Hina Bhanbhro^1,*, Yew Kwang Hooi¹, Mohammad Nordin Bin Zakaria¹, Worapan Kusakunniran², Zaira Hassan Amur¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2243-2259, 2024, DOI:10.32604/cmc.2024.052138 - 18 November 2024

Abstract Object detection has made a significant leap forward in recent years. However, the detection of small objects continues to be a great difficulty for various reasons, such as they have a very small size and they are susceptible to missed detection due to background noise. Additionally, small object information is affected due to the downsampling operations. Deep learning-based detection methods have been utilized to address the challenge posed by small objects. In this work, we propose a novel method, the Multi-Convolutional Block Attention Network (MCBAN), to increase the detection accuracy of minute objects aiming to… More >

Waqas Haider Bangyal¹, Abdul Hameed¹, Jamil Ahmad², Kashif Nisar^3,*, Muhammad Reazul Haque⁴, Ag. Asri Ag. Ibrahim³, Joel J. P. C. Rodrigues^5,6, M. Adil Khan⁷, Danda B. Rawat⁸, Richard Etengu⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2241-2259, 2022, DOI:10.32604/cmc.2022.017789 - 27 September 2021

Abstract Bat algorithm (BA) is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems. BA leverages the echolocation feature of bats produced by imitating the bats’ searching behavior. BA faces premature convergence due to its local search capability. Instead of using the standard uniform walk, the Torus walk is viewed as a promising alternative to improve the local search capability. In this work, we proposed an improved variation of BA by applying torus walk to improve diversity and convergence. The proposed. Modern Computerized Bat Algorithm (MCBA) approach has been… More >