Results 91 to 100 of about 214,358 (295)
Financial Markets Analysis by Probabilistic Fuzzy Modelling [PDF]
For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions.
Berg, J. van den +2 more
core +1 more source
A STOCHASTIC SIMULATION-BASED HYBRID INTERVAL FUZZY PROGRAMMING APPROACH FOR OPTIMIZING THE TREATMENT OF RECOVERED OILY WATER [PDF]
, 2012 In this paper, a stochastic simulation-based hybrid interval fuzzy programming (SHIFP) approach is developed to aid the decision-making process by solving fuzzy linear optimization problems.
Chen, Bing +3 more
core
, 2012 We use princiles of fuzzy logic to develop a general model representing several processes in a system's operation characterized by a degree of vagueness and/or uncertainy.
Voskoglou, Michael Gr.
core +1 more source
Advanced Intelligent Discovery, EarlyView.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
Journal of Applied Mathematics, 2015 In project management context, time management is one of the most important factors affecting project success. This paper proposes a new method to solve research project scheduling problems (RPSP) containing Fuzzy Graphical Evaluation and Review ...
Gholamreza Norouzi +3 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source
An Overview of Classifier Fusion Methods [PDF]
, 2000 A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance.
Gabrys, Bogdan, Ruta, Dymitr
core
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Conditional Probabilities and Fuzzy Entropy
Journal of Japan Society for Fuzzy Theory and Systems, 1990 Let \((\Omega, S, P)\) be a probability space and \(A \in S\) an event with its indicator function \(1_ A\). It is well-known that the conditional probability with respect to a subfield \(G \subset S\) satisfies \(P(A \mid G) = 1_ A\) if \(A \in G\) and that \(P(A \mid G)\) is a general function on [0,1] if \(A\) is not measurable with respect to \(G\).
openaire +3 more sources
NORMAL FUZZY PROBABILITY FOR TRAPEZOIDAL FUZZY SETS
East Asian mathematical journal, 2013 Abstract. A fuzzy set A de ned on a probability space (;F;P) is calleda fuzzy event. Zadeh de nes the probability of the fuzzy event A usingthe probability P. We de ne the normal fuzzy probability on R usingthe normal distribution. We calculate the normal fuzzy probability forgeneralized trapezoidal fuzzy sets and give some examples.
Changil Kim, Yong Sik Yun
openaire +2 more sources