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Your search keyword '"Zhao, Bingyan"' showing total 45 results
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45 results on '"Zhao, Bingyan"'

19. Novel Binary Encoding Differential Evolution Algorithm

Author

Deng, Changshou, Zhao, Bingyan, Yang, Yanling, Peng, Hu, Wei, Qiming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Tan, Ying, editor, Shi, Yuhui, editor, Chai, Yi, editor, and Wang, Guoyin, editor

Published
2011
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20. Hybrid Differential Evolution for Knapsack Problem

Author

Deng, Changshou, Zhao, Bingyan, Yang, Yanling, Deng, Anyuan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Tan, Ying, editor, Shi, Yuhui, editor, and Tan, Kay Chen, editor

Published
2010
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27. An algorithm for headlights region detection in nighttime vehicles

Author

Zhu Hong, Yu Shunyuan, Wang Dong, Zhao Bingyan, and Xue Shan

Subjects
business.industry, Computer science, Frame (networking), Region detection, Image segmentation, Facial recognition system, Key point, Computer vision, Segmentation, Artificial intelligence, Recall rate, Projection (set theory), business, Algorithm
Abstract

Headlights detection is the key point for the automatic recognition of the turned-on high beams in night-driving vehicles, however, it is uneasy to identify the quite similar vehicle light targets from the video frame images. To solve this problem, an accurate algorithm for headlights region segment is proposed in this paper, which is based on the confidence intervals estimation of the vehicle width projective transformation. Firstly, the vehicles light region is extracted crudely, and then the vehicle lights come from the same vehicle are matched according to the confidence intervals of the vehicle width, finally, the high and low light region can be segmented accurately through the local projection and the precise local threshold segmentation. The experiment results show that the proposed method achieves high accuracy rate and recall rate in the headlights tests of night-driving vehicles.

Published
2016
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45. Preoperative frailty in oesophageal cancer: postoperative outcomes and overall survival - meta-analysis and systematic review.

Author

Zhao B, Wu M, Bao L, Zhang SA, and Zhang C

Abstract

Purpose: To assess the prevalence of preoperative frailty in patients with oesophageal cancer and its impact on postoperative outcomes and overall survival., Methods: A comprehensive computer-based search of the CNKI, Wanfang, VIP, CBM, PubMed, Embase, Cochrane Library, Web of Science and CINAHL databases was conducted for articles related to preoperative frailty in patients with oesophageal cancer. The search was carried out from the time of the construction of the database to 20 April 2024. Data related to the prevalence of preoperative frailty in patients with oesophageal cancer and their postoperative outcomes and overall survival were extracted., Results: A total of 13 studies were included, including 12 cohort studies and 1 cross-sectional study involving 53 485 patients. Meta-analysis showed that the prevalence of preoperative frailty in patients with oesophageal cancer was 29.6% (95% CI 24.5% to 34.8%). Preoperative frailty increased the risk of postoperative mortality (HR 1.80, 95% CI 1.51 to 2.14, p<0.001), complications (HR 1.32, 95% CI 1.16 to 1.49, p<0.001) and 30-day readmission (HR 1.24, 95% CI 1.18 to 1.31, p<0.001), in patients with oesophageal cancer, but had no significant effect on overall survival (HR 1.28, 95% CI 0.97 to 1.68, p=0.08)., Conclusions: The prevalence of preoperative frailty is high in patients with oesophageal cancer, and preoperative frailty is strongly associated with increased adverse outcomes after surgery. Healthcare providers should identify preoperative frailty in patients with oesophageal cancer at an early stage and develop targeted intervention strategies to reduce the incidence of postoperative adverse outcomes., Prospero Registration Number: CRD42024541051., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2025. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Published
2025
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