Interpretable Automated Learning: Evolving Fuzzy Networks to Explicit Applications of Ai

• Published in: 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) pp. 1 - 12
• Main Authors: Rathi, Vikas, Vijayalakshmi, V.J., Mahesh, Bhandari, Ninoria, Shalini, Gupta, Ankur Kumar, Premananthan, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.07.2024
• Subjects: Ambiguity; Artificial intelligence (AI); Biological system modeling; Choices; Computational modeling; Conventional data gathering; Data models; Data-driven schooling; Defuzzification; Ecosystems; Evolving fuzzification; evolving hazy ecosystems; evolving neural nets; Fluid simulation; Fluids; Forecasting; Fuzzification; Insightful conclusions; Learning algorithm; Machine learning; Measurement; Neural networks; System analysis and design
• DOI: 10.1109/ICSCAI61790.2024.10866899

One of the biggest developments in intelligent machines has been the development of evolving fuzzification. They are flexible system designs created using evolving methods. Fluid simulation now has excellent capabilities to use in a variety of data scientific situations as a result of this combination. The goal with this addition is to create a policy document that provides a thorough study of the evolving hazy ecosystems field of study. In order to comprehend the present setting of this subject and its importance, queries are asked and answered with some of this goal in mind. It will be made clear why evolving neural nets are significant from an explicable perspective, even before they started, how much they are utilized for, and where scholars should devote themselves in this field going forward. People must really be crucial to the developing field of data-driven schooling.