Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Evaluating digital technologies for architectural heritage preservation using interval-valued q-rung orthopair fuzzy aggregation operators. [PDF]
Wang L.
europepmc +1 more source
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source
Event-triggered fuzzy neural-network PID control for nonlinear gas-blending processes. [PDF]
Dong W, Wang S, Zhang Z.
europepmc +1 more source
Bio-Inspired Reactive Approaches for Automated Guided Vehicle Path Planning: A Review. [PDF]
Lin S, Wang J, Kong X.
europepmc +1 more source
Risk Analysis of Real Estate Project Based on Fuzzy Comprehensive Evaluation
openaire +1 more source
Path tracking control method for tracked agricultural vehicles based on slip-aware look-ahead point offset. [PDF]
Liu H +6 more
europepmc +1 more source
A novel algorithm for model uncertainty reduction in trapezoidal fuzzy fault tree risk assessment. [PDF]
Zhang Y, Zhao L, Zhang T, Li W.
europepmc +1 more source
Enhancing smart energy load management in industrial steel production using the fermatean fuzzy FUCA multi-criteria analysis. [PDF]
Zhao D, Jiang M.
europepmc +1 more source
An intelligent framework combining deep learning and fuzzy logic for accurate remote language translation. [PDF]
Geng C.
europepmc +1 more source

