• Maheshwari
• Sapna Thakur

Mathematical techniques known as generalized fuzzy information measures are employed to quantify fuzziness, ambiguity, or uncertainty in data that is represented by fuzzy sets. These measures play a crucial role in fuzzy logic, fuzzy systems, and fuzzy set theory, as they provide a means to analyse and manipulate uncertain or imprecise information. The development of generalized fuzzy information measures has been an active area of research, aiming to extend and refine existing measures to better capture the complexities of fuzzy data. The article discusses fuzzy information measures and their generalizations, adopting both objective and descriptive methodologies. Applications for generalized fuzzy information measures can be found in several domains, including the analysis of images, data mining, machine learning, recognition of patterns, and decision-making. These measures provide a means to handle imprecise and uncertain information in real-world datasets, where traditional crisp methods may be inadequate. It covers problem-solving and problem-posing methods, along with fundamental ideas, providing a summary of various fuzzy entropy measures and their generalizations, emphasizing their importance for the advancement of fuzzy information theory.

Fuzzy Entropy/ Uncertainty/ Fuzziness/Measures of Information.

"An Overview on The Development of Generalized Fuzzy Information Measures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.113-119, November-2024, Available :http://www.jetir.org/papers/JETIRGP06013.pdf

"An Overview on The Development of Generalized Fuzzy Information Measures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp113-119, November-2024, Available at : http://www.jetir.org/papers/JETIRGP06013.pdf