Chrome Extension
WeChat Mini Program
Use on ChatGLM

Novel Fractional Inequalities Measured by Prabhakar Fuzzy Fractional Operators Pertaining to Fuzzy Convexities and Preinvexities

AIMS Mathematics(2024)

Univ Sargodha | Univ Lahore | King Faisal Univ | 4. Department of Mathematics

Cited 0|Views13
Abstract
In this article, we implemented the idea of a fuzzy interval-valued function with the well-known generalized fuzzy fractional operators, associated with different types of convexities and preinvexities. We developed the Prabhakar fuzzy fractional operators using the fuzzy interval-valued function. We presented the novel extensions of Hermite-Hadamard fuzzy-type and trapezoidal fuzzy-type inequalities, based on the $ h $-Godunova-Levin convex and $ h $-Godunova preinvex fuzzy interval-valued functions.
More
Translated text
Key words
fuzzy fractional integral,fuzzy interval-valued function,preinvex function,fuzzy convexity,Hermite-Hadamard inequality
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出了基于Prabhakar模糊分数算子的新型分数不等式,扩展了Hermite-Hadamard和梯形模糊不等式,涉及模糊凸性和预凸性。

方法】:通过结合模糊区间值函数与广义模糊分数算子,并考虑不同的凸性和预凸性类型,发展了Prabhakar模糊分数算子。

实验】:文中未具体描述实验过程,未提及使用的数据集名称和实验结果。