health sensing data
L Chato, E Regentova - Journal of Personalized Medicine, 2023 - mdpi.com
Machine learning and digital health sensing data have led to numerous research
achievements aimed at improving digital health technology. However, using machine …
achievements aimed at improving digital health technology. However, using machine …
[HTML][HTML] Revolutionary integration of artificial intelligence with meta-optics-focus on metalenses for imaging
NL Kazanskiy, SN Khonina, IV Oseledets… - Technologies, 2024 - mdpi.com
Artificial intelligence (AI) significantly enhances the development of Meta-Optics (MOs),
which encompasses advanced optical components like metalenses and metasurfaces …
which encompasses advanced optical components like metalenses and metasurfaces …
Unveiling the influence of artificial intelligence and machine learning on financial markets: A comprehensive analysis of AI applications in trading, risk management …
This study explores the adoption and impact of artificial intelligence (AI) and machine
learning (ML) in financial markets, utilizing a mixed-methods approach that includes a …
learning (ML) in financial markets, utilizing a mixed-methods approach that includes a …
Pathogen-based classification of plant diseases: A deep transfer learning approach for intelligent support systems
KPA Rani, S Gowrishankar - IEEE Access, 2023 - ieeexplore.ieee.org
The national economy's key pillar, agriculture has a significant influence on society. Plant
health monitoring and disease detection are essential for sustainable agriculture. To protect …
health monitoring and disease detection are essential for sustainable agriculture. To protect …
Machine learning for heavy metal removal from water: recent advances and challenges
Research on the removal of heavy metals (HMs) from contaminated waters, aiming at
ensuring the safety of water bodies, has shifted from direct experimental tests to machine …
ensuring the safety of water bodies, has shifted from direct experimental tests to machine …
Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks
Implicit solvent models are essential for molecular dynamics simulations of biomolecules,
striking a balance between computational efficiency and biological realism. Efforts are …
striking a balance between computational efficiency and biological realism. Efforts are …
Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata
Abstract Background Sexually transmitted infections (STIs) pose a significant global public
health challenge. Early diagnosis and treatment reduce STI transmission, but rely on …
health challenge. Early diagnosis and treatment reduce STI transmission, but rely on …
Emotion recognition from spatio-temporal representation of EEG signals via 3D-CNN with ensemble learning techniques
The recognition of emotions is one of the most challenging issues in human–computer
interaction (HCI). EEG signals are widely adopted as a method for recognizing emotions …
interaction (HCI). EEG signals are widely adopted as a method for recognizing emotions …
[HTML][HTML] Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning
Solar forecasting from ground-based sky images has shown great promise in reducing the
uncertainty in solar power generation. With more and more sky image datasets available in …
uncertainty in solar power generation. With more and more sky image datasets available in …