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- 저자 : Singh Simrandeep (검색결과 2 건)
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Ruptured Splenic Abscess with Pneumoperitoneum: A Rare Presentation
Shivani Goyal,Gurleen Kaur,Tanya Singh,Robin Kaushik,Simrandeep Singh 대한외상중환자외과학회 2023 Journal of Acute Care Surgery Vol.13 No.3
Splenic abscess is a rare entity encountered during clinical practice, with a high mortality rate. Formation of gas in splenic abscess is usually localized to the left upper quadrant of the abdomen. Here we report a case where the splenic abscess ruptured and presented with generalized peritonitis. The erect chest radiograph showed free air under the right dome of the diaphragm, thus masquerading a hollow viscera perforation (most common cause of pneumoperitoneum).
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An enhanced dynamic differential annealed algorithm for global optimization and feature selection
Hussien Abdelazim G,Kumar Sumit,Singh Simrandeep,Pan Jeng-Shyang,Hashim Fatma A 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.1
Dynamic differential annealed optimization (DDAO) is a recently developed physics-based metaheuristic technique that mimics the classical simulated annealing mechanism. However, DDAO has limited search abilities, especially when solving complicated and complex problems. A unique variation of DDAO, dubbed as mDDAO, is developed in this study, in which opposition-based learning technique and a novel updating equation are combined with DDAO. mDDAO is tested on 10 different functions from CEC2020 and compared with the original DDAO and nine other algorithms. The proposed mDDAO algorithm performance is evaluated using 10 numerical constrained functions from the recently released CEC 2020 benchmark suite, which includes a variety of dimensionally challenging optimisation tasks. Furthermore, to measure its viability, mDDAO is employed to solve feature selection problems using fourteen UCI datasets and a real-life Lymphoma diagnosis problem. Results prove that mDDAO has a superior performance and consistently outperforms counterparts across benchmarks, achieving fitness improvements ranging from 1% to 99.99%. In feature selection, mDDAO excels by reducing feature count by 23% to 79% compared to other methods, enhancing computational efficiency and maintaining classification accuracy. Moreover, in lymphoma diagnosis, mDDAO demonstrates up to 54% higher average fitness, 18% accuracy improvement, and 86% faster computation times.
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