A Generalized Measure of Cumulative Residual Entropy

Kattumannil, Sudheesh K.; Sreedevi, E. P.

doi:10.20944/preprints202202.0322.v1

2022

DOI: 10.20944/preprints202202.0322.v1

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A Generalized Measure of Cumulative Residual Entropy

Sudheesh K. Kattumannil¹,

E. P. Sreedevi²

Abstract: In this work, we introduce a generalized measure of cumulative residual entropy and study its properties. We show that several existing measures of entropy such as cumulative residual entropy, weighted cumulative residual entropy and cumulative residual Tsallis entropy, are all special cases of the generalized cumulative residual entropy. We also propose a measure of generalized cumulative entropy, which includes cumulative entropy, weighted cumulative entropy and cumulative Tsallis entropy as special cases. W… Show more

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Cited by 3 publications

References 31 publications

(52 reference statements)

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YOLO Recognition Method for Tea Shoots Based on Polariser Filtering and LFAnet

Peng,

Zhang,

Xian

et al. 2024

Agronomy

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This study proposes a YOLOv5 inspection model based on polariser filtering (PF) to improve the recognition accuracy of the machine vision inspection model for tea leaf shoots when operating under intense outdoor light. To study the influence of the polariser parameters on the quality of the tea shoot image datasets, we improved the YOLOv5 algorithm module, inputted the results obtained from the spatial pyramid pooling structure in the backbone module into the neck module, set the up-sampling link of the neck module as a low-level feature alignment (LFA) structure, and used a bounding box similarity comparison metric based on the minimum point distance (mpdiou) to improve the accuracy of the YOLOv5 detection model. The mpdiou loss function is used to replace the original loss function. Experimental results show that the proposed method can effectively address the impact of intense outdoor light on tea identification, effectively solving the problem of poor detection accuracy of tea buds in the top view state. In the same identification environment, the model mAP50 value increased by 3.3% compared to that of the existing best mainstream detection model, and the mAP50-90 increased by 3.1%. Under an environment of light intensity greater than 5×104 Lux, the proposed YOLOv5s+LFA+mpdiou+PF model reduced the leakage detection rate by 35% and false detection rate by 10% compared to that with YOLOv5s alone.

show abstract

YOLO Recognition Method for Tea Shoots Based on Polariser Filtering and LFAnet

Peng,

Zhang,

Xian

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

Weighted Mean Inactivity Time Function with Applications

Crescenzo

Toomaj

2022

Mathematics

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We consider an extension of the mean inactivity time based on a non-negative weight function. We show various properties of the new notion, and relate it to various functions of interest in reliability theory and information measures, such as the dynamic cumulative entropy, the past entropy, the varentropy, and the weighted cumulative entropy. Moreover, based on the comparison of weighted mean inactivity times, we introduce and study a new stochastic order and compare it with other suitable orders. We also discuss some results about the variance of transformed random variables and the weighted generalized cumulative entropy. Then, we investigate certain connections with the location-independent riskier order. Finally, we pinpoint several characterizations and preservation properties of the new stochastic order under shock models, random maxima, and notions of renewal theory.

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Sharma–Taneja–Mittal Entropy and Its Application of Obesity in Saudi Arabia

Sakr,

Mohamed

2024

Mathematics

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This paper presents several nonparametric estimators for the Sharma–Taneja–Mittal entropy measure of a continuous random variable with known support, utilizing spacing, a local linear model, and a kernel function. The properties of these estimators are discussed. Their performance was also examined through real data analysis and Monte Carlo simulations. In the Monte Carlo experiments, the proposed Sharma–Taneja–Mittal entropy estimators were employed to create a test of goodness-of-fit under the standard uniform distribution. The suggested test statistics demonstrate strong performance, as evidenced by a comparison of their power with that of other tests for uniformity. Finally, we examine a classification issue in the recognition of patterns to underscore the significance of these measures.

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A Generalized Measure of Cumulative Residual Entropy

Cited by 3 publications

References 31 publications

YOLO Recognition Method for Tea Shoots Based on Polariser Filtering and LFAnet

YOLO Recognition Method for Tea Shoots Based on Polariser Filtering and LFAnet

Weighted Mean Inactivity Time Function with Applications

Sharma–Taneja–Mittal Entropy and Its Application of Obesity in Saudi Arabia

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