[HTML][HTML] A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends
A Saranya, R Subhashini - Decision analytics journal, 2023 - Elsevier
Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and
solve common real-world problems. Machine learning and deep learning are Artificial …
solve common real-world problems. Machine learning and deep learning are Artificial …
A review on guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques
The development of structural health monitoring (SHM) techniques is of great importance to
improve the structural efficiency and safety. With advantages of long propagation distances …
improve the structural efficiency and safety. With advantages of long propagation distances …
[HTML][HTML] Computer vision applications for urban planning: A systematic review of opportunities and constraints
Computer vision (CV) technology, a key subset of artificial intelligence, provides powerful
tools for extracting valuable insights from visual data, which is a crucial component for the …
tools for extracting valuable insights from visual data, which is a crucial component for the …
Vision-language models for medical report generation and visual question answering: A review
I Hartsock, G Rasool - Frontiers in Artificial Intelligence, 2024 - frontiersin.org
Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …
Computer vision to advance the sensing and control of built environment towards occupant-centric sustainable development: A critical review
As urban development progresses, the built environment control has faced more critical
challenges in improving energy efficiency, air quality, and environmental comfort. Occupant …
challenges in improving energy efficiency, air quality, and environmental comfort. Occupant …
[HTML][HTML] Machine learning for predicting fatigue properties of additively manufactured materials
Fatigue properties of materials by Additive Manufacturing (AM) depend on many factors
such as AM processing parameter, microstructure, residual stress, surface roughness …
such as AM processing parameter, microstructure, residual stress, surface roughness …
[HTML][HTML] Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions
The demand for clean and sustainable energy solutions is escalating as the global
population grows and economies develop. Fossil fuels, which currently dominate the energy …
population grows and economies develop. Fossil fuels, which currently dominate the energy …
Machine learning-based morphological and mechanical prediction of kirigami-inspired active composites
Kirigami-inspired designs hold great potential for the development of functional materials
and devices, but predicting the morphological configuration of these structures under …
and devices, but predicting the morphological configuration of these structures under …
X-ray diffraction data analysis by machine learning methods—a review
X-ray diffraction (XRD) is a proven, powerful technique for determining the phase
composition, structure, and microstructural features of crystalline materials. The use of …
composition, structure, and microstructural features of crystalline materials. The use of …
A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead
K Zaman, A Marchisio, MA Hanif… - arXiv preprint arXiv …, 2023 - arxiv.org
Quantum Computing (QC) claims to improve the efficiency of solving complex problems,
compared to classical computing. When QC is applied to Machine Learning (ML) …
compared to classical computing. When QC is applied to Machine Learning (ML) …