P. K. A. Chitra¹, S. Appavu alias Balamurugan², S. Geetha³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*, Keejun Han⁸

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1367-1385, 2024, DOI:10.32604/csse.2023.034373 - 13 September 2024

Abstract A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l₂₁ norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed… More >

ShiJie Luo¹, Feng Yang¹, Yingjun Wang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.09474

Abstract The isogeometric analysis Method (IGA) is an efficient and accurate engineering analysis method. However, in order to obtain accurate analysis results, the grid must be refined, and the increase of the number of refinements will lead to large-scale equations, which will increase the computational cost. Compared with the traditional equation solvers such as preconditioned conjugate gradient method (PCG), generalized minimal residual (GMRES), the advantage of multigrid method is that the convergence rate is independent of grid scale when solving large-scale equations. This paper presents an adaptive weighted Jacobi method to improve the convergence of geometric… More >

Zhonghao Qian¹, Hanyi Ma¹, Jun Rao², Jun Hu¹, Lichengzi Yu^2,*, Caoyi Feng¹, Yunxu Qiu¹, Kemo Ding¹

Energy Engineering, Vol.120, No.9, pp. 2013-2027, 2023, DOI:10.32604/ee.2023.028859 - 03 August 2023

Abstract The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms. To improve the voltage stability and reactive power economy of wind farms, the improved particle swarm optimization is used to optimize the reactive power planning in wind farms. First, the power flow of offshore wind farms is modeled, analyzed and calculated. To improve the global search ability and local optimization ability of particle swarm optimization, the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor. Taking the minimum active power… More >

G. Augusta Kani^*, P. Geetha

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1447-1469, 2023, DOI:10.32604/csse.2023.035969 - 09 February 2023

Abstract Human Activity Recognition (HAR) from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications, video-based behavior monitoring, video classification, and indexing, patient monitoring, robotics, and behavior analysis. Although many techniques are available for HAR in video analysis tasks, most of them are not focusing on behavioral analysis. Hence, a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work. The most essential aim of this work is to recognize the complex activities that are… More >

Wenchuan Wang^1,*, Weican Tian¹, Kwok-wing Chau², Yiming Xue¹, Lei Xu³, Hongfei Zang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1603-1642, 2023, DOI:10.32604/cmes.2023.026231 - 06 February 2023

Abstract The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic algorithm. The algorithm simulates the hunting behavior of eagles, and obtains an optimal solution through three stages, namely selection stage, search stage and swooping stage. However, BES tends to drop-in local optimization and the maximum value of search space needs to be improved. To fill this research gap, we propose an improved bald eagle algorithm (CABES) that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima. Firstly, CABES introduces the Cauchy mutation strategy to adjust the step size of… More >

Xiaorui Zhang^1,2,3,*, Rui Jiang¹, Wei Sun^3,4, Aiguo Song⁵, Xindong Wei⁶, Ruohan Meng⁷

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.034748 - 06 February 2023

Abstract Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction. Deep learning has extremely powerful in extracting features, and watermarking algorithms based on deep learning have attracted widespread attention. Most existing methods use small kernel convolution to extract image features and embed the watermarking. However, the effective perception fields for small kernel convolution are extremely confined, so the pixels that each watermarking can affect are restricted, thus limiting the performance of the watermarking. To address these problems, we propose a watermarking network based on large kernel convolution and adaptive weight assignment for… More >

Ying Chen^{1, 2}, Yibin Tang³, Lin Zhou¹, Yan Zhou^{3, 4}, Jinxiu Zhu³, Li Zhao^{1, *}

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1219-1232, 2020, DOI:10.32604/cmc.2020.010638 - 10 June 2020

Abstract Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of… More >

Donghui Li¹, Cong Shen^2,*, Xiaopeng Dai¹, Xinghui Zhu¹, Jian Luo¹, Xueting Li¹, Haiwen Chen³, Zhiyao Liang⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1217-1231, 2019, DOI:10.32604/cmc.2019.06354

Abstract Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor. The data of water quality in the environment comes from different sensors, thus the data must be fused. In our research, self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value, temperature, oxygen dissolved and NH3 concentration of water quality environment. Based on the fusion, the Grubbs method is used to detect the abnormal data so as to provide data support for estimation, prediction and early warning of the water quality. More >