Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 371 - 382
Опубликована: Янв. 1, 2024
Язык: Английский
Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 371 - 382
Опубликована: Янв. 1, 2024
Язык: Английский
Biomedical Signal Processing and Control, Год журнала: 2024, Номер 96, С. 106492 - 106492
Опубликована: Июнь 7, 2024
Язык: Английский
Процитировано
6Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126746 - 126746
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Expert Systems, Год журнала: 2025, Номер 42(4)
Опубликована: Март 12, 2025
ABSTRACT Several decision‐making approaches involve ordinal labelling between feature values and decision outcomes. These issues refer to classification under monotonicity constraints. Recently, some machine learning have been designed deal with these kinds of problems. Indeed, numerous experiments shown that algorithms are widely used in real‐life applications because their flexibility efficiency terms interpretation predictions. In this paper, we introduce novel for measuring quality information quantity, called Rényi‐Tsallis Monotonic Tree (RTMT), which uses the advantages Rényi Tsallis entropies while incorporating constraints through an optimisation framework. Moreover, Mono‐CART, a variant CART approach adapted monotonic classification. New tree on basis aforementioned considering within system. The conducted using benchmark datasets demonstrate superiority proposed compared existing methods.
Язык: Английский
Процитировано
0PLoS ONE, Год журнала: 2024, Номер 19(3), С. e0297284 - e0297284
Опубликована: Март 21, 2024
Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes combination accelerating convergence diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination optimal thresholds for final segmentation. The efficacy DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature (FSIM), compared with six contemporary swarm intelligence algorithms. findings reveal that substantially surpasses its counterparts image processing speed. Further empirical analysis dataset comprising cases from level 1 to 5 underscores algorithm’s practical utility, achieving an impressive 80% accuracy identification underscoring potential recognition tasks.
Язык: Английский
Процитировано
1Опубликована: Авг. 7, 2024
Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The parameter q contained this plays an important role various adaptive algorithms and successfully applied bi-level thresholding. In paper, the relationships between pixels’ long-range correlations have further studied within multi-threshold segmentation. It is found that are remarkable stable for images generated by a known physical principle, such as infrared medical CT images. And corresponding can be evaluated using self-adaptive algorithm. results algorithm compared with those Shannon original algorithm, terms quantitative quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) SSIM (Structural Similarity). Furthermore, we observed series same background, values determined consistently kept narrow range. Therefore, similar or identical scenes during imaging would produce strength correlations, which provides potential applications unsupervised processing.
Язык: Английский
Процитировано
1Entropy, Год журнала: 2024, Номер 26(9), С. 777 - 777
Опубликована: Сен. 10, 2024
Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The parameter
Язык: Английский
Процитировано
1Multimedia Tools and Applications, Год журнала: 2024, Номер unknown
Опубликована: Май 11, 2024
Язык: Английский
Процитировано
0Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 371 - 382
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0