Fractional Order Moment

Fractional order moments are a generalization of traditional statistical moments, offering a flexible tool for characterizing probability distributions and signals, particularly in the presence of noise or uncertainty. Current research focuses on applying fractional moments in diverse fields, including improving the accuracy of differentially private data release, enhancing visual gyroscope performance through image analysis, and developing more robust and interpretable deep learning models for image processing and segmentation. This expanding area of research holds significant promise for advancing uncertainty quantification, improving machine learning algorithms, and enabling more accurate and efficient analysis of complex data in various scientific and engineering applications.